diff --git a/.travis.yml b/.travis.yml
index 7ca7e6a..712fc84 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -11,4 +11,4 @@ install:
 
 script:
     - flake8 . --show-source --statistics
-    - docker run baselines-test pytest -v --forked .
+    - docker run baselines-test pytest -v .
diff --git a/baselines/bench/benchmarks.py b/baselines/bench/benchmarks.py
index 0d63e7a..c381935 100644
--- a/baselines/bench/benchmarks.py
+++ b/baselines/bench/benchmarks.py
@@ -20,7 +20,7 @@ def register_benchmark(benchmark):
     if 'tasks' in benchmark:
         for t in benchmark['tasks']:
             if 'desc' not in t:
-                t['desc'] = remove_version_re.sub('', t['env_id'])
+                t['desc'] = remove_version_re.sub('', t.get('env_id', t.get('id')))
     _BENCHMARKS.append(benchmark)
 
 
diff --git a/baselines/bench/monitor.py b/baselines/bench/monitor.py
index 0db473a..1281f9a 100644
--- a/baselines/bench/monitor.py
+++ b/baselines/bench/monitor.py
@@ -16,11 +16,13 @@ class Monitor(Wrapper):
     def __init__(self, env, filename, allow_early_resets=False, reset_keywords=(), info_keywords=()):
         Wrapper.__init__(self, env=env)
         self.tstart = time.time()
-        self.results_writer = ResultsWriter(
-            filename,
-            header={"t_start": time.time(), 'env_id' : env.spec and env.spec.id},
-            extra_keys=reset_keywords + info_keywords
-        )
+        if filename:
+            self.results_writer = ResultsWriter(filename,
+                header={"t_start": time.time(), 'env_id' : env.spec and env.spec.id},
+                extra_keys=reset_keywords + info_keywords
+            )
+        else:
+            self.results_writer = None
         self.reset_keywords = reset_keywords
         self.info_keywords = info_keywords
         self.allow_early_resets = allow_early_resets
@@ -68,8 +70,9 @@ class Monitor(Wrapper):
             self.episode_lengths.append(eplen)
             self.episode_times.append(time.time() - self.tstart)
             epinfo.update(self.current_reset_info)
-            self.results_writer.write_row(epinfo)
-
+            if self.results_writer:
+                self.results_writer.write_row(epinfo)
+            assert isinstance(info, dict)
             if isinstance(info, dict):
                 info['episode'] = epinfo
 
@@ -96,24 +99,21 @@ class LoadMonitorResultsError(Exception):
 
 
 class ResultsWriter(object):
-    def __init__(self, filename=None, header='', extra_keys=()):
+    def __init__(self, filename, header='', extra_keys=()):
         self.extra_keys = extra_keys
-        if filename is None:
-            self.f = None
-            self.logger = None
-        else:
-            if not filename.endswith(Monitor.EXT):
-                if osp.isdir(filename):
-                    filename = osp.join(filename, Monitor.EXT)
-                else:
-                    filename = filename + "." + Monitor.EXT
-            self.f = open(filename, "wt")
-            if isinstance(header, dict):
-                header = '# {} \n'.format(json.dumps(header))
-            self.f.write(header)
-            self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+tuple(extra_keys))
-            self.logger.writeheader()
-            self.f.flush()
+        assert filename is not None
+        if not filename.endswith(Monitor.EXT):
+            if osp.isdir(filename):
+                filename = osp.join(filename, Monitor.EXT)
+            else:
+                filename = filename + "." + Monitor.EXT
+        self.f = open(filename, "wt")
+        if isinstance(header, dict):
+            header = '# {} \n'.format(json.dumps(header))
+        self.f.write(header)
+        self.logger = csv.DictWriter(self.f, fieldnames=('r', 'l', 't')+tuple(extra_keys))
+        self.logger.writeheader()
+        self.f.flush()
 
     def write_row(self, epinfo):
         if self.logger:
@@ -121,7 +121,6 @@ class ResultsWriter(object):
             self.f.flush()
 
 
-
 def get_monitor_files(dir):
     return glob(osp.join(dir, "*" + Monitor.EXT))
 
diff --git a/baselines/common/atari_wrappers.py b/baselines/common/atari_wrappers.py
index a08ab8f..2c9b8c6 100644
--- a/baselines/common/atari_wrappers.py
+++ b/baselines/common/atari_wrappers.py
@@ -6,6 +6,8 @@ import gym
 from gym import spaces
 import cv2
 cv2.ocl.setUseOpenCL(False)
+from .wrappers import TimeLimit
+
 
 class NoopResetEnv(gym.Wrapper):
     def __init__(self, env, noop_max=30):
@@ -221,14 +223,13 @@ class LazyFrames(object):
     def __getitem__(self, i):
         return self._force()[i]
 
-def make_atari(env_id, timelimit=True):
-    # XXX(john): remove timelimit argument after gym is upgraded to allow double wrapping
+def make_atari(env_id, max_episode_steps=None):
     env = gym.make(env_id)
-    if not timelimit:
-        env = env.env
     assert 'NoFrameskip' in env.spec.id
     env = NoopResetEnv(env, noop_max=30)
     env = MaxAndSkipEnv(env, skip=4)
+    if max_episode_steps is not None:
+        env = TimeLimit(env, max_episode_steps=max_episode_steps)
     return env
 
 def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):
diff --git a/baselines/common/cmd_util.py b/baselines/common/cmd_util.py
index 24b5b90..419b227 100644
--- a/baselines/common/cmd_util.py
+++ b/baselines/common/cmd_util.py
@@ -30,16 +30,19 @@ def make_vec_env(env_id, env_type, num_env, seed,
     wrapper_kwargs = wrapper_kwargs or {}
     mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
     seed = seed + 10000 * mpi_rank if seed is not None else None
+    logger_dir = logger.get_dir()
     def make_thunk(rank):
         return lambda: make_env(
             env_id=env_id,
             env_type=env_type,
-            subrank = rank,
+            mpi_rank=mpi_rank,
+            subrank=rank,
             seed=seed,
             reward_scale=reward_scale,
             gamestate=gamestate,
             flatten_dict_observations=flatten_dict_observations,
-            wrapper_kwargs=wrapper_kwargs
+            wrapper_kwargs=wrapper_kwargs,
+            logger_dir=logger_dir
         )
 
     set_global_seeds(seed)
@@ -49,8 +52,7 @@ def make_vec_env(env_id, env_type, num_env, seed,
         return DummyVecEnv([make_thunk(start_index)])
 
 
-def make_env(env_id, env_type, subrank=0, seed=None, reward_scale=1.0, gamestate=None, flatten_dict_observations=True, wrapper_kwargs=None):
-    mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
+def make_env(env_id, env_type, mpi_rank=0, subrank=0, seed=None, reward_scale=1.0, gamestate=None, flatten_dict_observations=True, wrapper_kwargs=None, logger_dir=None):
     wrapper_kwargs = wrapper_kwargs or {}
     if env_type == 'atari':
         env = make_atari(env_id)
@@ -67,7 +69,7 @@ def make_env(env_id, env_type, subrank=0, seed=None, reward_scale=1.0, gamestate
 
     env.seed(seed + subrank if seed is not None else None)
     env = Monitor(env,
-                  logger.get_dir() and os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(subrank)),
+                  logger_dir and os.path.join(logger_dir, str(mpi_rank) + '.' + str(subrank)),
                   allow_early_resets=True)
 
     if env_type == 'atari':
@@ -134,6 +136,7 @@ def common_arg_parser():
     """
     parser = arg_parser()
     parser.add_argument('--env', help='environment ID', type=str, default='Reacher-v2')
+    parser.add_argument('--env_type', help='type of environment, used when the environment type cannot be automatically determined', type=str)
     parser.add_argument('--seed', help='RNG seed', type=int, default=None)
     parser.add_argument('--alg', help='Algorithm', type=str, default='ppo2')
     parser.add_argument('--num_timesteps', type=float, default=1e6),
diff --git a/baselines/common/distributions.py b/baselines/common/distributions.py
index 8966ee3..0b5fc76 100644
--- a/baselines/common/distributions.py
+++ b/baselines/common/distributions.py
@@ -206,7 +206,8 @@ class CategoricalPd(Pd):
 class MultiCategoricalPd(Pd):
     def __init__(self, nvec, flat):
         self.flat = flat
-        self.categoricals = list(map(CategoricalPd, tf.split(flat, nvec, axis=-1)))
+        self.categoricals = list(map(CategoricalPd,
+            tf.split(flat, np.array(nvec, dtype=np.int32), axis=-1)))
     def flatparam(self):
         return self.flat
     def mode(self):
diff --git a/baselines/common/mpi_adam_optimizer.py b/baselines/common/mpi_adam_optimizer.py
index 8cf09c4..af381e7 100644
--- a/baselines/common/mpi_adam_optimizer.py
+++ b/baselines/common/mpi_adam_optimizer.py
@@ -17,9 +17,16 @@ class MpiAdamOptimizer(tf.train.AdamOptimizer):
         num_tasks = self.comm.Get_size()
         buf = np.zeros(sum(sizes), np.float32)
 
+        sess = tf.get_default_session()
+        assert sess is not None
+        countholder = [0] # Counts how many times _collect_grads has been called
+        stat = tf.reduce_sum(grads_and_vars[0][1]) # sum of first variable
         def _collect_grads(flat_grad):
             self.comm.Allreduce(flat_grad, buf, op=MPI.SUM)
             np.divide(buf, float(num_tasks), out=buf)
+            if countholder[0] % 100 == 0:
+                check_synced(sess, self.comm, stat)
+            countholder[0] += 1
             return buf
 
         avg_flat_grad = tf.py_func(_collect_grads, [flat_grad], tf.float32)
@@ -27,5 +34,13 @@ class MpiAdamOptimizer(tf.train.AdamOptimizer):
         avg_grads = tf.split(avg_flat_grad, sizes, axis=0)
         avg_grads_and_vars = [(tf.reshape(g, v.shape), v)
                     for g, (_, v) in zip(avg_grads, grads_and_vars)]
-
         return avg_grads_and_vars
+
+def check_synced(sess, comm, tfstat):
+    """
+    Check that 'tfstat' evaluates to the same thing on every MPI worker
+    """
+    localval = sess.run(tfstat)
+    vals = comm.gather(localval)
+    if comm.rank == 0:
+        assert all(val==vals[0] for val in vals[1:])
diff --git a/baselines/common/mpi_util.py b/baselines/common/mpi_util.py
index f04187b..d05d9cf 100644
--- a/baselines/common/mpi_util.py
+++ b/baselines/common/mpi_util.py
@@ -1,9 +1,16 @@
 from collections import defaultdict
-from mpi4py import MPI
 import os, numpy as np
 import platform
 import shutil
 import subprocess
+import warnings
+import sys
+
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
+
 
 def sync_from_root(sess, variables, comm=None):
     """
@@ -13,15 +20,10 @@ def sync_from_root(sess, variables, comm=None):
       variables: all parameter variables including optimizer's
     """
     if comm is None: comm = MPI.COMM_WORLD
-    rank = comm.Get_rank()
-    for var in variables:
-        if rank == 0:
-            comm.Bcast(sess.run(var))
-        else:
-            import tensorflow as tf
-            returned_var = np.empty(var.shape, dtype='float32')
-            comm.Bcast(returned_var)
-            sess.run(tf.assign(var, returned_var))
+    import tensorflow as tf
+    values = comm.bcast(sess.run(variables))
+    sess.run([tf.assign(var, val)
+        for (var, val) in zip(variables, values)])
 
 def gpu_count():
     """
@@ -34,13 +36,15 @@ def gpu_count():
 
 def setup_mpi_gpus():
     """
-    Set CUDA_VISIBLE_DEVICES using MPI.
+    Set CUDA_VISIBLE_DEVICES to MPI rank if not already set
     """
-    num_gpus = gpu_count()
-    if num_gpus == 0:
-        return
-    local_rank, _ = get_local_rank_size(MPI.COMM_WORLD)
-    os.environ['CUDA_VISIBLE_DEVICES'] = str(local_rank % num_gpus)
+    if 'CUDA_VISIBLE_DEVICES' not in os.environ:
+        if sys.platform == 'darwin': # This Assumes if you're on OSX you're just
+            ids = []                 # doing a smoke test and don't want GPUs
+        else:
+            lrank, _lsize = get_local_rank_size(MPI.COMM_WORLD)
+            ids = [lrank]
+        os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, ids))
 
 def get_local_rank_size(comm):
     """
@@ -81,6 +85,9 @@ def share_file(comm, path):
     comm.Barrier()
 
 def dict_gather(comm, d, op='mean', assert_all_have_data=True):
+    """
+    Perform a reduction operation over dicts
+    """
     if comm is None: return d
     alldicts = comm.allgather(d)
     size = comm.size
@@ -99,3 +106,28 @@ def dict_gather(comm, d, op='mean', assert_all_have_data=True):
         else:
             assert 0, op
     return result
+
+def mpi_weighted_mean(comm, local_name2valcount):
+    """
+    Perform a weighted average over dicts that are each on a different node
+    Input: local_name2valcount: dict mapping key -> (value, count)
+    Returns: key -> mean
+    """
+    all_name2valcount = comm.gather(local_name2valcount)
+    if comm.rank == 0:
+        name2sum = defaultdict(float)
+        name2count = defaultdict(float)
+        for n2vc in all_name2valcount:
+            for (name, (val, count)) in n2vc.items():
+                try:
+                    val = float(val)
+                except ValueError:
+                    if comm.rank == 0:
+                        warnings.warn(f'WARNING: tried to compute mean on non-float {name}={val}')
+                else:
+                    name2sum[name] += val * count
+                    name2count[name] += count
+        return {name : name2sum[name] / name2count[name] for name in name2sum}
+    else:
+        return {}
+
diff --git a/baselines/common/retro_wrappers.py b/baselines/common/retro_wrappers.py
index 1e98044..2c42926 100644
--- a/baselines/common/retro_wrappers.py
+++ b/baselines/common/retro_wrappers.py
@@ -1,25 +1,11 @@
- # flake8: noqa F403, F405
-from .atari_wrappers import *
+from collections import deque
+import cv2
+cv2.ocl.setUseOpenCL(False)
+from .atari_wrappers import WarpFrame, ClipRewardEnv, FrameStack, ScaledFloatFrame
+from .wrappers import TimeLimit
 import numpy as np
 import gym
 
-class TimeLimit(gym.Wrapper):
-    def __init__(self, env, max_episode_steps=None):
-        super(TimeLimit, self).__init__(env)
-        self._max_episode_steps = max_episode_steps
-        self._elapsed_steps = 0
-
-    def step(self, ac):
-        observation, reward, done, info = self.env.step(ac)
-        self._elapsed_steps += 1
-        if self._elapsed_steps >= self._max_episode_steps:
-            done = True
-            info['TimeLimit.truncated'] = True
-        return observation, reward, done, info
-
-    def reset(self, **kwargs):
-        self._elapsed_steps = 0
-        return self.env.reset(**kwargs)
 
 class StochasticFrameSkip(gym.Wrapper):
     def __init__(self, env, n, stickprob):
@@ -99,7 +85,7 @@ class Downsample(gym.ObservationWrapper):
         gym.ObservationWrapper.__init__(self, env)
         (oldh, oldw, oldc) = env.observation_space.shape
         newshape = (oldh//ratio, oldw//ratio, oldc)
-        self.observation_space = spaces.Box(low=0, high=255,
+        self.observation_space = gym.spaces.Box(low=0, high=255,
             shape=newshape, dtype=np.uint8)
 
     def observation(self, frame):
@@ -116,7 +102,7 @@ class Rgb2gray(gym.ObservationWrapper):
         """
         gym.ObservationWrapper.__init__(self, env)
         (oldh, oldw, _oldc) = env.observation_space.shape
-        self.observation_space = spaces.Box(low=0, high=255,
+        self.observation_space = gym.spaces.Box(low=0, high=255,
             shape=(oldh, oldw, 1), dtype=np.uint8)
 
     def observation(self, frame):
@@ -213,8 +199,10 @@ class StartDoingRandomActionsWrapper(gym.Wrapper):
                 self.some_random_steps()
         return self.last_obs, rew, done, info
 
-def make_retro(*, game, state, max_episode_steps, **kwargs):
+def make_retro(*, game, state=None, max_episode_steps=4500, **kwargs):
     import retro
+    if state is None:
+        state = retro.State.DEFAULT
     env = retro.make(game, state, **kwargs)
     env = StochasticFrameSkip(env, n=4, stickprob=0.25)
     if max_episode_steps is not None:
diff --git a/baselines/common/test_mpi_util.py b/baselines/common/test_mpi_util.py
new file mode 100644
index 0000000..dcabf2e
--- /dev/null
+++ b/baselines/common/test_mpi_util.py
@@ -0,0 +1,27 @@
+from baselines import logger
+from baselines.common.tests.test_with_mpi import with_mpi
+from baselines.common import mpi_util
+
+@with_mpi()
+def test_mpi_weighted_mean():
+    from mpi4py import MPI
+    comm = MPI.COMM_WORLD
+    with logger.scoped_configure(comm=comm):
+        if comm.rank == 0:
+            name2valcount = {'a' : (10, 2), 'b' : (20,3)}
+        elif comm.rank == 1:
+            name2valcount = {'a' : (19, 1), 'c' : (42,3)}
+        else:
+            raise NotImplementedError
+
+        d = mpi_util.mpi_weighted_mean(comm, name2valcount)
+        correctval = {'a' : (10 * 2 + 19) / 3.0, 'b' : 20, 'c' : 42}
+        if comm.rank == 0:
+            assert d == correctval, f'{d} != {correctval}'
+
+        for name, (val, count) in name2valcount.items():
+            for _ in range(count):
+                logger.logkv_mean(name, val)
+        d2 = logger.dumpkvs()
+        if comm.rank == 0:
+            assert d2 == correctval
diff --git a/baselines/common/tests/envs/fixed_sequence_env.py b/baselines/common/tests/envs/fixed_sequence_env.py
index 9b538e7..f5460d5 100644
--- a/baselines/common/tests/envs/fixed_sequence_env.py
+++ b/baselines/common/tests/envs/fixed_sequence_env.py
@@ -7,21 +7,20 @@ class FixedSequenceEnv(Env):
     def __init__(
             self,
             n_actions=10,
-            seed=0,
             episode_len=100
     ):
         self.np_random = np.random.RandomState()
-        self.np_random.seed(seed)
-        self.sequence = [self.np_random.randint(0, n_actions-1) for _ in range(episode_len)]
+        self.sequence = None
 
         self.action_space = Discrete(n_actions)
         self.observation_space = Discrete(1)
 
         self.episode_len = episode_len
         self.time = 0
-        self.reset()
 
     def reset(self):
+        if self.sequence is None:
+            self.sequence = [self.np_random.randint(0, self.action_space.n-1) for _ in range(self.episode_len)]
         self.time = 0
         return 0
 
@@ -35,6 +34,9 @@ class FixedSequenceEnv(Env):
 
         return 0, rew, done, {}
 
+    def seed(self, seed=None):
+        self.np_random.seed(seed)
+
     def _choose_next_state(self):
         self.time += 1
 
diff --git a/baselines/common/tests/envs/identity_env.py b/baselines/common/tests/envs/identity_env.py
index 4429f04..79e6c48 100644
--- a/baselines/common/tests/envs/identity_env.py
+++ b/baselines/common/tests/envs/identity_env.py
@@ -10,6 +10,7 @@ class IdentityEnv(Env):
             episode_len=None
     ):
 
+        self.observation_space = self.action_space
         self.episode_len = episode_len
         self.time = 0
         self.reset()
@@ -17,7 +18,6 @@ class IdentityEnv(Env):
     def reset(self):
         self._choose_next_state()
         self.time = 0
-        self.observation_space = self.action_space
 
         return self.state
 
@@ -26,11 +26,13 @@ class IdentityEnv(Env):
         self._choose_next_state()
         done = False
         if self.episode_len and self.time >= self.episode_len:
-            rew = 0
             done = True
 
         return self.state, rew, done, {}
 
+    def seed(self, seed=None):
+        self.action_space.seed(seed)
+
     def _choose_next_state(self):
         self.state = self.action_space.sample()
         self.time += 1
@@ -74,7 +76,7 @@ class BoxIdentityEnv(IdentityEnv):
             episode_len=None,
     ):
 
-        self.action_space = Box(low=-1.0, high=1.0, shape=shape)
+        self.action_space = Box(low=-1.0, high=1.0, shape=shape, dtype=np.float32)
         super().__init__(episode_len=episode_len)
 
     def _get_reward(self, actions):
diff --git a/baselines/common/tests/envs/mnist_env.py b/baselines/common/tests/envs/mnist_env.py
index 473008d..cc0bde0 100644
--- a/baselines/common/tests/envs/mnist_env.py
+++ b/baselines/common/tests/envs/mnist_env.py
@@ -9,7 +9,6 @@ from gym.spaces import Discrete, Box
 class MnistEnv(Env):
     def __init__(
             self,
-            seed=0,
             episode_len=None,
             no_images=None
     ):
@@ -23,7 +22,6 @@ class MnistEnv(Env):
            self.mnist = input_data.read_data_sets(mnist_path)
 
         self.np_random = np.random.RandomState()
-        self.np_random.seed(seed)
 
         self.observation_space = Box(low=0.0, high=1.0, shape=(28,28,1))
         self.action_space = Discrete(10)
@@ -50,6 +48,9 @@ class MnistEnv(Env):
 
         return self.state[0], rew, done, {}
 
+    def seed(self, seed=None):
+        self.np_random.seed(seed)
+
     def train_mode(self):
         self.dataset = self.mnist.train
 
diff --git a/baselines/common/tests/test_fixed_sequence.py b/baselines/common/tests/test_fixed_sequence.py
index 4131a9d..061c375 100644
--- a/baselines/common/tests/test_fixed_sequence.py
+++ b/baselines/common/tests/test_fixed_sequence.py
@@ -33,8 +33,7 @@ def test_fixed_sequence(alg, rnn):
     kwargs = learn_kwargs[alg]
     kwargs.update(common_kwargs)
 
-    episode_len = 5
-    env_fn = lambda: FixedSequenceEnv(10, episode_len=episode_len)
+    env_fn = lambda: FixedSequenceEnv(n_actions=10, episode_len=5)
     learn = lambda e: get_learn_function(alg)(
         env=e,
         network=rnn,
diff --git a/baselines/common/tests/test_mnist.py b/baselines/common/tests/test_mnist.py
index eea094d..bacc914 100644
--- a/baselines/common/tests/test_mnist.py
+++ b/baselines/common/tests/test_mnist.py
@@ -41,7 +41,7 @@ def test_mnist(alg):
 
     learn = get_learn_function(alg)
     learn_fn = lambda e: learn(env=e, **learn_kwargs)
-    env_fn = lambda: MnistEnv(seed=0, episode_len=100)
+    env_fn = lambda: MnistEnv(episode_len=100)
 
     simple_test(env_fn, learn_fn, 0.6)
 
diff --git a/baselines/common/tests/test_serialization.py b/baselines/common/tests/test_serialization.py
index 73d29e9..25cec88 100644
--- a/baselines/common/tests/test_serialization.py
+++ b/baselines/common/tests/test_serialization.py
@@ -44,7 +44,12 @@ def test_serialization(learn_fn, network_fn):
             # github issue: https://github.com/openai/baselines/issues/660
             return
 
-    env = DummyVecEnv([lambda: MnistEnv(10, episode_len=100)])
+    def make_env():
+        env = MnistEnv(episode_len=100)
+        env.seed(10)
+        return env
+
+    env = DummyVecEnv([make_env])
     ob = env.reset().copy()
     learn = get_learn_function(learn_fn)
 
diff --git a/baselines/common/tests/test_with_mpi.py b/baselines/common/tests/test_with_mpi.py
new file mode 100644
index 0000000..86be475
--- /dev/null
+++ b/baselines/common/tests/test_with_mpi.py
@@ -0,0 +1,36 @@
+import os
+import sys
+import subprocess
+import cloudpickle
+import base64
+import pytest
+
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
+
+def with_mpi(nproc=2, timeout=30, skip_if_no_mpi=True):
+    def outer_thunk(fn):
+        def thunk(*args, **kwargs):
+            serialized_fn = base64.b64encode(cloudpickle.dumps(lambda: fn(*args, **kwargs)))
+            subprocess.check_call([
+                'mpiexec','-n', str(nproc),
+                sys.executable,
+                '-m', 'baselines.common.tests.test_with_mpi',
+                serialized_fn
+            ], env=os.environ, timeout=timeout)
+
+        if skip_if_no_mpi:
+            return pytest.mark.skipif(MPI is None, reason="MPI not present")(thunk)
+        else:
+            return thunk
+
+    return outer_thunk
+
+
+if __name__ == '__main__':
+    if len(sys.argv) > 1:
+        fn = cloudpickle.loads(base64.b64decode(sys.argv[1]))
+        assert callable(fn)
+        fn()
diff --git a/baselines/common/tests/util.py b/baselines/common/tests/util.py
index ce44d50..38ea4dc 100644
--- a/baselines/common/tests/util.py
+++ b/baselines/common/tests/util.py
@@ -6,48 +6,39 @@ N_TRIALS = 10000
 N_EPISODES = 100
 
 def simple_test(env_fn, learn_fn, min_reward_fraction, n_trials=N_TRIALS):
+    def seeded_env_fn():
+        env = env_fn()
+        env.seed(0)
+        return env
+
     np.random.seed(0)
-    env = DummyVecEnv([env_fn])
-
-
+    env = DummyVecEnv([seeded_env_fn])
     with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default():
         tf.set_random_seed(0)
-
         model = learn_fn(env)
-
         sum_rew = 0
         done = True
-
         for i in range(n_trials):
             if done:
                 obs = env.reset()
                 state = model.initial_state
-
             if state is not None:
                 a, v, state, _ = model.step(obs, S=state, M=[False])
             else:
                 a, v, _, _ = model.step(obs)
-
             obs, rew, done, _ = env.step(a)
             sum_rew += float(rew)
-
         print("Reward in {} trials is {}".format(n_trials, sum_rew))
         assert sum_rew > min_reward_fraction * n_trials, \
             'sum of rewards {} is less than {} of the total number of trials {}'.format(sum_rew, min_reward_fraction, n_trials)
 
-
-
 def reward_per_episode_test(env_fn, learn_fn, min_avg_reward, n_trials=N_EPISODES):
     env = DummyVecEnv([env_fn])
-
     with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default():
         model = learn_fn(env)
-
         N_TRIALS = 100
-
         observations, actions, rewards = rollout(env, model, N_TRIALS)
         rewards = [sum(r) for r in rewards]
-
         avg_rew = sum(rewards) / N_TRIALS
         print("Average reward in {} episodes is {}".format(n_trials, avg_rew))
         assert avg_rew > min_avg_reward, \
@@ -57,14 +48,12 @@ def rollout(env, model, n_trials):
     rewards = []
     actions = []
     observations = []
-
     for i in range(n_trials):
         obs = env.reset()
         state = model.initial_state if hasattr(model, 'initial_state') else None
         episode_rew = []
         episode_actions = []
         episode_obs = []
-
         while True:
             if state is not None:
                 a, v, state, _ = model.step(obs, S=state, M=[False])
@@ -72,17 +61,13 @@ def rollout(env, model, n_trials):
                 a,v, _, _ = model.step(obs)
 
             obs, rew, done, _ = env.step(a)
-
             episode_rew.append(rew)
             episode_actions.append(a)
             episode_obs.append(obs)
-
             if done:
                 break
-
         rewards.append(episode_rew)
         actions.append(episode_actions)
         observations.append(episode_obs)
-
     return observations, actions, rewards
 
diff --git a/baselines/common/tf_util.py b/baselines/common/tf_util.py
index a2d5df4..cabbe0c 100644
--- a/baselines/common/tf_util.py
+++ b/baselines/common/tf_util.py
@@ -1,4 +1,3 @@
-import joblib
 import numpy as np
 import tensorflow as tf  # pylint: ignore-module
 import copy
@@ -339,6 +338,7 @@ def save_state(fname, sess=None):
 # TODO: ensure there is no subtle differences and remove one
 
 def save_variables(save_path, variables=None, sess=None):
+    import joblib
     sess = sess or get_session()
     variables = variables or tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
 
@@ -350,6 +350,7 @@ def save_variables(save_path, variables=None, sess=None):
     joblib.dump(save_dict, save_path)
 
 def load_variables(load_path, variables=None, sess=None):
+    import joblib
     sess = sess or get_session()
     variables = variables or tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
 
diff --git a/baselines/common/vec_env/__init__.py b/baselines/common/vec_env/__init__.py
index 075a139..1e4ef55 100644
--- a/baselines/common/vec_env/__init__.py
+++ b/baselines/common/vec_env/__init__.py
@@ -1,185 +1,10 @@
-from abc import ABC, abstractmethod
-from baselines.common.tile_images import tile_images
+from .vec_env import AlreadySteppingError, NotSteppingError, VecEnv, VecEnvWrapper, VecEnvObservationWrapper, CloudpickleWrapper
+from .dummy_vec_env import DummyVecEnv
+from .shmem_vec_env import ShmemVecEnv
+from .subproc_vec_env import SubprocVecEnv
+from .vec_frame_stack import VecFrameStack
+from .vec_monitor import VecMonitor
+from .vec_normalize import VecNormalize
+from .vec_remove_dict_obs import VecExtractDictObs
 
-class AlreadySteppingError(Exception):
-    """
-    Raised when an asynchronous step is running while
-    step_async() is called again.
-    """
-
-    def __init__(self):
-        msg = 'already running an async step'
-        Exception.__init__(self, msg)
-
-
-class NotSteppingError(Exception):
-    """
-    Raised when an asynchronous step is not running but
-    step_wait() is called.
-    """
-
-    def __init__(self):
-        msg = 'not running an async step'
-        Exception.__init__(self, msg)
-
-
-class VecEnv(ABC):
-    """
-    An abstract asynchronous, vectorized environment.
-    Used to batch data from multiple copies of an environment, so that
-    each observation becomes an batch of observations, and expected action is a batch of actions to
-    be applied per-environment.
-    """
-    closed = False
-    viewer = None
-
-    metadata = {
-        'render.modes': ['human', 'rgb_array']
-    }
-
-    def __init__(self, num_envs, observation_space, action_space):
-        self.num_envs = num_envs
-        self.observation_space = observation_space
-        self.action_space = action_space
-
-    @abstractmethod
-    def reset(self):
-        """
-        Reset all the environments and return an array of
-        observations, or a dict of observation arrays.
-
-        If step_async is still doing work, that work will
-        be cancelled and step_wait() should not be called
-        until step_async() is invoked again.
-        """
-        pass
-
-    @abstractmethod
-    def step_async(self, actions):
-        """
-        Tell all the environments to start taking a step
-        with the given actions.
-        Call step_wait() to get the results of the step.
-
-        You should not call this if a step_async run is
-        already pending.
-        """
-        pass
-
-    @abstractmethod
-    def step_wait(self):
-        """
-        Wait for the step taken with step_async().
-
-        Returns (obs, rews, dones, infos):
-         - obs: an array of observations, or a dict of
-                arrays of observations.
-         - rews: an array of rewards
-         - dones: an array of "episode done" booleans
-         - infos: a sequence of info objects
-        """
-        pass
-
-    def close_extras(self):
-        """
-        Clean up the  extra resources, beyond what's in this base class.
-        Only runs when not self.closed.
-        """
-        pass
-
-    def close(self):
-        if self.closed:
-            return
-        if self.viewer is not None:
-            self.viewer.close()
-        self.close_extras()
-        self.closed = True
-
-    def step(self, actions):
-        """
-        Step the environments synchronously.
-
-        This is available for backwards compatibility.
-        """
-        self.step_async(actions)
-        return self.step_wait()
-
-    def render(self, mode='human'):
-        imgs = self.get_images()
-        bigimg = tile_images(imgs)
-        if mode == 'human':
-            self.get_viewer().imshow(bigimg)
-            return self.get_viewer().isopen
-        elif mode == 'rgb_array':
-            return bigimg
-        else:
-            raise NotImplementedError
-
-    def get_images(self):
-        """
-        Return RGB images from each environment
-        """
-        raise NotImplementedError
-
-    @property
-    def unwrapped(self):
-        if isinstance(self, VecEnvWrapper):
-            return self.venv.unwrapped
-        else:
-            return self
-
-    def get_viewer(self):
-        if self.viewer is None:
-            from gym.envs.classic_control import rendering
-            self.viewer = rendering.SimpleImageViewer()
-        return self.viewer
-
-
-class VecEnvWrapper(VecEnv):
-    """
-    An environment wrapper that applies to an entire batch
-    of environments at once.
-    """
-
-    def __init__(self, venv, observation_space=None, action_space=None):
-        self.venv = venv
-        VecEnv.__init__(self,
-                        num_envs=venv.num_envs,
-                        observation_space=observation_space or venv.observation_space,
-                        action_space=action_space or venv.action_space)
-
-    def step_async(self, actions):
-        self.venv.step_async(actions)
-
-    @abstractmethod
-    def reset(self):
-        pass
-
-    @abstractmethod
-    def step_wait(self):
-        pass
-
-    def close(self):
-        return self.venv.close()
-
-    def render(self, mode='human'):
-        return self.venv.render(mode=mode)
-
-    def get_images(self):
-        return self.venv.get_images()
-
-class CloudpickleWrapper(object):
-    """
-    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
-    """
-
-    def __init__(self, x):
-        self.x = x
-
-    def __getstate__(self):
-        import cloudpickle
-        return cloudpickle.dumps(self.x)
-
-    def __setstate__(self, ob):
-        import pickle
-        self.x = pickle.loads(ob)
+__all__ = ['AlreadySteppingError', 'NotSteppingError', 'VecEnv', 'VecEnvWrapper', 'VecEnvObservationWrapper', 'CloudpickleWrapper', 'DummyVecEnv', 'ShmemVecEnv', 'SubprocVecEnv', 'VecFrameStack', 'VecMonitor', 'VecNormalize', 'VecExtractDictObs']
diff --git a/baselines/common/vec_env/dummy_vec_env.py b/baselines/common/vec_env/dummy_vec_env.py
index 339aa1e..adcbce8 100644
--- a/baselines/common/vec_env/dummy_vec_env.py
+++ b/baselines/common/vec_env/dummy_vec_env.py
@@ -1,6 +1,5 @@
 import numpy as np
-from gym import spaces
-from . import VecEnv
+from .vec_env import VecEnv
 from .util import copy_obs_dict, dict_to_obs, obs_space_info
 
 class DummyVecEnv(VecEnv):
@@ -27,7 +26,7 @@ class DummyVecEnv(VecEnv):
         self.buf_rews  = np.zeros((self.num_envs,), dtype=np.float32)
         self.buf_infos = [{} for _ in range(self.num_envs)]
         self.actions = None
-        self.specs = [e.spec for e in self.envs]
+        self.spec = self.envs[0].spec
 
     def step_async(self, actions):
         listify = True
@@ -46,8 +45,8 @@ class DummyVecEnv(VecEnv):
     def step_wait(self):
         for e in range(self.num_envs):
             action = self.actions[e]
-            if isinstance(self.envs[e].action_space, spaces.Discrete):
-                action = int(action)
+            # if isinstance(self.envs[e].action_space, spaces.Discrete):
+            #    action = int(action)
 
             obs, self.buf_rews[e], self.buf_dones[e], self.buf_infos[e] = self.envs[e].step(action)
             if self.buf_dones[e]:
diff --git a/baselines/common/vec_env/shmem_vec_env.py b/baselines/common/vec_env/shmem_vec_env.py
index 99cc586..343ef94 100644
--- a/baselines/common/vec_env/shmem_vec_env.py
+++ b/baselines/common/vec_env/shmem_vec_env.py
@@ -2,9 +2,9 @@
 An interface for asynchronous vectorized environments.
 """
 
-from multiprocessing import Pipe, Array, Process
+import multiprocessing as mp
 import numpy as np
-from . import VecEnv, CloudpickleWrapper
+from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
 import ctypes
 from baselines import logger
 
@@ -22,11 +22,12 @@ class ShmemVecEnv(VecEnv):
     Optimized version of SubprocVecEnv that uses shared variables to communicate observations.
     """
 
-    def __init__(self, env_fns, spaces=None):
+    def __init__(self, env_fns, spaces=None, context='spawn'):
         """
         If you don't specify observation_space, we'll have to create a dummy
         environment to get it.
         """
+        ctx = mp.get_context(context)
         if spaces:
             observation_space, action_space = spaces
         else:
@@ -39,22 +40,22 @@ class ShmemVecEnv(VecEnv):
         VecEnv.__init__(self, len(env_fns), observation_space, action_space)
         self.obs_keys, self.obs_shapes, self.obs_dtypes = obs_space_info(observation_space)
         self.obs_bufs = [
-            {k: Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
+            {k: ctx.Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
             for _ in env_fns]
         self.parent_pipes = []
         self.procs = []
-        for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
-            wrapped_fn = CloudpickleWrapper(env_fn)
-            parent_pipe, child_pipe = Pipe()
-            proc = Process(target=_subproc_worker,
-                           args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
-            proc.daemon = True
-            self.procs.append(proc)
-            self.parent_pipes.append(parent_pipe)
-            proc.start()
-            child_pipe.close()
+        with clear_mpi_env_vars():
+            for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
+                wrapped_fn = CloudpickleWrapper(env_fn)
+                parent_pipe, child_pipe = ctx.Pipe()
+                proc = ctx.Process(target=_subproc_worker,
+                            args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
+                proc.daemon = True
+                self.procs.append(proc)
+                self.parent_pipes.append(parent_pipe)
+                proc.start()
+                child_pipe.close()
         self.waiting_step = False
-        self.specs = [f().spec for f in env_fns]
         self.viewer = None
 
     def reset(self):
diff --git a/baselines/common/vec_env/subproc_vec_env.py b/baselines/common/vec_env/subproc_vec_env.py
index b651e0d..a36a7af 100644
--- a/baselines/common/vec_env/subproc_vec_env.py
+++ b/baselines/common/vec_env/subproc_vec_env.py
@@ -1,6 +1,8 @@
+import multiprocessing as mp
+
 import numpy as np
-from multiprocessing import Process, Pipe
-from . import VecEnv, CloudpickleWrapper
+from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
+
 
 def worker(remote, parent_remote, env_fn_wrapper):
     parent_remote.close()
@@ -21,8 +23,8 @@ def worker(remote, parent_remote, env_fn_wrapper):
             elif cmd == 'close':
                 remote.close()
                 break
-            elif cmd == 'get_spaces':
-                remote.send((env.observation_space, env.action_space))
+            elif cmd == 'get_spaces_spec':
+                remote.send((env.observation_space, env.action_space, env.spec))
             else:
                 raise NotImplementedError
     except KeyboardInterrupt:
@@ -36,7 +38,7 @@ class SubprocVecEnv(VecEnv):
     VecEnv that runs multiple environments in parallel in subproceses and communicates with them via pipes.
     Recommended to use when num_envs > 1 and step() can be a bottleneck.
     """
-    def __init__(self, env_fns, spaces=None):
+    def __init__(self, env_fns, spaces=None, context='spawn'):
         """
         Arguments:
 
@@ -45,19 +47,20 @@ class SubprocVecEnv(VecEnv):
         self.waiting = False
         self.closed = False
         nenvs = len(env_fns)
-        self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
-        self.ps = [Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
+        ctx = mp.get_context(context)
+        self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(nenvs)])
+        self.ps = [ctx.Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
                    for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
         for p in self.ps:
             p.daemon = True  # if the main process crashes, we should not cause things to hang
-            p.start()
+            with clear_mpi_env_vars():
+                p.start()
         for remote in self.work_remotes:
             remote.close()
 
-        self.remotes[0].send(('get_spaces', None))
-        observation_space, action_space = self.remotes[0].recv()
+        self.remotes[0].send(('get_spaces_spec', None))
+        observation_space, action_space, self.spec = self.remotes[0].recv()
         self.viewer = None
-        self.specs = [f().spec for f in env_fns]
         VecEnv.__init__(self, len(env_fns), observation_space, action_space)
 
     def step_async(self, actions):
@@ -99,16 +102,16 @@ class SubprocVecEnv(VecEnv):
     def _assert_not_closed(self):
         assert not self.closed, "Trying to operate on a SubprocVecEnv after calling close()"
 
+    def __del__(self):
+        if not self.closed:
+            self.close()
 
 def _flatten_obs(obs):
-    assert isinstance(obs, list) or isinstance(obs, tuple)
+    assert isinstance(obs, (list, tuple))
     assert len(obs) > 0
 
     if isinstance(obs[0], dict):
-        import collections
-        assert isinstance(obs, collections.OrderedDict)
         keys = obs[0].keys()
         return {k: np.stack([o[k] for o in obs]) for k in keys}
     else:
         return np.stack(obs)
-
diff --git a/baselines/common/vec_env/test_vec_env.py b/baselines/common/vec_env/test_vec_env.py
index da2f3fb..4feabb0 100644
--- a/baselines/common/vec_env/test_vec_env.py
+++ b/baselines/common/vec_env/test_vec_env.py
@@ -8,39 +8,40 @@ import pytest
 from .dummy_vec_env import DummyVecEnv
 from .shmem_vec_env import ShmemVecEnv
 from .subproc_vec_env import SubprocVecEnv
+from baselines.common.tests.test_with_mpi import with_mpi
 
 
-def assert_envs_equal(env1, env2, num_steps):
+def assert_venvs_equal(venv1, venv2, num_steps):
     """
     Compare two environments over num_steps steps and make sure
     that the observations produced by each are the same when given
     the same actions.
     """
-    assert env1.num_envs == env2.num_envs
-    assert env1.action_space.shape == env2.action_space.shape
-    assert env1.action_space.dtype == env2.action_space.dtype
-    joint_shape = (env1.num_envs,) + env1.action_space.shape
+    assert venv1.num_envs == venv2.num_envs
+    assert venv1.observation_space.shape == venv2.observation_space.shape
+    assert venv1.observation_space.dtype == venv2.observation_space.dtype
+    assert venv1.action_space.shape == venv2.action_space.shape
+    assert venv1.action_space.dtype == venv2.action_space.dtype
 
     try:
-        obs1, obs2 = env1.reset(), env2.reset()
+        obs1, obs2 = venv1.reset(), venv2.reset()
         assert np.array(obs1).shape == np.array(obs2).shape
-        assert np.array(obs1).shape == joint_shape
+        assert np.array(obs1).shape == (venv1.num_envs,) + venv1.observation_space.shape
         assert np.allclose(obs1, obs2)
-        np.random.seed(1337)
+        venv1.action_space.seed(1337)
         for _ in range(num_steps):
-            actions = np.array(np.random.randint(0, 0x100, size=joint_shape),
-                               dtype=env1.action_space.dtype)
-            for env in [env1, env2]:
-                env.step_async(actions)
-            outs1 = env1.step_wait()
-            outs2 = env2.step_wait()
+            actions = np.array([venv1.action_space.sample() for _ in range(venv1.num_envs)])
+            for venv in [venv1, venv2]:
+                venv.step_async(actions)
+            outs1 = venv1.step_wait()
+            outs2 = venv2.step_wait()
             for out1, out2 in zip(outs1[:3], outs2[:3]):
                 assert np.array(out1).shape == np.array(out2).shape
                 assert np.allclose(out1, out2)
             assert list(outs1[3]) == list(outs2[3])
     finally:
-        env1.close()
-        env2.close()
+        venv1.close()
+        venv2.close()
 
 
 @pytest.mark.parametrize('klass', (ShmemVecEnv, SubprocVecEnv))
@@ -63,7 +64,7 @@ def test_vec_env(klass, dtype):  # pylint: disable=R0914
     fns = [make_fn(i) for i in range(num_envs)]
     env1 = DummyVecEnv(fns)
     env2 = klass(fns)
-    assert_envs_equal(env1, env2, num_steps=num_steps)
+    assert_venvs_equal(env1, env2, num_steps=num_steps)
 
 
 class SimpleEnv(gym.Env):
@@ -99,3 +100,15 @@ class SimpleEnv(gym.Env):
 
     def render(self, mode=None):
         raise NotImplementedError
+
+
+
+@with_mpi()
+def test_mpi_with_subprocvecenv():
+    shape = (2,3,4)
+    nenv = 1
+    venv = SubprocVecEnv([lambda: SimpleEnv(0, shape, 'float32')] * nenv)
+    ob = venv.reset()
+    venv.close()
+    assert ob.shape == (nenv,) + shape
+
diff --git a/baselines/common/vec_env/vec_env.py b/baselines/common/vec_env/vec_env.py
new file mode 100644
index 0000000..7aa7878
--- /dev/null
+++ b/baselines/common/vec_env/vec_env.py
@@ -0,0 +1,219 @@
+import contextlib
+import os
+from abc import ABC, abstractmethod
+
+from baselines.common.tile_images import tile_images
+
+class AlreadySteppingError(Exception):
+    """
+    Raised when an asynchronous step is running while
+    step_async() is called again.
+    """
+
+    def __init__(self):
+        msg = 'already running an async step'
+        Exception.__init__(self, msg)
+
+
+class NotSteppingError(Exception):
+    """
+    Raised when an asynchronous step is not running but
+    step_wait() is called.
+    """
+
+    def __init__(self):
+        msg = 'not running an async step'
+        Exception.__init__(self, msg)
+
+
+class VecEnv(ABC):
+    """
+    An abstract asynchronous, vectorized environment.
+    Used to batch data from multiple copies of an environment, so that
+    each observation becomes an batch of observations, and expected action is a batch of actions to
+    be applied per-environment.
+    """
+    closed = False
+    viewer = None
+
+    metadata = {
+        'render.modes': ['human', 'rgb_array']
+    }
+
+    def __init__(self, num_envs, observation_space, action_space):
+        self.num_envs = num_envs
+        self.observation_space = observation_space
+        self.action_space = action_space
+
+    @abstractmethod
+    def reset(self):
+        """
+        Reset all the environments and return an array of
+        observations, or a dict of observation arrays.
+
+        If step_async is still doing work, that work will
+        be cancelled and step_wait() should not be called
+        until step_async() is invoked again.
+        """
+        pass
+
+    @abstractmethod
+    def step_async(self, actions):
+        """
+        Tell all the environments to start taking a step
+        with the given actions.
+        Call step_wait() to get the results of the step.
+
+        You should not call this if a step_async run is
+        already pending.
+        """
+        pass
+
+    @abstractmethod
+    def step_wait(self):
+        """
+        Wait for the step taken with step_async().
+
+        Returns (obs, rews, dones, infos):
+         - obs: an array of observations, or a dict of
+                arrays of observations.
+         - rews: an array of rewards
+         - dones: an array of "episode done" booleans
+         - infos: a sequence of info objects
+        """
+        pass
+
+    def close_extras(self):
+        """
+        Clean up the  extra resources, beyond what's in this base class.
+        Only runs when not self.closed.
+        """
+        pass
+
+    def close(self):
+        if self.closed:
+            return
+        if self.viewer is not None:
+            self.viewer.close()
+        self.close_extras()
+        self.closed = True
+
+    def step(self, actions):
+        """
+        Step the environments synchronously.
+
+        This is available for backwards compatibility.
+        """
+        self.step_async(actions)
+        return self.step_wait()
+
+    def render(self, mode='human'):
+        imgs = self.get_images()
+        bigimg = tile_images(imgs)
+        if mode == 'human':
+            self.get_viewer().imshow(bigimg)
+            return self.get_viewer().isopen
+        elif mode == 'rgb_array':
+            return bigimg
+        else:
+            raise NotImplementedError
+
+    def get_images(self):
+        """
+        Return RGB images from each environment
+        """
+        raise NotImplementedError
+
+    @property
+    def unwrapped(self):
+        if isinstance(self, VecEnvWrapper):
+            return self.venv.unwrapped
+        else:
+            return self
+
+    def get_viewer(self):
+        if self.viewer is None:
+            from gym.envs.classic_control import rendering
+            self.viewer = rendering.SimpleImageViewer()
+        return self.viewer
+
+class VecEnvWrapper(VecEnv):
+    """
+    An environment wrapper that applies to an entire batch
+    of environments at once.
+    """
+
+    def __init__(self, venv, observation_space=None, action_space=None):
+        self.venv = venv
+        VecEnv.__init__(self,
+                        num_envs=venv.num_envs,
+                        observation_space=observation_space or venv.observation_space,
+                        action_space=action_space or venv.action_space)
+
+    def step_async(self, actions):
+        self.venv.step_async(actions)
+
+    @abstractmethod
+    def reset(self):
+        pass
+
+    @abstractmethod
+    def step_wait(self):
+        pass
+
+    def close(self):
+        return self.venv.close()
+
+    def render(self, mode='human'):
+        return self.venv.render(mode=mode)
+
+    def get_images(self):
+        return self.venv.get_images()
+
+class VecEnvObservationWrapper(VecEnvWrapper):
+    @abstractmethod
+    def process(self, obs):
+        pass
+
+    def reset(self):
+        obs = self.venv.reset()
+        return self.process(obs)
+
+    def step_wait(self):
+        obs, rews, dones, infos = self.venv.step_wait()
+        return self.process(obs), rews, dones, infos
+
+class CloudpickleWrapper(object):
+    """
+    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
+    """
+
+    def __init__(self, x):
+        self.x = x
+
+    def __getstate__(self):
+        import cloudpickle
+        return cloudpickle.dumps(self.x)
+
+    def __setstate__(self, ob):
+        import pickle
+        self.x = pickle.loads(ob)
+
+
+@contextlib.contextmanager
+def clear_mpi_env_vars():
+    """
+    from mpi4py import MPI will call MPI_Init by default.  If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
+    This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing
+    Processes.
+    """
+    removed_environment = {}
+    for k, v in list(os.environ.items()):
+        for prefix in ['OMPI_', 'PMI_']:
+            if k.startswith(prefix):
+                removed_environment[k] = v
+                del os.environ[k]
+    try:
+        yield
+    finally:
+        os.environ.update(removed_environment)
diff --git a/baselines/common/vec_env/vec_frame_stack.py b/baselines/common/vec_env/vec_frame_stack.py
index 1b7a695..0da1a0a 100644
--- a/baselines/common/vec_env/vec_frame_stack.py
+++ b/baselines/common/vec_env/vec_frame_stack.py
@@ -1,4 +1,4 @@
-from . import VecEnvWrapper
+from .vec_env import VecEnvWrapper
 import numpy as np
 from gym import spaces
 
diff --git a/baselines/common/vec_env/vec_monitor.py b/baselines/common/vec_env/vec_monitor.py
index 960f76c..6e67378 100644
--- a/baselines/common/vec_env/vec_monitor.py
+++ b/baselines/common/vec_env/vec_monitor.py
@@ -2,15 +2,23 @@ from . import VecEnvWrapper
 from baselines.bench.monitor import ResultsWriter
 import numpy as np
 import time
-
+from collections import deque
 
 class VecMonitor(VecEnvWrapper):
-    def __init__(self, venv, filename=None):
+    def __init__(self, venv, filename=None, keep_buf=0):
         VecEnvWrapper.__init__(self, venv)
         self.eprets = None
         self.eplens = None
+        self.epcount = 0
         self.tstart = time.time()
-        self.results_writer = ResultsWriter(filename, header={'t_start': self.tstart})
+        if filename:
+            self.results_writer = ResultsWriter(filename, header={'t_start': self.tstart})
+        else:
+            self.results_writer = None
+        self.keep_buf = keep_buf
+        if self.keep_buf:
+            self.epret_buf = deque([], maxlen=keep_buf)
+            self.eplen_buf = deque([], maxlen=keep_buf)
 
     def reset(self):
         obs = self.venv.reset()
@@ -28,10 +36,14 @@ class VecMonitor(VecEnvWrapper):
             if done:
                 epinfo = {'r': ret, 'l': eplen, 't': round(time.time() - self.tstart, 6)}
                 info['episode'] = epinfo
+                if self.keep_buf:
+                    self.epret_buf.append(ret)
+                    self.eplen_buf.append(eplen)
+                self.epcount += 1
                 self.eprets[i] = 0
                 self.eplens[i] = 0
-                self.results_writer.write_row(epinfo)
-
+                if self.results_writer:
+                    self.results_writer.write_row(epinfo)
             newinfos.append(info)
 
         return obs, rews, dones, newinfos
diff --git a/baselines/common/vec_env/vec_remove_dict_obs.py b/baselines/common/vec_env/vec_remove_dict_obs.py
new file mode 100644
index 0000000..602b949
--- /dev/null
+++ b/baselines/common/vec_env/vec_remove_dict_obs.py
@@ -0,0 +1,11 @@
+from .vec_env import VecEnvObservationWrapper
+
+
+class VecExtractDictObs(VecEnvObservationWrapper):
+    def __init__(self, venv, key):
+        self.key = key
+        super().__init__(venv=venv,
+            observation_space=venv.observation_space.spaces[self.key])
+
+    def process(self, obs):
+        return obs[self.key]
\ No newline at end of file
diff --git a/baselines/common/wrappers.py b/baselines/common/wrappers.py
new file mode 100644
index 0000000..7683d18
--- /dev/null
+++ b/baselines/common/wrappers.py
@@ -0,0 +1,19 @@
+import gym
+
+class TimeLimit(gym.Wrapper):
+    def __init__(self, env, max_episode_steps=None):
+        super(TimeLimit, self).__init__(env)
+        self._max_episode_steps = max_episode_steps
+        self._elapsed_steps = 0
+
+    def step(self, ac):
+        observation, reward, done, info = self.env.step(ac)
+        self._elapsed_steps += 1
+        if self._elapsed_steps >= self._max_episode_steps:
+            done = True
+            info['TimeLimit.truncated'] = True
+        return observation, reward, done, info
+
+    def reset(self, **kwargs):
+        self._elapsed_steps = 0
+        return self.env.reset(**kwargs)
\ No newline at end of file
diff --git a/baselines/ddpg/test_smoke.py b/baselines/ddpg/test_smoke.py
index b5bf866..a9fdc05 100644
--- a/baselines/ddpg/test_smoke.py
+++ b/baselines/ddpg/test_smoke.py
@@ -1,7 +1,10 @@
-from baselines.run import main as M
+from multiprocessing import Process
+import baselines.run
 
 def _run(argstr):
-    M(('--alg=ddpg --env=Pendulum-v0 --num_timesteps=0 ' + argstr).split(' '))
+    p = Process(target=baselines.run.main, args=('--alg=ddpg --env=Pendulum-v0 --num_timesteps=0 ' + argstr).split(' '))
+    p.start()
+    p.join()
 
 def test_popart():
     _run('--normalize_returns=True --popart=True')
diff --git a/baselines/her/her.py b/baselines/her/her.py
index ec96f9e..1821fc5 100644
--- a/baselines/her/her.py
+++ b/baselines/her/her.py
@@ -108,7 +108,7 @@ def learn(*, network, env, total_timesteps,
 
     # Prepare params.
     params = config.DEFAULT_PARAMS
-    env_name = env.specs[0].id
+    env_name = env.spec.id
     params['env_name'] = env_name
     params['replay_strategy'] = replay_strategy
     if env_name in config.DEFAULT_ENV_PARAMS:
diff --git a/baselines/logger.py b/baselines/logger.py
index a45fa8a..6c08ca0 100644
--- a/baselines/logger.py
+++ b/baselines/logger.py
@@ -7,6 +7,7 @@ import time
 import datetime
 import tempfile
 from collections import defaultdict
+from contextlib import contextmanager
 
 DEBUG = 10
 INFO = 20
@@ -68,7 +69,8 @@ class HumanOutputFormat(KVWriter, SeqWriter):
         self.file.flush()
 
     def _truncate(self, s):
-        return s[:20] + '...' if len(s) > 23 else s
+        maxlen = 30
+        return s[:maxlen-3] + '...' if len(s) > maxlen else s
 
     def writeseq(self, seq):
         seq = list(seq)
@@ -195,13 +197,13 @@ def logkv(key, val):
     Call this once for each diagnostic quantity, each iteration
     If called many times, last value will be used.
     """
-    Logger.CURRENT.logkv(key, val)
+    get_current().logkv(key, val)
 
 def logkv_mean(key, val):
     """
     The same as logkv(), but if called many times, values averaged.
     """
-    Logger.CURRENT.logkv_mean(key, val)
+    get_current().logkv_mean(key, val)
 
 def logkvs(d):
     """
@@ -213,21 +215,18 @@ def logkvs(d):
 def dumpkvs():
     """
     Write all of the diagnostics from the current iteration
-
-    level: int. (see logger.py docs) If the global logger level is higher than
-                the level argument here, don't print to stdout.
     """
-    Logger.CURRENT.dumpkvs()
+    return get_current().dumpkvs()
 
 def getkvs():
-    return Logger.CURRENT.name2val
+    return get_current().name2val
 
 
 def log(*args, level=INFO):
     """
     Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).
     """
-    Logger.CURRENT.log(*args, level=level)
+    get_current().log(*args, level=level)
 
 def debug(*args):
     log(*args, level=DEBUG)
@@ -246,30 +245,29 @@ def set_level(level):
     """
     Set logging threshold on current logger.
     """
-    Logger.CURRENT.set_level(level)
+    get_current().set_level(level)
+
+def set_comm(comm):
+    get_current().set_comm(comm)
 
 def get_dir():
     """
     Get directory that log files are being written to.
     will be None if there is no output directory (i.e., if you didn't call start)
     """
-    return Logger.CURRENT.get_dir()
+    return get_current().get_dir()
 
 record_tabular = logkv
 dump_tabular = dumpkvs
 
-class ProfileKV:
-    """
-    Usage:
-    with logger.ProfileKV("interesting_scope"):
-        code
-    """
-    def __init__(self, n):
-        self.n = "wait_" + n
-    def __enter__(self):
-        self.t1 = time.time()
-    def __exit__(self ,type, value, traceback):
-        Logger.CURRENT.name2val[self.n] += time.time() - self.t1
+@contextmanager
+def profile_kv(scopename):
+    logkey = 'wait_' + scopename
+    tstart = time.time()
+    try:
+        yield
+    finally:
+        get_current().name2val[logkey] += time.time() - tstart
 
 def profile(n):
     """
@@ -279,7 +277,7 @@ def profile(n):
     """
     def decorator_with_name(func):
         def func_wrapper(*args, **kwargs):
-            with ProfileKV(n):
+            with profile_kv(n):
                 return func(*args, **kwargs)
         return func_wrapper
     return decorator_with_name
@@ -289,17 +287,25 @@ def profile(n):
 # Backend
 # ================================================================
 
+def get_current():
+    if Logger.CURRENT is None:
+        _configure_default_logger()
+
+    return Logger.CURRENT
+
+
 class Logger(object):
     DEFAULT = None  # A logger with no output files. (See right below class definition)
                     # So that you can still log to the terminal without setting up any output files
     CURRENT = None  # Current logger being used by the free functions above
 
-    def __init__(self, dir, output_formats):
+    def __init__(self, dir, output_formats, comm=None):
         self.name2val = defaultdict(float)  # values this iteration
         self.name2cnt = defaultdict(int)
         self.level = INFO
         self.dir = dir
         self.output_formats = output_formats
+        self.comm = comm
 
     # Logging API, forwarded
     # ----------------------------------------
@@ -307,20 +313,27 @@ class Logger(object):
         self.name2val[key] = val
 
     def logkv_mean(self, key, val):
-        if val is None:
-            self.name2val[key] = None
-            return
         oldval, cnt = self.name2val[key], self.name2cnt[key]
         self.name2val[key] = oldval*cnt/(cnt+1) + val/(cnt+1)
         self.name2cnt[key] = cnt + 1
 
     def dumpkvs(self):
-        if self.level == DISABLED: return
+        if self.comm is None:
+            d = self.name2val
+        else:
+            from baselines.common import mpi_util
+            d = mpi_util.mpi_weighted_mean(self.comm,
+                {name : (val, self.name2cnt.get(name, 1))
+                    for (name, val) in self.name2val.items()})
+            if self.comm.rank != 0:
+                d['dummy'] = 1 # so we don't get a warning about empty dict
+        out = d.copy() # Return the dict for unit testing purposes
         for fmt in self.output_formats:
             if isinstance(fmt, KVWriter):
-                fmt.writekvs(self.name2val)
+                fmt.writekvs(d)
         self.name2val.clear()
         self.name2cnt.clear()
+        return out
 
     def log(self, *args, level=INFO):
         if self.level <= level:
@@ -331,6 +344,9 @@ class Logger(object):
     def set_level(self, level):
         self.level = level
 
+    def set_comm(self, comm):
+        self.comm = comm
+
     def get_dir(self):
         return self.dir
 
@@ -345,7 +361,10 @@ class Logger(object):
             if isinstance(fmt, SeqWriter):
                 fmt.writeseq(map(str, args))
 
-def configure(dir=None, format_strs=None):
+def configure(dir=None, format_strs=None, comm=None):
+    """
+    If comm is provided, average all numerical stats across that comm
+    """
     if dir is None:
         dir = os.getenv('OPENAI_LOGDIR')
     if dir is None:
@@ -372,15 +391,11 @@ def configure(dir=None, format_strs=None):
     format_strs = filter(None, format_strs)
     output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs]
 
-    Logger.CURRENT = Logger(dir=dir, output_formats=output_formats)
+    Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm)
     log('Logging to %s'%dir)
 
 def _configure_default_logger():
-    format_strs = None
-    # keep the old default of only writing to stdout
-    if 'OPENAI_LOG_FORMAT' not in os.environ:
-        format_strs = ['stdout']
-    configure(format_strs=format_strs)
+    configure()
     Logger.DEFAULT = Logger.CURRENT
 
 def reset():
@@ -389,17 +404,15 @@ def reset():
         Logger.CURRENT = Logger.DEFAULT
         log('Reset logger')
 
-class scoped_configure(object):
-    def __init__(self, dir=None, format_strs=None):
-        self.dir = dir
-        self.format_strs = format_strs
-        self.prevlogger = None
-    def __enter__(self):
-        self.prevlogger = Logger.CURRENT
-        configure(dir=self.dir, format_strs=self.format_strs)
-    def __exit__(self, *args):
+@contextmanager
+def scoped_configure(dir=None, format_strs=None, comm=None):
+    prevlogger = Logger.CURRENT
+    configure(dir=dir, format_strs=format_strs, comm=comm)
+    try:
+        yield
+    finally:
         Logger.CURRENT.close()
-        Logger.CURRENT = self.prevlogger
+        Logger.CURRENT = prevlogger
 
 # ================================================================
 
@@ -423,7 +436,7 @@ def _demo():
     logkv_mean("b", -44.4)
     logkv("a", 5.5)
     dumpkvs()
-    info("^^^ should see b = 33.3")
+    info("^^^ should see b = -33.3")
 
     logkv("b", -2.5)
     dumpkvs()
@@ -456,7 +469,6 @@ def read_tb(path):
     import pandas
     import numpy as np
     from glob import glob
-    from collections import defaultdict
     import tensorflow as tf
     if osp.isdir(path):
         fnames = glob(osp.join(path, "events.*"))
@@ -482,8 +494,5 @@ def read_tb(path):
             data[step-1, colidx] = value
     return pandas.DataFrame(data, columns=tags)
 
-# configure the default logger on import
-_configure_default_logger()
-
 if __name__ == "__main__":
     _demo()
diff --git a/baselines/ppo1/pposgd_simple.py b/baselines/ppo1/pposgd_simple.py
index 2ce734f..60d1d3a 100644
--- a/baselines/ppo1/pposgd_simple.py
+++ b/baselines/ppo1/pposgd_simple.py
@@ -97,7 +97,6 @@ def learn(env, policy_fn, *,
     ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
 
     lrmult = tf.placeholder(name='lrmult', dtype=tf.float32, shape=[]) # learning rate multiplier, updated with schedule
-    clip_param = clip_param * lrmult # Annealed clipping parameter epsilon
 
     ob = U.get_placeholder_cached(name="ob")
     ac = pi.pdtype.sample_placeholder([None])
diff --git a/baselines/ppo1/run_humanoid.py b/baselines/ppo1/run_humanoid.py
index 17b42b5..91a6db7 100644
--- a/baselines/ppo1/run_humanoid.py
+++ b/baselines/ppo1/run_humanoid.py
@@ -19,16 +19,17 @@ def train(num_timesteps, seed, model_path=None):
     # these are good enough to make humanoid walk, but whether those are
     # an absolute best or not is not certain
     env = RewScale(env, 0.1)
+    logger.log("NOTE: reward will be scaled by a factor of 10  in logged stats. Check the monitor for unscaled reward.")
     pi = pposgd_simple.learn(env, policy_fn,
             max_timesteps=num_timesteps,
             timesteps_per_actorbatch=2048,
-            clip_param=0.2, entcoeff=0.0,
+            clip_param=0.1, entcoeff=0.0,
             optim_epochs=10,
-            optim_stepsize=3e-4,
+            optim_stepsize=1e-4,
             optim_batchsize=64,
             gamma=0.99,
             lam=0.95,
-            schedule='linear',
+            schedule='constant',
         )
     env.close()
     if model_path:
@@ -47,7 +48,7 @@ def main():
     logger.configure()
     parser = mujoco_arg_parser()
     parser.add_argument('--model-path', default=os.path.join(logger.get_dir(), 'humanoid_policy'))
-    parser.set_defaults(num_timesteps=int(2e7))
+    parser.set_defaults(num_timesteps=int(5e7))
 
     args = parser.parse_args()
 
@@ -68,8 +69,5 @@ def main():
             if done:
                 ob = env.reset()
 
-
-
-
 if __name__ == '__main__':
     main()
diff --git a/baselines/ppo2/defaults.py b/baselines/ppo2/defaults.py
index 2bba5e6..9f3f96e 100644
--- a/baselines/ppo2/defaults.py
+++ b/baselines/ppo2/defaults.py
@@ -18,7 +18,7 @@ def atari():
         lam=0.95, gamma=0.99, noptepochs=4, log_interval=1,
         ent_coef=.01,
         lr=lambda f : f * 2.5e-4,
-        cliprange=lambda f : f * 0.1,
+        cliprange=0.1,
     )
 
 def retro():
diff --git a/baselines/run.py b/baselines/run.py
index cc63c11..d1c0ac7 100644
--- a/baselines/run.py
+++ b/baselines/run.py
@@ -6,15 +6,13 @@ from collections import defaultdict
 import tensorflow as tf
 import numpy as np
 
+from baselines.common.vec_env import VecFrameStack, VecNormalize
 from baselines.common.vec_env.vec_video_recorder import VecVideoRecorder
-from baselines.common.vec_env.vec_frame_stack import VecFrameStack
 from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_vec_env, make_env
 from baselines.common.tf_util import get_session
 from baselines import logger
 from importlib import import_module
 
-from baselines.common.vec_env.vec_normalize import VecNormalize
-
 try:
     from mpi4py import MPI
 except ImportError:
@@ -52,7 +50,7 @@ _game_envs['retro'] = {
 
 
 def train(args, extra_args):
-    env_type, env_id = get_env_type(args.env)
+    env_type, env_id = get_env_type(args)
     print('env_type: {}'.format(env_type))
 
     total_timesteps = int(args.num_timesteps)
@@ -64,7 +62,7 @@ def train(args, extra_args):
 
     env = build_env(args)
     if args.save_video_interval != 0:
-        env = VecVideoRecorder(env, osp.join(logger.Logger.CURRENT.dir, "videos"), record_video_trigger=lambda x: x % args.save_video_interval == 0, video_length=args.save_video_length)
+        env = VecVideoRecorder(env, osp.join(logger.get_dir(), "videos"), record_video_trigger=lambda x: x % args.save_video_interval == 0, video_length=args.save_video_length)
 
     if args.network:
         alg_kwargs['network'] = args.network
@@ -91,7 +89,7 @@ def build_env(args):
     alg = args.alg
     seed = args.seed
 
-    env_type, env_id = get_env_type(args.env)
+    env_type, env_id = get_env_type(args)
 
     if env_type in {'atari', 'retro'}:
         if alg == 'deepq':
@@ -104,22 +102,27 @@ def build_env(args):
             env = VecFrameStack(env, frame_stack_size)
 
     else:
-       config = tf.ConfigProto(allow_soft_placement=True,
+        config = tf.ConfigProto(allow_soft_placement=True,
                                intra_op_parallelism_threads=1,
                                inter_op_parallelism_threads=1)
-       config.gpu_options.allow_growth = True
-       get_session(config=config)
+        config.gpu_options.allow_growth = True
+        get_session(config=config)
 
-       flatten_dict_observations = alg not in {'her'}
-       env = make_vec_env(env_id, env_type, args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations)
+        flatten_dict_observations = alg not in {'her'}
+        env = make_vec_env(env_id, env_type, args.num_env or 1, seed, reward_scale=args.reward_scale, flatten_dict_observations=flatten_dict_observations)
 
-       if env_type == 'mujoco':
-           env = VecNormalize(env)
+        if env_type == 'mujoco':
+            env = VecNormalize(env)
 
     return env
 
 
-def get_env_type(env_id):
+def get_env_type(args):
+    env_id = args.env
+
+    if args.env_type is not None:
+        return args.env_type, env_id
+
     # Re-parse the gym registry, since we could have new envs since last time.
     for env in gym.envs.registry.all():
         env_type = env._entry_point.split(':')[0].split('.')[-1]
@@ -205,7 +208,6 @@ def main(args):
         rank = MPI.COMM_WORLD.Get_rank()
 
     model, env = train(args, extra_args)
-    env.close()
 
     if args.save_path is not None and rank == 0:
         save_path = osp.expanduser(args.save_path)
@@ -213,26 +215,28 @@ def main(args):
 
     if args.play:
         logger.log("Running trained model")
-        env = build_env(args)
         obs = env.reset()
 
         state = model.initial_state if hasattr(model, 'initial_state') else None
         dones = np.zeros((1,))
 
+        episode_rew = 0
         while True:
             if state is not None:
                 actions, _, state, _ = model.step(obs,S=state, M=dones)
             else:
                 actions, _, _, _ = model.step(obs)
 
-            obs, _, done, _ = env.step(actions)
+            obs, rew, done, _ = env.step(actions)
+            episode_rew += rew[0]
             env.render()
             done = done.any() if isinstance(done, np.ndarray) else done
-
             if done:
+                print(f'episode_rew={episode_rew}')
+                episode_rew = 0
                 obs = env.reset()
 
-        env.close()
+    env.close()
 
     return model