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deduplicate algorithms in rl-algs and baselines (#18)

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* move vec_env

* cleaning up rl_common

* tests are passing (but mosts tests are deleted as moved to baselines)

* add benchmark runner for smoke tests

* removed duplicated algos

* route references to rl_algs.a2c to baselines.a2c

* route references to rl_algs.a2c to baselines.a2c

* unify conftest.py

* removing references to duplicated algs from codegen

* removing references to duplicated algs from codegen

* alex's changes to dummy_vec_env

* fixed test_carpole[deepq] testcase by decreasing number of training steps... alex's changes seemed to have fixed the bug and make it train better, but at seed=0 there is a dip in the training curve at 30k steps that fails the test

* codegen tests with atol=1e-6 seem to be unstable

* rl_common.vec_env -> baselines.common.vec_env mass replace

* fixed reference in trpo_mpi

* a2c.util references

* restored rl_algs.bench in sonic_prob

* fix reference in ci/runtests.sh

* simplifed expression in baselines/common/cmd_util

* further increased rtol to 1e-3 in codegen tests

* switched vecenvs to use SimpleImageViewer from gym instead of cv2

* make run.py --play option work with num_envs > 1

* make rosenbrock test reproducible

* git subrepo pull (merge) baselines

subrepo:
  subdir:   "baselines"
  merged:   "e23524a5"
upstream:
  origin:   "git@github.com:openai/baselines.git"
  branch:   "master"
  commit:   "bcde04e7"
git-subrepo:
  version:  "0.4.0"
  origin:   "git@github.com:ingydotnet/git-subrepo.git"
  commit:   "74339e8"

* updated baselines README (num-timesteps --> num_timesteps)

* typo in deepq/README.md
This commit is contained in:
pzhokhov
2018-08-17 09:40:35 -07:00
committed by Peter Zhokhov
parent 64c0c0a043 5edcd6886e
commit 353bb15e90
26 changed files with 439 additions and 150 deletions
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4
README.md
View File

@@ -102,11 +102,11 @@ The algorithms serialization API is not properly unified yet; however, there is
`--save_path` and `--load_path` command-line option loads the tensorflow state from a given path before training, and saves it after the training, respectively.
Let's imagine you'd like to train ppo2 on Atari Pong, save the model and then later visualize what has it learnt.
```bash
python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num-timesteps=2e7 --save_path=~/models/pong_20M_ppo2
python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=2e7 --save_path=~/models/pong_20M_ppo2
```
This should get to the mean reward per episode about 5k. To load and visualize the model, we'll do the following - load the model, train it for 0 steps, and then visualize:
```bash
python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num-timesteps=0 --load_path=~/models/pong_20M_ppo2 --play
python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --load_path=~/models/pong_20M_ppo2 --play
```
*NOTE:* At the moment Mujoco training uses VecNormalize wrapper for the environment which is not being saved correctly; so loading the models trained on Mujoco will not work well if the environment is recreated. If necessary, you can work around that by replacing RunningMeanStd by TfRunningMeanStd in [baselines/common/vec_env/vec_normalize.py](baselines/common/vec_env/vec_normalize.py#L12). This way, mean and std of environment normalizing wrapper will be saved in tensorflow variables and included in the model file; however, training is slower that way - hence not including it by default

3
baselines/a2c/README.md
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@@ -2,4 +2,5 @@
- Original paper: https://arxiv.org/abs/1602.01783
- Baselines blog post: https://blog.openai.com/baselines-acktr-a2c/
- `python -m baselines.a2c.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.run --alg=a2c --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options
- also refer to the repo-wide [README.md](../../README.md#training-models)

4
baselines/acer/README.md
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@@ -1,4 +1,6 @@
# ACER
- Original paper: https://arxiv.org/abs/1611.01224
- `python -m baselines.acer.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.run --alg=acer --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
- also refer to the repo-wide [README.md](../../README.md#training-models)

5
baselines/acktr/README.md
View File

@@ -2,4 +2,7 @@
- Original paper: https://arxiv.org/abs/1708.05144
- Baselines blog post: https://blog.openai.com/baselines-acktr-a2c/
- `python -m baselines.acktr.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.run --alg=acktr --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
- also refer to the repo-wide [README.md](../../README.md#training-models)

23
baselines/acktr/run_atari.py
View File

@@ -1,23 +0,0 @@
#!/usr/bin/env python3
from functools import partial
from baselines import logger
from baselines.acktr.acktr_disc import learn
from baselines.common.cmd_util import make_atari_env, atari_arg_parser
from baselines.common.vec_env.vec_frame_stack import VecFrameStack
from baselines.common.policies import cnn
def train(env_id, num_timesteps, seed, num_cpu):
env = VecFrameStack(make_atari_env(env_id, num_cpu, seed), 4)
policy_fn = cnn(env=env, one_dim_bias=True)
learn(policy_fn, env, seed, total_timesteps=int(num_timesteps * 1.1), nprocs=num_cpu)
env.close()
def main():
args = atari_arg_parser().parse_args()
logger.configure()
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed, num_cpu=32)
if __name__ == '__main__':
main()

3
baselines/common/cmd_util.py
View File

@@ -40,7 +40,8 @@ def make_mujoco_env(env_id, seed, reward_scale=1.0):
myseed = seed + 1000 * rank if seed is not None else None
set_global_seeds(myseed)
env = gym.make(env_id)
env = Monitor(env, os.path.join(logger.get_dir(), str(rank)), allow_early_resets=True)
logger_path = None if logger.get_dir() is None else os.path.join(logger.get_dir(), str(rank))
env = Monitor(env, logger_path, allow_early_resets=True)
env.seed(seed)
if reward_scale != 1.0:

5
baselines/common/tests/test_cartpole.py
View File

@@ -14,7 +14,7 @@ common_kwargs = dict(
learn_kwargs = {
'a2c' : dict(nsteps=32, value_network='copy', lr=0.05),
'acktr': dict(nsteps=32, value_network='copy'),
'deepq': {},
'deepq': dict(total_timesteps=20000),
'ppo2': dict(value_network='copy'),
'trpo_mpi': {}
}
@@ -38,3 +38,6 @@ def test_cartpole(alg):
return env
reward_per_episode_test(env_fn, learn_fn, 100)
if __name__ == '__main__':
test_cartpole('deepq')

35
baselines/common/vec_env/__init__.py
View File

@@ -1,28 +1,34 @@
from abc import ABC, abstractmethod
from baselines import logger
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.
"""
def __init__(self, num_envs, observation_space, action_space):
self.num_envs = num_envs
self.observation_space = observation_space
@@ -32,7 +38,7 @@ class VecEnv(ABC):
def reset(self):
"""
Reset all the environments and return an array of
observations, or a tuple of observation arrays.
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
@@ -58,7 +64,7 @@ class VecEnv(ABC):
Wait for the step taken with step_async().
Returns (obs, rews, dones, infos):
- obs: an array of observations, or a tuple of
- obs: an array of observations, or a dict of
arrays of observations.
- rews: an array of rewards
- dones: an array of "episode done" booleans
@@ -74,11 +80,16 @@ class VecEnv(ABC):
pass
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'):
logger.warn('Render not defined for %s'%self)
logger.warn('Render not defined for %s' % self)
@property
def unwrapped(self):
@@ -87,13 +98,19 @@ class VecEnv(ABC):
else:
return self
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)
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)
@@ -112,15 +129,19 @@ class VecEnvWrapper(VecEnv):
def render(self):
self.venv.render()
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)

25
baselines/common/vec_env/dummy_vec_env.py
View File

@@ -2,27 +2,16 @@ import numpy as np
from gym import spaces
from collections import OrderedDict
from . import VecEnv
from .util import copy_obs_dict, dict_to_obs, obs_space_info
class DummyVecEnv(VecEnv):
def __init__(self, env_fns):
self.envs = [fn() for fn in env_fns]
env = self.envs[0]
VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
shapes, dtypes = {}, {}
self.keys = []
obs_space = env.observation_space
if isinstance(obs_space, spaces.Dict):
assert isinstance(obs_space.spaces, OrderedDict)
subspaces = obs_space.spaces
else:
subspaces = {None: obs_space}
for key, box in subspaces.items():
shapes[key] = box.shape
dtypes[key] = box.dtype
self.keys.append(key)
self.keys, shapes, dtypes = obs_space_info(obs_space)
self.buf_obs = { k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys }
self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool)
self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32)
@@ -53,7 +42,7 @@ class DummyVecEnv(VecEnv):
if self.buf_dones[e]:
obs = self.envs[e].reset()
self._save_obs(e, obs)
return (np.copy(self._obs_from_buf()), np.copy(self.buf_rews), np.copy(self.buf_dones),
return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones),
self.buf_infos.copy())
def reset(self):
@@ -76,7 +65,5 @@ class DummyVecEnv(VecEnv):
self.buf_obs[k][e] = obs[k]
def _obs_from_buf(self):
if self.keys==[None]:
return self.buf_obs[None]
else:
return self.buf_obs
return dict_to_obs(copy_obs_dict(self.buf_obs))

152
baselines/common/vec_env/shmem_vec_env.py Normal file
View File

@@ -0,0 +1,152 @@
"""
An interface for asynchronous vectorized environments.
"""
from multiprocessing import Pipe, Array, Process
import numpy as np
from . import VecEnv, CloudpickleWrapper
import ctypes
from gym.envs.classic_control import rendering
from baselines import logger
from baselines.common.tile_images import tile_images
from .util import dict_to_obs, obs_space_info, obs_to_dict
_NP_TO_CT = {np.float32: ctypes.c_float,
np.int32: ctypes.c_int32,
np.int8: ctypes.c_int8,
np.uint8: ctypes.c_char,
np.bool: ctypes.c_bool}
class ShmemVecEnv(VecEnv):
"""
An AsyncEnv that uses multiprocessing to run multiple
environments in parallel.
"""
def __init__(self, env_fns, spaces=None):
"""
If you don't specify observation_space, we'll have to create a dummy
environment to get it.
"""
if spaces:
observation_space, action_space = spaces
else:
logger.log('Creating dummy env object to get spaces')
with logger.scoped_configure(format_strs=[]):
dummy = env_fns[0]()
observation_space, action_space = dummy.observation_space, dummy.action_space
dummy.close()
del dummy
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}
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()
self.waiting_step = False
self.viewer = None
def reset(self):
if self.waiting_step:
logger.warn('Called reset() while waiting for the step to complete')
self.step_wait()
for pipe in self.parent_pipes:
pipe.send(('reset', None))
return self._decode_obses([pipe.recv() for pipe in self.parent_pipes])
def step_async(self, actions):
assert len(actions) == len(self.parent_pipes)
for pipe, act in zip(self.parent_pipes, actions):
pipe.send(('step', act))
def step_wait(self):
outs = [pipe.recv() for pipe in self.parent_pipes]
obs, rews, dones, infos = zip(*outs)
return self._decode_obses(obs), np.array(rews), np.array(dones), infos
def close(self):
if self.waiting_step:
self.step_wait()
for pipe in self.parent_pipes:
pipe.send(('close', None))
for pipe in self.parent_pipes:
pipe.recv()
pipe.close()
for proc in self.procs:
proc.join()
if self.viewer is not None:
self.viewer.close()
def render(self, mode='human'):
for pipe in self.parent_pipes:
pipe.send(('render', None))
imgs = [pipe.recv() for pipe in self.parent_pipes]
bigimg = tile_images(imgs)
if mode == 'human':
if self.viewer is None:
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(bigimg[:, :, ::-1])
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
def _decode_obses(self, obs):
result = {}
for k in self.obs_keys:
bufs = [b[k] for b in self.obs_bufs]
o = [np.frombuffer(b.get_obj(), dtype=self.obs_dtypes[k]).reshape(self.obs_shapes[k]) for b in bufs]
result[k] = np.array(o)
return dict_to_obs(result)
def _subproc_worker(pipe, parent_pipe, env_fn_wrapper, obs_bufs, obs_shapes, obs_dtypes, keys):
"""
Control a single environment instance using IPC and
shared memory.
"""
def _write_obs(maybe_dict_obs):
flatdict = obs_to_dict(maybe_dict_obs)
for k in keys:
dst = obs_bufs[k].get_obj()
dst_np = np.frombuffer(dst, dtype=obs_dtypes[k]).reshape(obs_shapes[k]) # pylint: disable=W0212
np.copyto(dst_np, flatdict[k])
env = env_fn_wrapper.x()
parent_pipe.close()
try:
while True:
cmd, data = pipe.recv()
if cmd == 'reset':
pipe.send(_write_obs(env.reset()))
elif cmd == 'step':
obs, reward, done, info = env.step(data)
if done:
obs = env.reset()
pipe.send((_write_obs(obs), reward, done, info))
elif cmd == 'render':
pipe.send(env.render(mode='rgb_array'))
elif cmd == 'close':
pipe.send(None)
break
else:
raise RuntimeError('Got unrecognized cmd %s' % cmd)
except KeyboardInterrupt:
print('ShmemVecEnv worker: got KeyboardInterrupt')
finally:
env.close()

23
baselines/common/vec_env/subproc_vec_env.py
View File

@@ -1,7 +1,8 @@
import numpy as np
from multiprocessing import Process, Pipe
from baselines.common.vec_env import VecEnv, CloudpickleWrapper
from . import VecEnv, CloudpickleWrapper
from baselines.common.tile_images import tile_images
from gym.envs.classic_control import rendering
def worker(remote, parent_remote, env_fn_wrapper):
@@ -32,6 +33,7 @@ def worker(remote, parent_remote, env_fn_wrapper):
finally:
env.close()
class SubprocVecEnv(VecEnv):
def __init__(self, env_fns, spaces=None):
"""
@@ -42,15 +44,16 @@ class SubprocVecEnv(VecEnv):
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)))
for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
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.daemon = True # if the main process crashes, we should not cause things to hang
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.viewer = None
VecEnv.__init__(self, len(env_fns), observation_space, action_space)
def step_async(self, actions):
@@ -78,12 +81,14 @@ class SubprocVecEnv(VecEnv):
if self.closed:
return
if self.waiting:
for remote in self.remotes:
for remote in self.remotes:
remote.recv()
for remote in self.remotes:
remote.send(('close', None))
for p in self.ps:
p.join()
if self.viewer is not None:
self.viewer.close()
self.closed = True
def render(self, mode='human'):
@@ -92,10 +97,12 @@ class SubprocVecEnv(VecEnv):
imgs = [pipe.recv() for pipe in self.remotes]
bigimg = tile_images(imgs)
if mode == 'human':
import cv2
cv2.imshow('vecenv', bigimg[:,:,::-1])
cv2.waitKey(1)
if self.viewer is None:
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(bigimg[:, :, ::-1])
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
raise NotImplementedError

85
baselines/common/vec_env/test_vec_env.py Normal file
View File

@@ -0,0 +1,85 @@
"""
Tests for asynchronous vectorized environments.
"""
import gym
import numpy as np
import pytest
from .dummy_vec_env import DummyVecEnv
from .shmem_vec_env import ShmemVecEnv
from .subproc_vec_env import SubprocVecEnv
@pytest.mark.parametrize('klass', (ShmemVecEnv, SubprocVecEnv))
@pytest.mark.parametrize('dtype', ('uint8', 'float32'))
def test_vec_env(klass, dtype): # pylint: disable=R0914
"""
Test that a vectorized environment is equivalent to
DummyVecEnv, since DummyVecEnv is less likely to be
error prone.
"""
num_envs = 3
num_steps = 100
shape = (3, 8)
def make_fn(seed):
"""
Get an environment constructor with a seed.
"""
return lambda: _SimpleEnv(seed, shape, dtype)
fns = [make_fn(i) for i in range(num_envs)]
env1 = DummyVecEnv(fns)
env2 = klass(fns)
try:
obs1, obs2 = env1.reset(), env2.reset()
assert np.array(obs1).shape == np.array(obs2).shape
assert np.allclose(obs1, obs2)
np.random.seed(1337)
for _ in range(num_steps):
joint_shape = (len(fns),) + shape
actions = np.array(np.random.randint(0, 0x100, size=joint_shape),
dtype=dtype)
for env in [env1, env2]:
env.step_async(actions)
outs1 = env1.step_wait()
outs2 = env2.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()
class _SimpleEnv(gym.Env):
"""
An environment with a pre-determined observation space
and RNG seed.
"""
def __init__(self, seed, shape, dtype):
np.random.seed(seed)
self._dtype = dtype
self._start_obs = np.array(np.random.randint(0, 0x100, size=shape),
dtype=dtype)
self._max_steps = seed + 1
self._cur_obs = None
self._cur_step = 0
self.action_space = gym.spaces.Box(low=0, high=100, shape=shape, dtype=dtype)
self.observation_space = self.action_space
def step(self, action):
self._cur_obs += np.array(action, dtype=self._dtype)
self._cur_step += 1
done = self._cur_step >= self._max_steps
reward = self._cur_step / self._max_steps
return self._cur_obs, reward, done, {'foo': 'bar' + str(reward)}
def reset(self):
self._cur_obs = self._start_obs
self._cur_step = 0
return self._cur_obs
def render(self, mode=None):
raise NotImplementedError

59
baselines/common/vec_env/util.py Normal file
View File

@@ -0,0 +1,59 @@
"""
Helpers for dealing with vectorized environments.
"""
from collections import OrderedDict
import gym
import numpy as np
def copy_obs_dict(obs):
"""
Deep-copy an observation dict.
"""
return {k: np.copy(v) for k, v in obs.items()}
def dict_to_obs(obs_dict):
"""
Convert an observation dict into a raw array if the
original observation space was not a Dict space.
"""
if set(obs_dict.keys()) == {None}:
return obs_dict[None]
return obs_dict
def obs_space_info(obs_space):
"""
Get dict-structured information about a gym.Space.
Returns:
A tuple (keys, shapes, dtypes):
keys: a list of dict keys.
shapes: a dict mapping keys to shapes.
dtypes: a dict mapping keys to dtypes.
"""
if isinstance(obs_space, gym.spaces.Dict):
assert isinstance(obs_space.spaces, OrderedDict)
subspaces = obs_space.spaces
else:
subspaces = {None: obs_space}
keys = []
shapes = {}
dtypes = {}
for key, box in subspaces.items():
keys.append(key)
shapes[key] = box.shape
dtypes[key] = box.dtype
return keys, shapes, dtypes
def obs_to_dict(obs):
"""
Convert an observation into a dict.
"""
if isinstance(obs, dict):
return obs
return {None: obs}

13
baselines/common/vec_env/vec_frame_stack.py
View File

@@ -1,18 +1,16 @@
from baselines.common.vec_env import VecEnvWrapper
from . import VecEnvWrapper
import numpy as np
from gym import spaces
class VecFrameStack(VecEnvWrapper):
"""
Vectorized environment base class
"""
def __init__(self, venv, nstack):
self.venv = venv
self.nstack = nstack
wos = venv.observation_space # wrapped ob space
wos = venv.observation_space # wrapped ob space
low = np.repeat(wos.low, self.nstack, axis=-1)
high = np.repeat(wos.high, self.nstack, axis=-1)
self.stackedobs = np.zeros((venv.num_envs,)+low.shape, low.dtype)
self.stackedobs = np.zeros((venv.num_envs,) + low.shape, low.dtype)
observation_space = spaces.Box(low=low, high=high, dtype=venv.observation_space.dtype)
VecEnvWrapper.__init__(self, venv, observation_space=observation_space)
@@ -26,9 +24,6 @@ class VecFrameStack(VecEnvWrapper):
return self.stackedobs, rews, news, infos
def reset(self):
"""
Reset all environments
"""
obs = self.venv.reset()
self.stackedobs[...] = 0
self.stackedobs[..., -obs.shape[-1]:] = obs

29
baselines/common/vec_env/vec_monitor.py Normal file
View File

@@ -0,0 +1,29 @@
from . import VecEnvWrapper
import numpy as np
class VecMonitor(VecEnvWrapper):
def __init__(self, venv):
VecEnvWrapper.__init__(self, venv)
self.eprets = None
self.eplens = None
def reset(self):
obs = self.venv.reset()
self.eprets = np.zeros(self.num_envs, 'f')
self.eplens = np.zeros(self.num_envs, 'i')
return obs
def step_wait(self):
obs, rews, dones, infos = self.venv.step_wait()
self.eprets += rews
self.eplens += 1
newinfos = []
for (i, (done, ret, eplen, info)) in enumerate(zip(dones, self.eprets, self.eplens, infos)):
info = info.copy()
if done:
info['episode'] = {'r': ret, 'l': eplen}
self.eprets[i] = 0
self.eplens[i] = 0
newinfos.append(info)
return obs, rews, dones, newinfos

18
baselines/common/vec_env/vec_normalize.py
View File

@@ -1,17 +1,18 @@
from baselines.common.vec_env import VecEnvWrapper
from . import VecEnvWrapper
from baselines.common.running_mean_std import RunningMeanStd
import numpy as np
class VecNormalize(VecEnvWrapper):
"""
Vectorized environment base class
A vectorized wrapper that normalizes the observations
and returns from an environment.
"""
def __init__(self, venv, ob=True, ret=True, clipob=10., cliprew=10., gamma=0.99, epsilon=1e-8):
VecEnvWrapper.__init__(self, venv)
self.ob_rms = RunningMeanStd(shape=self.observation_space.shape) if ob else None
self.ret_rms = RunningMeanStd(shape=()) if ret else None
#self.ob_rms = TfRunningMeanStd(shape=self.observation_space.shape, scope='observation_running_mean_std') if ob else None
#self.ret_rms = TfRunningMeanStd(shape=(), scope='return_running_mean_std') if ret else None
self.clipob = clipob
self.cliprew = cliprew
self.ret = np.zeros(self.num_envs)
@@ -19,12 +20,6 @@ class VecNormalize(VecEnvWrapper):
self.epsilon = epsilon
def step_wait(self):
"""
Apply sequence of actions to sequence of environments
actions -> (observations, rewards, news)
where 'news' is a boolean vector indicating whether each element is new.
"""
obs, rews, news, infos = self.venv.step_wait()
self.ret = self.ret * self.gamma + rews
obs = self._obfilt(obs)
@@ -42,8 +37,5 @@ class VecNormalize(VecEnvWrapper):
return obs
def reset(self):
"""
Reset all environments
"""
obs = self.venv.reset()
return self._obfilt(obs)

27
baselines/deepq/README.md
View File

@@ -9,44 +9,29 @@ Here's a list of commands to run to quickly get a working example:
```bash
# Train model and save the results to cartpole_model.pkl
python -m baselines.deepq.experiments.train_cartpole
python -m baselines.run --alg=deepq --env=CartPole-v0 --save_path=./cartpole_model.pkl --num_timesteps=1e5
# Load the model saved in cartpole_model.pkl and visualize the learned policy
python -m baselines.deepq.experiments.enjoy_cartpole
python -m baselines.run --alg=deepq --env=CartPole-v0 --load_path=./cartpole_model.pkl --num_timesteps=0 --play
```
Be sure to check out the source code of [both](experiments/train_cartpole.py) [files](experiments/enjoy_cartpole.py)!
## If you wish to apply DQN to solve a problem.
Check out our simple agent trained with one stop shop `deepq.learn` function.
- [baselines/deepq/experiments/train_cartpole.py](experiments/train_cartpole.py) - train a Cartpole agent.
- [baselines/deepq/experiments/train_pong.py](experiments/train_pong.py) - train a Pong agent using convolutional neural networks.
In particular notice that once `deepq.learn` finishes training it returns `act` function which can be used to select actions in the environment. Once trained you can easily save it and load at later time. For both of the files listed above there are complimentary files `enjoy_cartpole.py` and `enjoy_pong.py` respectively, that load and visualize the learned policy.
In particular notice that once `deepq.learn` finishes training it returns `act` function which can be used to select actions in the environment. Once trained you can easily save it and load at later time. Complimentary file `enjoy_cartpole.py` loads and visualizes the learned policy.
## If you wish to experiment with the algorithm
##### Check out the examples
- [baselines/deepq/experiments/custom_cartpole.py](experiments/custom_cartpole.py) - Cartpole training with more fine grained control over the internals of DQN algorithm.
- [baselines/deepq/experiments/run_atari.py](experiments/run_atari.py) - more robust setup for training at scale.
##### Download a pretrained Atari agent
For some research projects it is sometimes useful to have an already trained agent handy. There's a variety of models to choose from. You can list them all by running:
- [baselines/deepq/defaults.py](defaults.py) - settings for training on atari. Run
```bash
python -m baselines.deepq.experiments.atari.download_model
python -m baselines.run --alg=deepq --env=PongNoFrameskip-v4
```
to train on Atari Pong (see more in repo-wide [README.md](../../README.md#training-models))
Once you pick a model, you can download it and visualize the learned policy. Be sure to pass `--dueling` flag to visualization script when using dueling models.
```bash
python -m baselines.deepq.experiments.atari.download_model --blob model-atari-duel-pong-1 --model-dir /tmp/models
python -m baselines.deepq.experiments.atari.enjoy --model-dir /tmp/models/model-atari-duel-pong-1 --env Pong --dueling
```

2
baselines/deepq/build_graph.py
View File

@@ -309,7 +309,7 @@ def build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope="deepq",
outputs=output_actions,
givens={update_eps_ph: -1.0, stochastic_ph: True, reset_ph: False, update_param_noise_threshold_ph: False, update_param_noise_scale_ph: False},
updates=updates)
def act(ob, reset, update_param_noise_threshold, update_param_noise_scale, stochastic=True, update_eps=-1):
def act(ob, reset=False, update_param_noise_threshold=False, update_param_noise_scale=False, stochastic=True, update_eps=-1):
return _act(ob, stochastic, update_eps, reset, update_param_noise_threshold, update_param_noise_scale)
return act

6
baselines/deepq/deepq.py
View File

@@ -27,7 +27,7 @@ class ActWrapper(object):
self.initial_state = None
@staticmethod
def load_act(self, path):
def load_act(path):
with open(path, "rb") as f:
model_data, act_params = cloudpickle.load(f)
act = deepq.build_act(**act_params)
@@ -70,6 +70,7 @@ class ActWrapper(object):
def save(self, path):
save_state(path)
self.save_act(path+".pickle")
def load_act(path):
@@ -194,8 +195,9 @@ def learn(env,
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(env.observation_space, name=name)
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,

12
baselines/deepq/experiments/run_atari.py
View File

@@ -23,17 +23,15 @@ def main():
env = make_atari(args.env)
env = bench.Monitor(env, logger.get_dir())
env = deepq.wrap_atari_dqn(env)
model = deepq.models.cnn_to_mlp(
convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
hiddens=[256],
dueling=bool(args.dueling),
)
deepq.learn(
env,
q_func=model,
"conv_only",
convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)],
hiddens=[256],
dueling=bool(args.dueling),
lr=1e-4,
max_timesteps=args.num_timesteps,
total_timesteps=args.num_timesteps,
buffer_size=10000,
exploration_fraction=0.1,
exploration_final_eps=0.01,

5
baselines/deepq/experiments/train_cartpole.py
View File

@@ -11,12 +11,11 @@ def callback(lcl, _glb):
def main():
env = gym.make("CartPole-v0")
model = deepq.models.mlp([64])
act = deepq.learn(
env,
q_func=model,
network='mlp',
lr=1e-3,
max_timesteps=100000,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,

6
baselines/ppo2/README.md
View File

@@ -2,5 +2,7 @@
- Original paper: https://arxiv.org/abs/1707.06347
- Baselines blog post: https://blog.openai.com/openai-baselines-ppo/
- `python -m baselines.ppo2.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.ppo2.run_mujoco` runs the algorithm for 1M frames on a Mujoco environment.
- `python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
- `python -m baselines.run --alg=ppo2 --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M frames on a Mujoco Ant environment.
- also refer to the repo-wide [README.md](../../README.md#training-models)

19
baselines/run.py
View File

@@ -5,6 +5,7 @@ import os.path as osp
import gym
from collections import defaultdict
import tensorflow as tf
import numpy as np
from baselines.common.vec_env.vec_frame_stack import VecFrameStack
from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_mujoco_env, make_atari_env
@@ -75,10 +76,10 @@ def train(args, extra_args):
return model, env
def build_env(args, render=False):
def build_env(args):
ncpu = multiprocessing.cpu_count()
if sys.platform == 'darwin': ncpu //= 2
nenv = args.num_env or ncpu if not render else 1
nenv = args.num_env or ncpu
alg = args.alg
rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
seed = args.seed
@@ -123,14 +124,18 @@ def build_env(args, render=False):
env = bench.Monitor(env, logger.get_dir())
env = retro_wrappers.wrap_deepmind_retro(env)
elif env_type == 'classic':
elif env_type == 'classic_control':
def make_env():
e = gym.make(env_id)
e = bench.Monitor(e, logger.get_dir(), allow_early_resets=True)
e.seed(seed)
return e
env = DummyVecEnv([make_env])
else:
raise ValueError('Unknown env_type {}'.format(env_type))
return env
@@ -149,7 +154,7 @@ def get_env_type(env_id):
return env_type, env_id
def get_default_network(env_type):
if env_type == 'mujoco' or env_type=='classic':
if env_type == 'mujoco' or env_type == 'classic_control':
return 'mlp'
if env_type == 'atari':
return 'cnn'
@@ -215,12 +220,14 @@ def main():
if args.play:
logger.log("Running trained model")
env = build_env(args, render=True)
env = build_env(args)
obs = env.reset()
while True:
actions = model.step(obs)[0]
obs, _, done, _ = env.step(actions)
env.render()
done = done.any() if isinstance(done, np.ndarray) else done
if done:
obs = env.reset()

5
baselines/trpo_mpi/README.md
View File

@@ -2,5 +2,6 @@
- Original paper: https://arxiv.org/abs/1502.05477
- Baselines blog post https://blog.openai.com/openai-baselines-ppo/
- `mpirun -np 16 python -m baselines.trpo_mpi.run_atari` runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (`-h`) for more options.
- `python -m baselines.trpo_mpi.run_mujoco` runs the algorithm for 1M timesteps on a Mujoco environment.
- `mpirun -np 16 python -m baselines.run --alg=trpo_mpi --env=PongNoFrameskip-v4` runs the algorithm for 40M frames = 10M timesteps on an Atari Pong. See help (`-h`) for more options.
- `python -m baselines.run --alg=trpo_mpi --env=Ant-v2 --num_timesteps=1e6` runs the algorithm for 1M timesteps on a Mujoco Ant environment.
- also refer to the repo-wide [README.md](../../README.md#training-models)

2
baselines/trpo_mpi/defaults.py
View File

@@ -1,4 +1,4 @@
from rl_common.models import mlp, cnn_small
from baselines.common.models import mlp, cnn_small
def atari():

19
conftest.py
View File

@@ -1,19 +0,0 @@
import pytest
def pytest_addoption(parser):
parser.addoption('--runslow', action='store_true', default=False, help='run slow tests')
def pytest_collection_modifyitems(config, items):
if config.getoption('--runslow'):
# --runslow given in cli: do not skip slow tests
return
skip_slow = pytest.mark.skip(reason='need --runslow option to run')
slow_tests = []
for item in items:
if 'slow' in item.keywords:
slow_tests.append(item.name)
item.add_marker(skip_slow)
print('skipping slow tests', ' '.join(slow_tests), 'use --runslow to run this')