defaults are handled through registry

baselines/acer/acer.py

@@ -16,6 +16,7 @@ from baselines.a2c.utils import EpisodeStats
 from baselines.a2c.utils import get_by_index, check_shape, avg_norm, gradient_add, q_explained_variance
 from baselines.acer.buffer import Buffer
 from baselines.acer.runner import Runner
+from baselines.acer.defaults import defaults
 
 # remove last step
 def strip(var, nenvs, nsteps, flat = False):
@@ -270,7 +271,7 @@ class Acer():
                 logger.record_tabular(name, float(val))
             logger.dump_tabular()
 
-@registry.register('acer')
+@registry.register('acer', defaults=defaults)
 def learn(network, env, seed=None, nsteps=20, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
           max_grad_norm=10, lr=7e-4, lrschedule='linear', rprop_epsilon=1e-5, rprop_alpha=0.99, gamma=0.99,
           log_interval=100, buffer_size=50000, replay_ratio=4, replay_start=10000, c=10.0,

baselines/acer/defaults.py

@@ -1,4 +1,3 @@
-def atari():
-    return dict(
-        lrschedule='constant'
-    )
+defaults = {
+    'atari': dict(lrschedule='constant')
+}

baselines/acktr/acktr.py

@@ -11,6 +11,7 @@ from baselines.common.tf_util import get_session, save_variables, load_variables
 from baselines.a2c.runner import Runner
 from baselines.a2c.utils import Scheduler, find_trainable_variables
 from baselines.acktr import kfac
+from baselines.acktr.defaults import defaults
 
 
 class Model(object):
@@ -90,7 +91,7 @@ class Model(object):
         self.initial_state = step_model.initial_state
         tf.global_variables_initializer().run(session=sess)
 
-@registry.register('acktr')
+@registry.register('acktr', defaults=defaults)
 def learn(network, env, seed, total_timesteps=int(40e6), gamma=0.99, log_interval=1, nprocs=32, nsteps=20,
                  ent_coef=0.01, vf_coef=0.5, vf_fisher_coef=1.0, lr=0.25, max_grad_norm=0.5,
                  kfac_clip=0.001, save_interval=None, lrschedule='linear', load_path=None, is_async=True, **network_kwargs):

baselines/acktr/defaults.py

@@ -1,5 +1,6 @@
-def mujoco():
-    return dict(
+defaults = {
+    'mujoco' : dict(
         nsteps=2500,
         value_network='copy'
     )
+}

baselines/common/cmd_util.py

@@ -16,7 +16,7 @@ from baselines.common import set_global_seeds
 from baselines.common.atari_wrappers import make_atari, wrap_deepmind
 from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
 from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
-from baselines.common.vec_env.vec_normalize import VecNormalize
+from baselines.common.vec_env.vec_frame_stack import VecFrameStack
 
 from baselines.common import retro_wrappers
 
@@ -46,6 +46,8 @@ def make_vec_env(env_id, env_type, num_env, seed, wrapper_kwargs=None, start_ind
     if frame_stack_size > 1:
         venv = VecFrameStack(venv, frame_stack_size)
 
+    return venv
+
 
 def env_thunk(env_id, env_type, subrank=0, seed=None, reward_scale=1.0, gamestate=None, wrapper_kwargs={}):
     mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0

baselines/deepq/deepq.py

@@ -18,6 +18,7 @@ from baselines.deepq.utils import ObservationInput
 
 from baselines.common.tf_util import get_session
 from baselines.deepq.models import build_q_func
+from baselines.deepq.defaults import defaults
 
 
 class ActWrapper(object):
@@ -92,7 +93,7 @@ def load_act(path):
     return ActWrapper.load_act(path)
 
 
-@registry.register('deepq', supports_vecenvs=False)
+@registry.register('deepq', supports_vecenvs=False, defaults=defaults)
 def learn(env,
           network,
           seed=None,

baselines/deepq/defaults.py

@@ -16,6 +16,8 @@ def atari():
         dueling=True
     )
 
-def retro():
-    return atari()
 
+defaults = {
+    'atari': atari()
+    'retro': atari()
+}

baselines/ppo2/defaults.py

@@ -1,5 +1,5 @@
-def mujoco():
-    return dict(
+defaults = {
+    'mujoco': dict(
         nsteps=2048,
         nminibatches=32,
         lam=0.95,
@@ -10,13 +10,13 @@ def mujoco():
         lr=lambda f: 3e-4 * f,
         cliprange=0.2,
         value_network='copy'
-    )
+    ),
 
-def atari():
-    return dict(
+    'atari': dict(
         nsteps=128, nminibatches=4,
         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,
     )
+}

baselines/ppo2/ppo2.py

@@ -15,6 +15,7 @@ from baselines.common.mpi_adam_optimizer import MpiAdamOptimizer
 from mpi4py import MPI
 from baselines.common.tf_util import initialize
 from baselines.common.mpi_util import sync_from_root
+from baselines.ppo2.defaults import defaults
 
 class Model(object):
     """
@@ -218,7 +219,7 @@ def constfn(val):
         return val
     return f
 
-@registry.register('ppo2')
+@registry.register('ppo2', defaults=defaults)
 def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
             vf_coef=0.5,  max_grad_norm=0.5, gamma=0.99, lam=0.95,
             log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,

baselines/registry.py

@@ -1,7 +1,7 @@
 from baselines import logger
 registry = {}
 
-def register(name, supports_vecenv=True, **kwargs):
+def register(name, supports_vecenv=True, defaults={}, **kwargs):
     def get_fn_entrypoint(fn):
         import inspect
         return '.'.join([inspect.getmodule(fn).__name__, fn.__name__])
@@ -15,6 +15,7 @@ def register(name, supports_vecenv=True, **kwargs):
         registry[name] = dict(
             fn = learn_fn,
             supports_vecenv=supports_vecenv,
+            defaults=defaults,
             **kwargs
         )
         return learn_fn

baselines/run.py

@@ -5,7 +5,7 @@ import gym
 from collections import defaultdict
 import numpy as np
 
-from baselines.common.vec_env.vec_frame_stack import VecFrameStack
+from baselines.common.vec_env.vec_normalize import VecNormalize
 from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_vec_env, env_thunk
 from baselines import logger
 from baselines.registry import registry
@@ -86,6 +86,7 @@ def build_env(args):
 
     env_type, env_id = get_env_type(args.env)
     assert alg in registry, 'Unknown algorithm {}'.format(alg)
+
     if env_type in {'atari', 'retro'}:
         frame_stack_size = 4
     else:
@@ -141,13 +142,10 @@ def get_learn_function(alg):
 
 
 def get_learn_function_defaults(alg, env_type):
-    try:
-        alg_defaults = get_alg_module(alg, 'defaults')
-        kwargs = getattr(alg_defaults, env_type)()
-    except (ImportError, AttributeError):
-        kwargs = {}
-    return kwargs
-
+    entry = registry.get(alg)
+    assert entry is not None, 'Unregistered algorithm {}'.format(alg)
+    return entry['defaults'].get(env_type, {})
+    
 
 
 def parse_cmdline_kwargs(args):

baselines/trpo_mpi/defaults.py

@@ -28,3 +28,9 @@ def mujoco():
         vf_stepsize=1e-3,
         normalize_observations=True,
     )
+
+defaults = {
+    'atari': atari(), 
+    'mujoco': mujoco(),
+}
+    

baselines/trpo_mpi/trpo_mpi.py

@@ -13,6 +13,8 @@ from baselines.common.input import observation_placeholder
 from baselines.common.policies import build_policy
 from contextlib import contextmanager
 
+from baselines.trpo_mpi.defaults import defaults
+
 def traj_segment_generator(pi, env, horizon, stochastic):
     # Initialize state variables
     t = 0
@@ -82,7 +84,7 @@ def add_vtarg_and_adv(seg, gamma, lam):
         gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam
     seg["tdlamret"] = seg["adv"] + seg["vpred"]
 
-@registry.register('trpo_mpi', supports_vecenvs=False)
+@registry.register('trpo_mpi', supports_vecenvs=False, defaults=defaults)
 def learn(*,
         network,
         env,