def make_mujoco_env(env_id, seed):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
set_global_seeds(seed)
env = gym.make(env_id)
env = Monitor(env, logger.get_dir())
env.seed(seed)
return env
def make_robotics_env(env_id, seed, rank=0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
set_global_seeds(seed)
env = gym.make(env_id)
env = FlattenDictWrapper(env, ['observation', 'desired_goal'])
env = Monitor(
env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)),
info_keywords=('is_success',))
env.seed(seed)
return env
def make_atari_env(env_id, num_env, seed, wrapper_kwargs=None, start_index=0):
"""
Create a wrapped, monitored SubprocVecEnv for Atari.
"""
if wrapper_kwargs is None: wrapper_kwargs = {}
def make_env(rank): # pylint: disable=C0111
def _thunk():
env = make_atari(env_id)
env.seed(seed + rank)
env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
return wrap_deepmind(env, **wrapper_kwargs)
return _thunk
set_global_seeds(seed)
return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)])
def main(policy_file, seed, n_test_rollouts, render):
set_global_seeds(seed)
# Load policy.
with open(policy_file, 'rb') as f:
policy = pickle.load(f)
env_name = policy.info['env_name']
# Prepare params.
params = config.DEFAULT_PARAMS
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]
) # merge env-specific parameters in
params['env_name'] = env_name
params = config.prepare_params(params)
config.log_params(params, logger=logger)
dims = config.configure_dims(params)
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'compute_Q': True,
'rollout_batch_size': 1,
'render': bool(render),
}
for name in ['T', 'gamma', 'noise_eps', 'random_eps']:
eval_params[name] = params[name]
evaluator = RolloutWorker(params['make_env'], policy, dims, logger,
**eval_params)
evaluator.seed(seed)
# Run evaluation.
evaluator.clear_history()
for _ in range(n_test_rollouts):
evaluator.generate_rollouts()
# record logs
for key, val in evaluator.logs('test'):
logger.record_tabular(key, np.mean(val))
logger.dump_tabular()
def launch(env_name,
logdir,
n_epochs,
num_cpu,
seed,
replay_strategy,
policy_save_interval,
clip_return,
override_params={},
save_policies=True):
# Fork for multi-CPU MPI implementation.
if num_cpu > 1:
whoami = mpi_fork(num_cpu)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.single_threaded_session().__enter__()
rank = MPI.COMM_WORLD.Get_rank()
# Configure logging
if rank == 0:
if logdir or logger.get_dir() is None:
logger.configure(dir=logdir)
else:
logger.configure()
logdir = logger.get_dir()
assert logdir is not None
os.makedirs(logdir, exist_ok=True)
# Seed everything.
rank_seed = seed + 1000000 * rank
set_global_seeds(rank_seed)
# Prepare params.
params = config.DEFAULT_PARAMS
params['env_name'] = env_name
params['replay_strategy'] = replay_strategy
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]
) # merge env-specific parameters in
params.update(
**override_params) # makes it possible to override any parameter
with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f:
json.dump(params, f)
params = config.prepare_params(params)
config.log_params(params, logger=logger)
dims = config.configure_dims(params)
policy = config.configure_ddpg(dims=dims,
params=params,
clip_return=clip_return)
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': False,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in [
'T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps'
]:
rollout_params[name] = params[name]
eval_params[name] = params[name]
rollout_worker = RolloutWorker(params['make_env'], policy, dims, logger,
**rollout_params)
rollout_worker.seed(rank_seed)
evaluator = RolloutWorker(params['make_env'], policy, dims, logger,
**eval_params)
evaluator.seed(rank_seed)
train(logdir=logdir,
policy=policy,
rollout_worker=rollout_worker,
evaluator=evaluator,
n_epochs=n_epochs,
n_test_rollouts=params['n_test_rollouts'],
n_cycles=params['n_cycles'],
n_batches=params['n_batches'],
policy_save_interval=policy_save_interval,
save_policies=save_policies)
def launch(env_name,
logdir,
n_epochs,
num_cpu,
seed,
replay_strategy,
policy_save_interval,
clip_return,
temperature,
prioritization,
binding,
logging,
version,
dump_buffer,
n_cycles,
rank_method,
fit_interval,
override_params={},
save_policies=True):
# Fork for multi-CPU MPI implementation.
if num_cpu > 1:
whoami = mpi_fork(num_cpu, binding)
if whoami == 'parent':
sys.exit(0)
import baselines.common.tf_util as U
U.single_threaded_session().__enter__()
rank = MPI.COMM_WORLD.Get_rank()
# Configure logging
'''
if logging:
logdir = 'logs/'+str(env_name)+'-prioritization'+str(prioritization)+'-replay_strategy'+str(replay_strategy)+\
'-n_epochs'+str(n_epochs)+'-num_cpu'+str(num_cpu)+'-seed'+str(seed)+'-version'+str(version)
else:
logdir = osp.join(tempfile.gettempdir(),
datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"))
'''
# Configure logging
if rank == 0:
if logdir or logger.get_dir() is None:
logger.configure(dir=logdir)
else:
logger.configure()
logdir = logger.get_dir()
assert logdir is not None
os.makedirs(logdir, exist_ok=True)
if rank == 0:
if logdir or logger.get_dir() is None:
logger.configure(dir=logdir)
else:
logger.configure()
logdir = logger.get_dir()
assert logdir is not None
os.makedirs(logdir, exist_ok=True)
# Seed everything.
rank_seed = seed + 1000000 * rank
set_global_seeds(rank_seed)
# Prepare params.
params = config.DEFAULT_PARAMS
params['env_name'] = env_name
params['replay_strategy'] = replay_strategy
params['temperature'] = temperature
params['prioritization'] = prioritization
params['binding'] = binding
params['max_timesteps'] = n_epochs * params['n_cycles'] * params[
'n_batches'] * num_cpu
params['version'] = version
params['dump_buffer'] = dump_buffer
params['n_cycles'] = n_cycles
params['rank_method'] = rank_method
params['fit_interval'] = fit_interval
params['n_epochs'] = n_epochs
params['num_cpu'] = num_cpu
if params['dump_buffer']:
params['alpha'] = 0
if env_name in config.DEFAULT_ENV_PARAMS:
params.update(config.DEFAULT_ENV_PARAMS[env_name]
) # merge env-specific parameters in
params.update(
**override_params) # makes it possible to override any parameter
with open(os.path.join(logger.get_dir(), 'params.json'), 'w') as f:
json.dump(params, f)
params = config.prepare_params(params)
config.log_params(params, logger=logger)
dims = config.configure_dims(params)
policy = config.configure_ddpg(dims=dims,
params=params,
clip_return=clip_return)
rollout_params = {
'exploit': False,
'use_target_net': False,
'use_demo_states': True,
'compute_Q': False,
'T': params['T'],
}
eval_params = {
'exploit': True,
'use_target_net': params['test_with_polyak'],
'use_demo_states': False,
'compute_Q': True,
'T': params['T'],
}
for name in [
'T', 'rollout_batch_size', 'gamma', 'noise_eps', 'random_eps'
]:
rollout_params[name] = params[name]
eval_params[name] = params[name]
rollout_worker = RolloutWorker(params['make_env'], policy, dims, logger,
**rollout_params)
rollout_worker.seed(rank_seed)
evaluator = RolloutWorker(params['make_env'], policy, dims, logger,
**eval_params)
evaluator.seed(rank_seed)
train(logdir=logdir,
policy=policy,
rollout_worker=rollout_worker,
evaluator=evaluator,
n_epochs=n_epochs,
n_test_rollouts=params['n_test_rollouts'],
n_cycles=params['n_cycles'],
n_batches=params['n_batches'],
policy_save_interval=policy_save_interval,
save_policies=save_policies,
num_cpu=num_cpu,
dump_buffer=dump_buffer,
rank_method=rank_method,
fit_interval=fit_interval,
prioritization=prioritization)