def define_batch_env2(config_envs, env_processes):
"""Create environments and apply all desired wrappers.
Args:
constructor: Constructor of an OpenAI gym environment.
num_agents: Number of environments to combine in the batch.
env_processes: Whether to step environment in external processes.
Returns:
In-graph environments object.
"""
with tf.variable_scope('environments'):
if env_processes:
envs = []
j = 0
for env in config_envs:
envs.append(tools.wrappers.ExternalProcess2(env, j))
j = j + 1
# envs = [
# tools.wrappers.ExternalProcess(constructor)
# for _ in range(num_agents)]
else:
envs = [constructor() for _ in range(num_agents)]
batch_env = tools.BatchEnv(envs, blocking=not env_processes)
batch_env = tools.InGraphBatchEnv(batch_env)
return batch_env
def _define_batch_env(constructor, num_agents, env_processes, blocking):
if env_processes:
envs = [
tools.wrappers.ExternalProcess(constructor)
for _ in range(num_agents)]
else:
envs = [constructor() for _ in range(num_agents)]
batch_env = tools.BatchEnv(envs, blocking=blocking)
batch_env = tools.InGraphBatchEnv(batch_env)
return batch_env
def _create_test_batch_env(self, durations):
envs = []
for duration in durations:
env = tools.MockEnvironment(observ_shape=(2, 3),
action_shape=(3, ),
min_duration=duration,
max_duration=duration)
env = tools.wrappers.ConvertTo32Bit(env)
envs.append(env)
batch_env = tools.BatchEnv(envs, blocking=True)
batch_env = tools.InGraphBatchEnv(batch_env)
return batch_env
def define_batch_env(constructor, num_agents, env_processes, outdir=None):
"""Create environments and apply all desired wrappers.
Args:
constructor: Constructor of an OpenAI gym environment.
num_agents: Number of environments to combine in the batch.
env_processes: Whether to step environment in external processes.
Returns:
In-graph environments object.
"""
with tf.variable_scope('environments'):
if env_processes:
if num_agents > 1 and outdir is not None:
envs = []
envs = [
tools.wrappers.ExternalProcess(constructor)
for _ in range(num_agents - 1)
]
video_env = constructor()
import gym
video_env = gym.wrappers.Monitor(
video_env,
outdir,
video_callable=capped_cubic_video_schedule,
force=True)
envs.append(tools.wrappers.ExternalProcess(lambda: video_env))
else:
envs = [
tools.wrappers.ExternalProcess(constructor)
for _ in range(num_agents)
]
else:
envs = [constructor() for _ in range(num_agents)]
batch_env = tools.BatchEnv(envs, blocking=not env_processes)
batch_env = tools.InGraphBatchEnv(batch_env)
return batch_env
