def define_batch_env(env_ctor, num_agents, isolate_envs):
with tf.variable_scope('environments'):
if isolate_envs == 'none':
factory = lambda ctor: ctor()
blocking = True
elif isolate_envs == 'thread':
factory = functools.partial(wrappers.Async, strategy='thread')
blocking = False
elif isolate_envs == 'process':
factory = functools.partial(wrappers.Async, strategy='process')
blocking = False
else:
raise NotImplementedError(isolate_envs)
envs = [factory(env_ctor) for _ in range(num_agents)]
env = batch_env.BatchEnv(envs, blocking)
env = in_graph_batch_env.InGraphBatchEnv(env)
return env
def define_batch_env(env_ctor, num_agents, env_processes):
with tf.variable_scope('environments'):
if env_processes:
envs = [
wrappers.ExternalProcess(env_ctor) for _ in range(num_agents)
]
else:
envs = [env_ctor() for _ in range(num_agents)]
env = batch_env.BatchEnv(envs, blocking=not env_processes)
# # For testing the wrapper class BatchEnv:
# env.reset()
# env.step(np.array([[0.5,0.5]]))
env = in_graph_batch_env.InGraphBatchEnv(env)
# # For testing the wrapper class BatchEnv:
# env.reset()
# env.step(np.array([[0.5,0.5]]))
return env