def define_batch_env(constructor, num_agents, xvfb=False):
"""Create environments and apply all desired wrappers."""
with tf.variable_scope("environments"):
envs = [
ExternalProcessEnv(constructor, xvfb) for _ in range(num_agents)
]
env = batch_env.BatchEnv(envs, blocking=False)
env = py_func_batch_env.PyFuncBatchEnv(env)
return env
def define_batch_env(constructor, num_agents, xvfb=False):
"""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.
xvfb: Frame buffer.
env_processes: Whether to step environment in external processes.
Returns:
In-graph environments object.
"""
with tf.variable_scope("environments"):
envs = [
ExternalProcessEnv(constructor, xvfb) for _ in range(num_agents)
]
env = batch_env.BatchEnv(envs, blocking=False)
env = py_func_batch_env.PyFuncBatchEnv(env)
return env
def define_batch_env(constructor, num_agents, env_processes=True):
"""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 = [ExternalProcessEnv(constructor) for _ in range(num_agents)]
else:
envs = [constructor() for _ in range(num_agents)]
env = batch_env.BatchEnv(envs, blocking=not env_processes)
env = in_graph_batch_env.InGraphBatchEnv(env)
return env
