def _apply_output_wrappers(env, rewards, representation, channel_dimensions,
apply_single_agent_wrappers, stacked):
"""Wraps with necessary wrappers modifying the output of the environment.
Args:
env: A GFootball gym environment.
rewards: What rewards to apply.
representation: See create_environment.representation comment.
channel_dimensions: (width, height) tuple that represents the dimensions of
SMM or pixels representation.
apply_single_agent_wrappers: Whether to reduce output to single agent case.
stacked: Should observations be stacked.
Returns:
Google Research Football environment.
"""
env = _process_reward_wrappers(env, rewards)
env = _process_representation_wrappers(env, representation,
channel_dimensions)
if apply_single_agent_wrappers:
if representation != 'raw':
env = wrappers.SingleAgentObservationWrapper(env)
env = wrappers.SingleAgentRewardWrapper(env)
if stacked:
env = wrappers.FrameStack(env, 4)
env = wrappers.GetStateWrapper(env)
return env
def __init__(self,
num_of_left_agents,
num_of_right_agents=0,
env_name="test_example_multiagent",
stacked=False,
representation='extracted',
rewards='scoring',
write_goal_dumps=False,
write_full_episode_dumps=False,
render=False,
write_video=False,
dump_frequency=1,
extra_players=None,
channel_dimensions=(96, 72),
other_config_options={}) -> None:
assert num_of_left_agents >= 0
assert num_of_right_agents >= 0
assert num_of_left_agents + num_of_right_agents != 0
# config the environment
scenario_config = config.Config({'level': env_name}).ScenarioConfig()
players = [('agent:left_players=%d,right_players=%d' %
(num_of_left_agents, num_of_right_agents))]
if extra_players is not None:
players.extend(extra_players)
config_values = {
'dump_full_episodes': write_full_episode_dumps,
'dump_scores': write_goal_dumps,
'players': players,
'level': env_name,
'tracesdir': "/tmp/gfootball_log",
'write_video': write_video,
}
config_values.update(other_config_options)
c = config.Config(config_values)
self._env = football_env.FootballEnv(c)
if dump_frequency > 1:
self._env = wrappers.PeriodicDumpWriter(self._env, dump_frequency,
render)
elif render:
self._env.render()
# _apply_output_wrappers 在只有一个agent时非要加 wrapper
self._env = _process_reward_wrappers(self._env, rewards)
self._env = _process_representation_wrappers(self._env, representation,
channel_dimensions)
if stacked:
self._env = wrappers.FrameStack(self._env, 4)
self._env = wrappers.GetStateWrapper(self._env)
self._action_space = gym.spaces.Discrete(
self._env.action_space.nvec[0])
self._observation_space = None if representation == "raw" else gym.spaces.Box(
low=self._env.observation_space.low[0],
high=self._env.observation_space.high[0],
dtype=self._env.observation_space.dtype)
self._num_left = num_of_left_agents
self._num_right = num_of_right_agents
self._share_observation_space = gym.spaces.Box(
low=np.concatenate([
self._observation_space.low
for i in range(self._num_left + self._num_right)
],
axis=-1),
high=np.concatenate([
self._observation_space.high
for i in range(self._num_left + self._num_right)
],
axis=-1),
dtype=self._observation_space.dtype)
