def _setup(self, config):
env = self._env_id
if env:
config["env"] = env
if _global_registry.contains(ENV_CREATOR, env):
self.env_creator = _global_registry.get(ENV_CREATOR, env)
else:
import gym # soft dependency
self.env_creator = lambda env_config: gym.make(env)
else:
self.env_creator = lambda env_config: None
# Merge the supplied config with the class default
merged_config = copy.deepcopy(self._default_config)
merged_config = deep_update(merged_config, config,
self._allow_unknown_configs,
self._allow_unknown_subkeys)
self.raw_user_config = config
self.config = merged_config
if self.config["normalize_actions"]:
inner = self.env_creator
self.env_creator = (
lambda env_config: NormalizeActionWrapper(inner(env_config)))
Trainer._validate_config(self.config)
log_level = self.config.get("log_level")
if log_level in ["WARN", "ERROR"]:
logger.info("Current log_level is {}. For more information, "
"set 'log_level': 'INFO' / 'DEBUG' or use the -v and "
"-vv flags.".format(log_level))
if self.config.get("log_level"):
logging.getLogger("ray.rllib").setLevel(self.config["log_level"])
def get_scope():
if tf and not tf.executing_eagerly():
return tf.Graph().as_default()
else:
return open("/dev/null") # fake a no-op scope
with get_scope():
self._init(self.config, self.env_creator)
# Evaluation setup.
if self.config.get("evaluation_interval"):
# Update env_config with evaluation settings:
extra_config = copy.deepcopy(self.config["evaluation_config"])
extra_config.update({
"batch_mode": "complete_episodes",
"batch_steps": 1,
})
logger.debug(
"using evaluation_config: {}".format(extra_config))
self.evaluation_workers = self._make_workers(
self.env_creator,
self._policy,
merge_dicts(self.config, extra_config),
num_workers=self.config["evaluation_num_workers"])
self.evaluation_metrics = {}
def normalize(env):
import gym # soft dependency
if not isinstance(env, gym.Env):
raise ValueError(
"Cannot apply NormalizeActionActionWrapper to env of "
"type {}, which does not subclass gym.Env.", type(env))
return NormalizeActionWrapper(env)
def __init__(self, env_config):
assert "num_agents" in env_config
assert "env_name" in env_config
num_agents = env_config['num_agents']
agent_ids = ["agent{}".format(i) for i in range(num_agents)]
self._render_policy = env_config.get('render_policy')
self.num_agents = num_agents
self.agent_ids = agent_ids
self.env_name = env_config['env_name']
self.env_maker = get_env_maker(env_config['env_name'],
require_render=bool(
self._render_policy))
if env_config.get("normalize_action", False):
self.env_maker = lambda: NormalizeActionWrapper(self.env_maker())
self.envs = {}
if not isinstance(agent_ids, list):
agent_ids = [agent_ids]
for aid in agent_ids:
if aid not in self.envs:
self.envs[aid] = self.env_maker()
self.dones = set()
tmp_env = next(iter(self.envs.values()))
self.observation_space = tmp_env.observation_space
self.action_space = tmp_env.action_space
self.reward_range = tmp_env.reward_range
self.metadata = tmp_env.metadata
self.spec = tmp_env.spec