[CI] [RLlib] Prevent Black from erroring while formatting `multi_agent_env.py`

[bveeramani]

Ray Component

RLlib

What happened + What you expected to happen

I ran black rllib/env/multi_agent_env.py but got the message

error: cannot format rllib/env/multi_agent_env.py: INTERNAL ERROR: Black produce
d code that is not equivalent to the source on pass 1.  Please report a bug on h
ttps://github.com/psf/black/issues.  This diff might be helpful: /var/folders/gx
/t32twm6x54dftn9b2wxcbl100000gn/T/blk_8yrnynba.log
Oh no! 💥 💔 💥
1 file failed to reformat.

I'm not sure why the error occurs, but it has something to do with the yapf: disable.

ray/rllib/env/multi_agent_env.py

Lines 91 to 183 in 6996eaa

	# yapf: disable
	# __grouping_doc_begin__
	@ExperimentalAPI
	def with_agent_groups(
	self,
	groups: Dict[str, List[AgentID]],
	obs_space: gym.Space = None,
	act_space: gym.Space = None) -> "MultiAgentEnv":
	"""Convenience method for grouping together agents in this env.
	An agent group is a list of agent IDs that are mapped to a single
	logical agent. All agents of the group must act at the same time in the
	environment. The grouped agent exposes Tuple action and observation
	spaces that are the concatenated action and obs spaces of the
	individual agents.
	The rewards of all the agents in a group are summed. The individual
	agent rewards are available under the "individual_rewards" key of the
	group info return.
	Agent grouping is required to leverage algorithms such as Q-Mix.
	This API is experimental.
	Args:
	groups: Mapping from group id to a list of the agent ids
	of group members. If an agent id is not present in any group
	value, it will be left ungrouped.
	obs_space: Optional observation space for the grouped
	env. Must be a tuple space.
	act_space: Optional action space for the grouped env.
	Must be a tuple space.
	Examples:
	>>> env = YourMultiAgentEnv(...)
	>>> grouped_env = env.with_agent_groups(env, {
	... "group1": ["agent1", "agent2", "agent3"],
	... "group2": ["agent4", "agent5"],
	... })
	"""
	from ray.rllib.env.wrappers.group_agents_wrapper import \
	GroupAgentsWrapper
	return GroupAgentsWrapper(self, groups, obs_space, act_space)
	@PublicAPI
	def to_base_env(self,
	make_env: Callable[[int], EnvType] = None,
	num_envs: int = 1,
	remote_envs: bool = False,
	remote_env_batch_wait_ms: int = 0,
	) -> "BaseEnv":
	"""Converts an RLlib MultiAgentEnv into a BaseEnv object.
	The resulting BaseEnv is always vectorized (contains n
	sub-environments) to support batched forward passes, where n may
	also be 1. BaseEnv also supports async execution via the `poll` and
	`send_actions` methods and thus supports external simulators.
	Args:
	make_env: A callable taking an int as input (which indicates
	the number of individual sub-environments within the final
	vectorized BaseEnv) and returning one individual
	sub-environment.
	num_envs: The number of sub-environments to create in the
	resulting (vectorized) BaseEnv. The already existing `env`
	will be one of the `num_envs`.
	remote_envs: Whether each sub-env should be a @ray.remote
	actor. You can set this behavior in your config via the
	`remote_worker_envs=True` option.
	remote_env_batch_wait_ms: The wait time (in ms) to poll remote
	sub-environments for, if applicable. Only used if
	`remote_envs` is True.
	Returns:
	The resulting BaseEnv object.
	"""
	from ray.rllib.env.remote_vector_env import RemoteBaseEnv
	if remote_envs:
	env = RemoteBaseEnv(
	make_env,
	num_envs,
	multiagent=True,
	remote_env_batch_wait_ms=remote_env_batch_wait_ms)
	# Sub-environments are not ray.remote actors.
	else:
	env = MultiAgentEnvWrapper(
	make_env=make_env, existing_envs=[self], num_envs=num_envs)
	return env
	# __grouping_doc_end__
	# yapf: enable

Versions / Dependencies

• Python: 3.8.12
• OS: macOS 11.5.2
• Black: 21.12b0
• Ray commit: 14ed7cfaaa8f890204619c149d4d50b5857c3cd6

Reproduction script

black rllib/env/multi_agent_env.py