MultiAgentEnv API#

rllib.env.multi_agent_env.MultiAgentEnv#

ray.rllib.env.multi_agent_env.MultiAgentEnv#

alias of <MagicMock spec=’str’ id=’140207173656784’>

Convert gym.Env into MultiAgentEnv#

ray.rllib.env.multi_agent_env.make_multi_agent(env_name_or_creator: Union[str, Callable[[EnvContext], Optional[Any]]]) → Type[MultiAgentEnv][source]#

Convenience wrapper for any single-agent env to be converted into MA.

Allows you to convert a simple (single-agent) gym.Env class into a MultiAgentEnv class. This function simply stacks n instances of the given `gym.Env` class into one unified MultiAgentEnv class and returns this class, thus pretending the agents act together in the same environment, whereas - under the hood - they live separately from each other in n parallel single-agent envs.

Agent IDs in the resulting and are int numbers starting from 0 (first agent).

Parameters

env_name_or_creator – String specifier or env_maker function taking an EnvContext object as only arg and returning a gym.Env.

Returns

New MultiAgentEnv class to be used as env. The constructor takes a config dict with num_agents key (default=1). The rest of the config dict will be passed on to the underlying single-agent env’s constructor.

Examples

>>> from ray.rllib.env.multi_agent_env import make_multi_agent
>>> # By gym string:
>>> ma_cartpole_cls = make_multi_agent("CartPole-v1") 
>>> # Create a 2 agent multi-agent cartpole.
>>> ma_cartpole = ma_cartpole_cls({"num_agents": 2}) 
>>> obs = ma_cartpole.reset() 
>>> print(obs) 
{0: [...], 1: [...]}
>>> # By env-maker callable:
>>> from ray.rllib.examples.env.stateless_cartpole 
...    import StatelessCartPole
>>> ma_stateless_cartpole_cls = make_multi_agent( 
...    lambda config: StatelessCartPole(config)) 
>>> # Create a 3 agent multi-agent stateless cartpole.
>>> ma_stateless_cartpole = ma_stateless_cartpole_cls( 
...    {"num_agents": 3}) 
>>> print(obs) 
{0: [...], 1: [...], 2: [...]}