pettingzoo_env#
Source code: tianshou/env/pettingzoo_env.py
- class PettingZooEnv(env: BaseWrapper)[source]#
Bases:
AECEnv
,ABC
The interface for petting zoo environments.
Multi-agent environments must be wrapped as
PettingZooEnv
. Here is the usage:env = PettingZooEnv(...) # obs is a dict containing obs, agent_id, and mask obs = env.reset() action = policy(obs) obs, rew, trunc, term, info = env.step(action) env.close()
The available action’s mask is set to True, otherwise it is set to False. Further usage can be found at Multi-Agent Reinforcement Learning.
- reset(*args: Any, **kwargs: Any) tuple[dict, dict] [source]#
Resets the environment to a starting state.
- step(action: Any) tuple[dict, list[int], bool, bool, dict] [source]#
Accepts and executes the action of the current agent_selection in the environment.
Automatically switches control to the next agent.
- close() None [source]#
Closes any resources that should be released.
Closes the rendering window, subprocesses, network connections, or any other resources that should be released.
- render() Any [source]#
Renders the environment as specified by self.render_mode.
Render mode can be human to display a window. Other render modes in the default environments are ‘rgb_array’ which returns a numpy array and is supported by all environments outside of classic, and ‘ansi’ which returns the strings printed (specific to classic environments).