OpenSpiel#

Installation#

pip install shimmy[pettingzoo]

Usage#

import pyspiel
from shimmy.openspiel_compatibility import OpenspielCompatibilityV0

env = pyspiel.load_game("2048")
env = OpenspielCompatibilityV0(game=env, render_mode=None)

Class Description#

class shimmy.openspiel_compatibility.OpenspielCompatibilityV0(game: pyspiel.Game, render_mode: str | None)#

This compatibility wrapper that converts an openspiel environment into a pettingzoo environment.

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas.

metadata: Dict[str, Any] = {'render_modes': []}#
possible_agents: List[AgentID]#
observation_space(agent: str)#

observation_space.

We get the observation space from the underlying game. OpenSpiel possibly provides information and observation in several forms. This wrapper chooses which one to use depending on the following precedence:

  1. Observation Tensor

  2. Information Tensor

  3. Observation String

  4. Information String

Parameters:

agent (AgentID) – agent

action_space(agent: str)#

action_space.

Parameters:

agent (AgentID) – agent

render()#

render.

observe(agent: str)#

observe.

Parameters:

agent (AgentID) – agent

close()#

close.

reset(seed: int | None = None, return_info: bool | None = False, options: dict | None = None)#

reset.

Parameters:

  • seed (Optional[int]) – seed

  • return_info (Optional[bool]) – return_info

  • options (Optional[Dict]) – options

step(action: int)#

Steps the environment.

Parameters:

action (int) – action

agents: List[AgentID]#
observation_spaces: Dict[AgentID, gymnasium.spaces.Space]#
action_spaces: Dict[AgentID, gymnasium.spaces.Space]#
terminations: Dict[AgentID, bool]#
truncations: Dict[AgentID, bool]#
rewards: Dict[AgentID, float]#
infos: Dict[AgentID, Dict[str, Any]]#
agent_selection: AgentID#