An environment for playing Cribbage in OpenAI gym (2-4 players).
The gym environment can be installed using python setup.py install
.
Run pip install -e .
.
The environment can be imported after install via:
from gym_cribbage.envs.cribbage_env import CribbageEnv
env = CribbageEnv(n_players=2, verbose=False)
Where n_players
can be between 2-4 and verbose
can be set to True
for
(copius) debug information.
The environment can be initalized via:
state, reward, done, debug = env.reset()
Which starts a new game. The environment can be interacted with as a standard
openAI gym environment, I.e., env.step()
, env.render()
and env.reset()
.
The state returned by the environment contains a State() object with the following fields:
State.hand
-- the current player's playable cards. Non-playable cards are never returned in the hand.State.hand_id
-- the ID of the current player.State.reward_id
-- the ID of the player who recieves the reward returned by the environment.State.phase
-- the current phase of the game.0
== 'the Deal',1
== 'the Play',2
= 'the Show'.State.player_score
-- the current score (out of 121) of the current player.State.opponent_score
-- a list of all opponent scores (out of 121).
The cribbage environment cycles through hands an accumulates scores until one
player reaches 121, whereby the environment immediately returns done==True
and the game is over. A new game can be started via
state, reward, done, debug = env.reset()
.
Actions taken during steps have different meaning during each phase of the game.
- During 'the Deal' (
State.phase == 0
), cards submitted viaenv.step(Card)
are discarded to the crib. Cards must be taken from the player's handState.hand[i]
. - During 'the Play' (
State.phase == 1
), cards submitted viaenv.step(Card)
are played to the table to earn points. Again, the must be taken from the player's handState.hand[i]
. - During 'the Show' (
State.phase == 2
), nothing submitted is used by the environment. Therefore it is convienient to pass a 'dummy card' to the environmentstate, reward, done, debug = env.step(Card(99, SUITS[0]))
, which can be easily done if one imports some utility classes fromcribbage_env
:from gym_cribbage.envs.cribbage_env import Card, SUITS
. The purpose of these steps is to return the appropriate points to each agent for Show (in sequence, following the rules of Cribbage).