Test Game¶
Example server environment primarily for testing.
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class
colosseumrl.envs.testgame.TestGame.TestGame(config: str = '')[source]¶ Bases:
colosseumrl.BaseEnvironment.BaseEnvironment-
is_valid_action(state: numpy.ndarray, player: int, action: str) → bool[source]¶ Whether or not an action is valid for a specific state.
- Parameters
state (object) – The current state of the game.
player (int) – The player who is executing this action.
action (str) – The action the player is executing.
- Returns
Whether or not this is a valid action in the current state.
- Return type
bool
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property
max_players¶ Property holding the max number of players present for a game.
Currently, this should be the same as min_players. Future additions will allow for dynamic games.
- Returns
Maximum number of players allowed in game.
- Return type
int
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property
min_players¶ Property holding the number of players present required to play game.
- Returns
Minimum number of players for game to start.
- Return type
int
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new_state(num_players: int = 1)[source]¶ Create a fresh state. This could return a fixed object or randomly initialized on, depending on the game.
Note that player numbers must be numbers in the set {0, 1, …, n-1} for an n player game.
- Parameters
num_players (int) – Total number of players in this game.
- Returns
new_state (object) – The initial state for the game. This can be any python object you wish.
new_players (List[int]) – List of players who’s turn it is now.
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next_state(state: numpy.ndarray, players: [<class 'int'>], actions: [<class 'str'>]) → Tuple[numpy.ndarray, List[int], List[float], bool, Optional[List[int]]][source]¶ Compute a single step in the game.
Notes
Player numbers must be numbers in the set {0, 1, …, n-1} for an n player game.
- Parameters
state (object) – The current state of the game.
players ([int]) – The players which are taking the given actions.
actions ([str]) – The actions of each player.
- Returns
new_state (object) – The new state of the game.
new_players (List[int]) – List of players who’s turn it is in the new state now.
rewards (List[float]) – The reward for each player that acted.
terminal (bool) – Whether or not the game has ended.
winners (List[int]) – If the game has ended, who are the winners.
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static
observation_names() → List[str][source]¶ Static method for returning the names of the observation objects.
This needs to be static to setup spacetime dataframes.
- Returns
The keys of the observation dictionary.
- Return type
List[str]
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property
observation_shape¶ Describe the fixed numpy shapes of each observation.
- Returns
The shape, as a tuple, of each numpy array by their name.
- Return type
Dict[str, Tuple[int]]
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state_to_observation(state: numpy.ndarray, player: int) → Dict[str, numpy.ndarray][source]¶ Convert the raw game state to the observation for the agent. Maps each observation name into an observation.
This can return different values for the different players. Default implementation is just the identity.
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