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First examples

One can run an example of a game running (in the build/ folder):

./examples/example --game=tic_tac_toe

Similar examples using the Python API (run from one above build):

# Similar to the C++ example:
python3 open_spiel/python/examples/example.py --game=breakthrough

# Play a game against a random or MCTS bot:
python3 open_spiel/python/examples/mcts.py --game=tic_tac_toe --player1=human --player2=random
python3 open_spiel/python/examples/mcts.py --game=tic_tac_toe --player1=human --player2=mcts

Concepts

The following documentation describes the high-level concepts. Refer to the code comments for specific API descriptions.

Note that, in English, the word "game" is used for both the description of the rules (e.g. the game of chess) and for a specific instance of a playthrough (e.g. "we played a game of chess yesterday"). We will be using "playthrough" or "trajectory" to refer to the second concept.

The methods names are in CamelCase in C++ and snake_case in Python without any other difference (e.g. state.ApplyAction in C++ will be state.apply_action in Python).

The tree representation

There are mainly 2 concepts to know about (defined in open_spiel/spiel.h):

  • A Game object contains the high level description for a game (e.g. whether it is simultaneous or sequential, the number of players, the maximum and minimum scores).
  • A State, which describe a specifics point (e.g. a specific board position in chess, a specific set of player cards, public cards and past bets in Poker) within a trajectory.

All possible trajectories in a game are represented as a tree. In this tree, a node is a State and is associated to a specific history of moves for all players. Transitions are actions taken by players (in case of a simultaneous node, the transition is composed of the actions for all players).

Note that in most games, we deal with chance (i.e. any source of randomness) using a an explicit player (the "chance" player, which has id kChancePlayerId). For example, in Poker, the root state would just be the players without any cards, and the first transitions will be chance nodes to deal the cards to the players (in practice once card is dealt per transition).

See spiel.h for the full API description. For example, game.NewInitialState() will return the root State. Then, state.LegalActions() can be used to get the possible legal actions and state.ApplyAction(action) can be used to update state in place to play the given action (use state.Child(action) to create a new state and apply the action to it).

Loading a game

The games are all implemented in C++ in open_spiel/games. Available games names can be listed using RegisteredNames().

A game can be created from its name and its arguments (which usually have defaults). There are 2 ways to create a game:

  • Using the game name and a structured GameParameters object (which, in Python, is a dictionary from argument name to compatible types (int, bool, str or a further dict). e.g. {"players": 3} with LoadGame.
  • Using a string representation such as kuhn_poker(players=3), giving LoadGame(kuhn_poker(players=3)). See open_spiel/game_parameters.cc for the exact syntax.

Creating sequential games from simultaneous games

It is possible to apply generic game transformations (see open_spiel/game_transforms/) such as loading an n-players simultaneous games into an equivalent turn-based game where simultaneous moves are encoded as n turns.

One can use LoadGameAsTurnBased(game), or use the string representation, such as turn_based_simultaneous_game(game=goofspiel(imp_info=True,num_cards=4,points_order=descending)).

Playing a trajectory

Here are for example the Python code to play one trajectory:

import random
import pyspiel
import numpy as np

game = pyspiel.load_game("kuhn_poker")
state = game.new_initial_state()
while not state.is_terminal():
  legal_actions = state.legal_actions()
  if state.is_chance_node():
    # Sample a chance event outcome.
    outcomes_with_probs = state.chance_outcomes()
    action_list, prob_list = zip(*outcomes_with_probs)
    action = np.random.choice(action_list, p=prob_list)
    state.apply_action(action)
  else:
    # The algorithm can pick an action based on an observation (fully observable
    # games) or an information state (information available for that player)
    # We arbitrarily select the first available action as an example.
    action = legal_actions[0]
    state.apply_action(action)

See open_spiel/python/examples/example.py for a more thorough example that covers more use of the core API.

See open_spiel/python/examples/playthrough.py (and open_spiel/python/algorithms/generate_playthrough.py) for an richer example generating a playthrough and printing all available information.

In C++, see open_spiel/examples/example.cc which generates random trajectories.