arelai 0.0.7

A library for creating games and training agents using reinforcement learning.

Arelai

Arelai is a framework for creating games and training agents using reinforcement learning (RL). It includes core components like states, actions, observations, and players that can be customized for specific games.

Usage

Using the framework is simple.

1. Create a Custom Game

   • Subclass State, Action, Observation, and Game to define your game’s rules and mechanics.

2. Define Players

   • Subclass Player to define how players interact with the game.

3. Run the Game

   • Create a Game object with players and an initial state, then call the play() method.

Example

class MyState(State):
    # Define the game state

class MyAction(Action):
    # Define an action structure
    
class MyObservation(Observation):
    # Define an observation structure
    

class MyPlayer(Player):
    def get_all_actions(self, state: MyState) -> list[MyAction]:
        # Return a list of actions

    def select_action(self, list[MyAction], observation: MyObservation, simulate_action_fnc: Callable[[Action], Observation]) -> MyAction:
        # Select an action for the player

class MyGame(Game):
    def log(self):
        # Define anything that should be logged for the round

    def terminal(self) -> bool:
        # Define the game-ending condition

    def observe(self, player: MyPlayer, state: MyState) -> MyObservation:
        # Return the observation for the player

    def all_actions(self, player: MyPlayer, state: MyState) -> list[MyAction]:
        # Return all possible actions for the player

    def apply_action(self, state: State, action: Action) -> State:
        # Apply the action profile and return the new state

    def calculate_reward(self,player: Player, old_state: MyState, action: MyAction, new_state: MyState) -> float:
        # Calculate the reward from the environment for the player