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ActionExtractor

class ActionExtractor(BaseExtractorStrategy):
A strategy for extracting RLCard actions from text.

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init

def __init__(self, action_pattern: str = '<Action>\\s*(.+)'):
Initialize the action extractor with a regex pattern. Parameters:
  • action_pattern (str): The regex pattern to extract actions. (default: :obj:"<Action>\\s*(.+)").

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RLCardsEnv

class RLCardsEnv(MultiStepEnv):
A base environment for RLCard games. This environment implements a wrapper around RLCard environments for reinforcement learning with LLMs. It handles the conversion between RLCard states and actions and the CAMEL environment interface.

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init

def __init__(
    self,
    game_name: str,
    extractor: Optional[BaseExtractor] = None,
    max_steps: Optional[int] = None,
    num_players: int = 2,
    **kwargs
):
Initialize the RLCard environment. Parameters:
  • game_name (str): The name of the RLCard game to play.
  • extractor (Optional[BaseExtractor]): Extractor to process LLM responses. If None, a default extractor with ActionExtractor will be used. (default: :obj:None)
  • max_steps (Optional[int]): Maximum steps per episode. (default: :obj:None)
  • num_players (int): Number of players in the game. (default: :obj:2) **kwargs: Additional environment parameters.

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_get_initial_state

def _get_initial_state(self):
Returns: Dict[str, Any]: A dictionary containing the initial state with game state, player info, and game status flags.

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_get_next_observation

def _get_next_observation(self):
Returns: Observation: An Observation object containing the game state description.

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_get_terminal_observation

def _get_terminal_observation(self):
Returns: Observation: An Observation object containing the final game state description.

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_is_done

def _is_done(self):
Returns: bool: True if the game is over, False otherwise.

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_convert_to_rlcard_action

def _convert_to_rlcard_action(self, action_str: str):
Convert a string action to the format expected by RLCard. This method must be implemented by subclasses to handle the specific action format of each game. Parameters:
  • action_str (str): The string representation of the action.
Returns: Any: The action in the format expected by the RLCard environment.

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_format_state_for_observation

def _format_state_for_observation(self, state: Dict[str, Any]):
Format the RLCard state for human-readable observation. This method must be implemented by subclasses to create a human-readable representation of the game state. Parameters:
  • state (Dict[str, Any]): The RLCard state dictionary.
Returns: str: A human-readable representation of the state. 
def _format_legal_actions(self, legal_actions: List[Any]):
Format the legal actions for human-readable observation. This method must be implemented by subclasses to create a human-readable representation of the legal actions. Parameters:
  • legal_actions (List[Any]): The list of legal actions.
Returns: str: A human-readable representation of the legal actions.

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BlackjackEnv

class BlackjackEnv(RLCardsEnv):
A Blackjack environment for reinforcement learning with LLMs. This environment implements a standard Blackjack game where the LLM agent plays against a dealer.

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init

def __init__(
    self,
    extractor: Optional[BaseExtractor] = None,
    max_steps: Optional[int] = None,
    **kwargs
):
Initialize the Blackjack environment. Parameters:
  • extractor (Optional[BaseExtractor]): Extractor to process LLM responses. If None, a default extractor will be used. (default: :obj:None)
  • max_steps (Optional[int]): Maximum steps per episode. (default: :obj:None) **kwargs: Additional environment parameters.

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_convert_to_rlcard_action

def _convert_to_rlcard_action(self, action_str: str):
Convert a string action to the format expected by RLCard Blackjack. Parameters:
  • action_str (str): The string representation of the action. Expected to be ‘hit’ or ‘stand’.
Returns: int: 0 for ‘hit’, 1 for ‘stand’.

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_format_state_for_observation

def _format_state_for_observation(self, state: Dict[str, Any]):
Format the Blackjack state for human-readable observation. Parameters:
  • state (Dict[str, Any]): The RLCard state dictionary.
Returns: str: A human-readable representation of the state. 
def _format_legal_actions(self, legal_actions: List[int]):
Format the legal actions for Blackjack. Parameters:
  • legal_actions (List[int]): The list of legal actions.
Returns: str: A human-readable representation of the legal actions.

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_format_cards

def _format_cards(self, cards: List[str]):
Format a list of cards for display. Parameters:
  • cards (List[str]): List of card strings.
Returns: str: Formatted card string.

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_calculate_hand_value

def _calculate_hand_value(self, cards: List[str]):
Calculate the value of a hand in Blackjack. Parameters:
  • cards (List[str]): List of card strings.
Returns: int: The value of the hand.

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LeducHoldemEnv

class LeducHoldemEnv(RLCardsEnv):
A Leduc Hold’em environment for reinforcement learning with LLMs. This environment implements a Leduc Hold’em poker game where the LLM agent plays against one or more opponents.

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init

def __init__(
    self,
    extractor: Optional[BaseExtractor] = None,
    max_steps: Optional[int] = None,
    num_players: int = 2,
    **kwargs
):
Initialize the Leduc Hold’em environment. Parameters:
  • extractor (Optional[BaseExtractor]): Extractor to process LLM responses. If None, a default extractor will be used. (default: :obj:None)
  • max_steps (Optional[int]): Maximum steps per episode. (default: :obj:None)
  • num_players (int): Number of players in the game. (default: :obj:2) **kwargs: Additional environment parameters.

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_convert_to_rlcard_action

def _convert_to_rlcard_action(self, action_str: str):
Convert a string action to the format expected by RLCard Leduc Hold’em. Parameters:
  • action_str (str): The string representation of the action. Expected to be ‘fold’, ‘check’, ‘call’, or ‘raise’.
Returns: int: 0 for ‘fold’, 1 for ‘check/call’, 2 for ‘raise’.

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_format_state_for_observation

def _format_state_for_observation(self, state: Dict[str, Any]):
Format the Leduc Hold’em state for human-readable observation. Parameters:
  • state (Dict[str, Any]): The RLCard state dictionary.
Returns: str: A human-readable representation of the state. 
def _format_legal_actions(self, legal_actions: List[int]):
Format the legal actions for Leduc Hold’em. Parameters:
  • legal_actions (List[int]): The list of legal actions.
Returns: str: A human-readable representation of the legal actions.

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DoudizhuEnv

class DoudizhuEnv(RLCardsEnv):
A Doudizhu environment for reinforcement learning with LLMs. This environment implements a standard Doudizhu game where the LLM agent plays against two AI opponents.

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init

def __init__(
    self,
    extractor: Optional[BaseExtractor] = None,
    max_steps: Optional[int] = None,
    **kwargs
):
Initialize the Doudizhu environment. Parameters:
  • extractor (Optional[BaseExtractor]): Extractor to process LLM responses. If None, a default extractor will be used. (default: :obj:None)
  • max_steps (Optional[int]): Maximum steps per episode. (default: :obj:None) **kwargs: Additional environment parameters.

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_convert_to_rlcard_action

def _convert_to_rlcard_action(self, action_str: str):
Convert a string action to the format expected by RLCard Doudizhu. Parameters:
  • action_str (str): The string representation of the action. Expected to be a card combination or ‘pass’.
Returns: str: The action string in the format expected by RLCard.

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_format_state_for_observation

def _format_state_for_observation(self, state: Dict[str, Any]):
Format the Doudizhu state for human-readable observation. Parameters:
  • state (Dict[str, Any]): The RLCard state dictionary.
Returns: str: A human-readable representation of the state. 
def _format_legal_actions(self, legal_actions: List[str]):
Format the legal actions for Doudizhu. Parameters:
  • legal_actions (List[str]): The list of legal actions.
Returns: str: A human-readable representation of the legal actions.

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_format_cards

def _format_cards(self, cards: List[str]):
Format a list of cards for display. Parameters:
  • cards (List[str]): List of card strings.
Returns: str: Formatted card string.
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