GT - Basics

Ver. 1.2.0 (2022-11-01)

This module provides model classes for tasks related to cooperative Game Theory.

class mlpro.gt.models.GameBoard(p_mode=0, p_latency: timedelta | None = None, p_fct_strans: FctSTrans | None = None, p_fct_reward: FctReward | None = None, p_fct_success: FctSuccess | None = None, p_fct_broken: FctBroken | None = None, p_mujoco_file=None, p_frame_skip: int = 1, p_state_mapping=None, p_action_mapping=None, p_camera_conf: tuple = (None, None, None), p_visualize: bool = False, p_logging=True)

Bases: Environment

Model class for a game theoretical game board. See super class for more information.

C_TYPE = 'Game Board'

C_REWARD_TYPE = 1

class mlpro.gt.models.PGameBoard(p_mode=0, p_latency: timedelta | None = None, p_fct_strans: FctSTrans | None = None, p_fct_reward: FctReward | None = None, p_fct_success: FctSuccess | None = None, p_fct_broken: FctBroken | None = None, p_mujoco_file=None, p_frame_skip: int = 1, p_state_mapping=None, p_action_mapping=None, p_camera_conf: tuple = (None, None, None), p_visualize: bool = False, p_logging=True)

Bases: GameBoard

Model class for a potential game theoretical game board. See super class for more information.

C_TYPE = 'Potential Game Board'

compute_potential()

Computes (weighted) potential level of the game board.

class mlpro.gt.models.Player(p_policy: Policy, p_envmodel: EnvModel | None = None, p_em_acc_thsld=0.9, p_action_planner: ActionPlanner | None = None, p_predicting_horizon=0, p_controlling_horizon=0, p_planning_width=0, p_name='', p_ada=True, p_visualize: bool = True, p_logging=True, **p_mb_training_param)

Bases: Agent

This class implements a game theoretical player model. See super class for more information.

C_TYPE = 'Player'

class mlpro.gt.models.MultiPlayer(p_name: str = '', p_ada: bool = True, p_visualize: bool = False, p_logging=True)

Bases: MultiAgent

This class implements a game theoretical model for a team of players. See super class for more information.

C_TYPE = 'Multi-Player'

add_player(p_player: Player, p_weight=1.0) → None

class mlpro.gt.models.Game(p_mode=0, p_ada: bool = True, p_cycle_limit=0, p_visualize: bool = True, p_logging=True)

Bases: RLScenario

This class implements a game consisting of a game board and a (multi-)player. See super class for more information.

C_TYPE = 'Game'

class mlpro.gt.models.GTTraining(**p_kwargs)

Bases: RLTraining

This class implements a standardized episodical training process. See super class for more information.

Parameters:

• p_game_cls – Name of GT game class, compatible to/inherited from class Game.

• p_cycle_limit (int) – Maximum number of training cycles (0=no limit). Default = 0.

• p_cycles_per_epi_limit (int) – Optional limit of cycles per episode (0=no limit, -1=get environment limit). Default = -1.

• p_adaptation_limit (int) – Maximum number of adaptations (0=no limit). Default = 0.

• p_stagnation_limit (int) – Optional limit of consecutive evaluations without training progress. Default = 0.

• p_eval_frequency (int) – Optional evaluation frequency (0=no evaluation). Default = 0.

• p_eval_grp_size (int) – Number of evaluation episodes (eval group). Default = 0.

• p_hpt (HyperParamTuner) – Optional hyperparameter tuner (see class mlpro.bf.ml.HyperParamTuner). Default = None.

• p_hpt_trials (int) – Optional number of hyperparameter tuning trials. Default = 0. Must be > 0 if p_hpt is supplied.

• p_path (str) – Optional destination path to store training data. Default = None.

• p_collect_states (bool) – If True, the environment states will be collected. Default = True.

• p_collect_actions (bool) – If True, the agent actions will be collected. Default = True.

• p_collect_rewards (bool) – If True, the environment reward will be collected. Default = True.

• p_collect_training (bool) – If True, global training data will be collected. Default = True.

• p_visualize (bool) – Boolean switch for env/agent visualisation. Default = False.

• p_logging – Log level (see constants of class mlpro.bf.various.Log). Default = Log.C_LOG_WE.

C_NAME = 'GT'