colosseum.mdp.frozen_lake.finite_horizon

View Source

 1from typing import Any, Dict, List
 2
 3import gin
 4
 5from colosseum.mdp import EpisodicMDP
 6from colosseum.mdp.frozen_lake.base import FrozenLakeMDP
 7
 8
 9@gin.configurable
10class FrozenLakeEpisodic(EpisodicMDP, FrozenLakeMDP):
11    """
12    The FrozenLake episodic MDP.
13    """
14
15    @staticmethod
16    def sample_parameters(n: int, seed: int = None) -> List[Dict[str, Any]]:
17        return FrozenLakeMDP.sample_mdp_parameters(n, True, seed)

@gin.configurable

class FrozenLakeEpisodic(colosseum.mdp.base_finite.EpisodicMDP, colosseum.mdp.frozen_lake.base.FrozenLakeMDP): View Source

10@gin.configurable
11class FrozenLakeEpisodic(EpisodicMDP, FrozenLakeMDP):
12    """
13    The FrozenLake episodic MDP.
14    """
15
16    @staticmethod
17    def sample_parameters(n: int, seed: int = None) -> List[Dict[str, Any]]:
18        return FrozenLakeMDP.sample_mdp_parameters(n, True, seed)

The FrozenLake episodic MDP.

FrozenLakeEpisodic(H: int = None, **kwargs) View Source

 78    def __init__(self, H: int = None, **kwargs):
 79        super(EpisodicMDP, self).__init__(**kwargs)
 80
 81        # Computing the time horizon
 82        self._input_H = H
 83        self._H = None
 84
 85        # Episodic setting specific caching variables
 86        self._reachable_states = None
 87        self._episodic_graph = dict()
 88        self._continuous_form_episodic_transition_matrix_and_rewards = None
 89        self._episodic_transition_matrix_and_rewards = None
 90        self._optimal_policy_cf = dict()
 91        self._worst_policy_cf = dict()
 92        self._optimal_value_cf = None
 93        self._worst_value_cf = None
 94        self._random_value_cf = None
 95        self._eoar = None
 96        self._woar = None
 97        self._roar = None
 98        self._random_policy_cf = None
 99        self._random_policy = None
100        self._average_optimal_episodic_reward = None
101        self._average_worst_episodic_reward = None
102        self._average_random_episodic_reward = None

Parameters

seed (int): The seed used for sampling rewards and next states.
size (int): The size of the grid.
p_frozen (float): The probability that a tile of the lake is frozen and does not contain a hole.
optimal_return (float): If the rewards are made stochastic, this parameter controls the mean reward for the optimal trajectory. By default, it is set to 1.
suboptimal_return (float): If the rewards are made stochastic, this parameter controls the mean reward for suboptimal trajectories. By default, it is set to 0.1.
is_slippery (bool): If True, the outcome of the action is stochastic due to the frozen tiles being slippery. By default, it is set to True.
goal_r (Union[Tuple, rv_continuous]): The distribution of the highly rewarding state. It can be either passed as a tuple containing Beta parameters or as a rv_continuous object.
default_r (Union[Tuple, rv_continuous]): The distribution of the other states. It can be either passed as a tuple containing Beta parameters or as a rv_continuous object.
make_reward_stochastic (bool): If True, the rewards of the MDP will be stochastic. By default, it is set to False.
reward_variance_multiplier (float): A constant that can be used to increase the variance of the reward distributions without changing their means. The lower the value, the higher the variance. By default, it is set to 1.

@staticmethod

def sample_parameters(n: int, seed: int = None) -> List[Dict[str, Any]]: View Source

16    @staticmethod
17    def sample_parameters(n: int, seed: int = None) -> List[Dict[str, Any]]:
18        return FrozenLakeMDP.sample_mdp_parameters(n, True, seed)

Returns

List[Dict[str, Any]]: n sampled parameters that can be used to construct an MDP in a reasonable amount of time.

Inherited Members

colosseum.mdp.base_finite.EpisodicMDP: is_episodic; H; random_policy_cf; random_policy; parameters; reachable_states; T_cf; R_cf; optimal_value_continuous_form; worst_value_continuous_form; random_value_continuous_form; episodic_optimal_average_reward; episodic_worst_average_reward; episodic_random_average_reward; continuous_form_episodic_transition_matrix_and_rewards; episodic_transition_matrix_and_rewards; get_optimal_policy_continuous_form; get_worst_policy_continuous_form; get_random_policy_continuous_form; get_minimal_regret_for_starting_node; get_optimal_policy_starting_value; get_worst_policy_starting_value; get_random_policy_starting_value; get_episodic_graph; get_grid_representation
colosseum.mdp.frozen_lake.base.FrozenLakeMDP: get_unique_symbols; does_seed_change_MDP_structure; sample_mdp_parameters; get_node_class; get_gin_parameters; n_actions
colosseum.mdp.base.BaseMDP: get_available_hardness_measures; produce_gin_file_from_mdp_parameters; get_gin_config; get_node_labels; get_node_action_labels; hash; instantiate_MDP; T; R; recurrent_nodes_set; communication_class; get_optimal_policy; get_worst_policy; get_value_functions; optimal_value_functions; worst_value_functions; random_value_functions; optimal_transition_probabilities; worst_transition_probabilities; random_transition_probabilities; optimal_markov_chain; worst_markov_chain; random_markov_chain; get_stationary_distribution; optimal_stationary_distribution; worst_stationary_distribution; random_stationary_distribution; optimal_average_rewards; worst_average_rewards; random_average_rewards; get_average_reward; optimal_average_reward; worst_average_reward; random_average_reward; transition_matrix_and_rewards; graph_layout; graph_metrics; diameter; sum_reciprocals_suboptimality_gaps; discounted_value_norm; undiscounted_value_norm; value_norm; measures_of_hardness; summary; hardness_report; get_info_class; get_transition_distributions; get_reward_distribution; sample_reward; get_measure_from_name; action_spec; observation_spec; get_observation; reset; step; random_steps; random_step; get_visitation_counts; reset_visitation_counts; get_value_node_labels
dm_env._environment.Environment: reward_spec; discount_spec; close