Module pearl.utils.instantiations.environments.reward_is_equal_to_ten_times_action_contextual_bandit_environment

Expand source code 
from typing import Optional, Tuple

from pearl.api.action import Action
from pearl.api.action_space import ActionSpace
from pearl.api.observation import Observation
from pearl.api.reward import Value
from pearl.utils.instantiations.environments.contextual_bandit_environment import (
    ContextualBanditEnvironment,
)


class RewardIsEqualToTenTimesActionContextualBanditEnvironment(
    ContextualBanditEnvironment
):
    """
    A example implementation of a contextual bandit environment.
    """

    def __init__(self, action_space: ActionSpace) -> None:
        self._action_space = action_space

    @property
    def action_space(self) -> ActionSpace:
        return self._action_space

    def reset(self, seed: Optional[int] = None) -> Tuple[Observation, ActionSpace]:
        # Function returning the context and the available action space
        # Here, we use no context (None), but we could return varied implementations.
        return None, self.action_space

    def get_reward(self, action: Action) -> Value:
        # Here goes the code for computing the reward given an action on the current state
        # In this example, the reward is 10 times the digit representing the action.
        return (action * 10).item()

    def __str__(self) -> str:
        return "Bandit with reward = 10 * action index"

Classes

class RewardIsEqualToTenTimesActionContextualBanditEnvironment (action_space: ActionSpace)

A example implementation of a contextual bandit environment.

Expand source code 
class RewardIsEqualToTenTimesActionContextualBanditEnvironment(
    ContextualBanditEnvironment
):
    """
    A example implementation of a contextual bandit environment.
    """

    def __init__(self, action_space: ActionSpace) -> None:
        self._action_space = action_space

    @property
    def action_space(self) -> ActionSpace:
        return self._action_space

    def reset(self, seed: Optional[int] = None) -> Tuple[Observation, ActionSpace]:
        # Function returning the context and the available action space
        # Here, we use no context (None), but we could return varied implementations.
        return None, self.action_space

    def get_reward(self, action: Action) -> Value:
        # Here goes the code for computing the reward given an action on the current state
        # In this example, the reward is 10 times the digit representing the action.
        return (action * 10).item()

    def __str__(self) -> str:
        return "Bandit with reward = 10 * action index"

Ancestors

Methods

def get_reward(self, action: torch.Tensor) ‑> object
Expand source code 
def get_reward(self, action: Action) -> Value:
    # Here goes the code for computing the reward given an action on the current state
    # In this example, the reward is 10 times the digit representing the action.
    return (action * 10).item()

Inherited members