from typing import List
import numpy as np
import torch
from genrl.agents.bandits.contextual.base import DCBAgent
from genrl.utils.data_bandits.base import DataBasedBandit
[docs]class FixedAgent(DCBAgent):
def __init__(
self, bandit: DataBasedBandit, p: List[float] = None, device: str = "cpu"
):
"""A fixed policy agent for deep contextual bandits.
Args:
bandit (DataBasedBandit): Bandit to solve.
p (List[float], optional): List of probabilities for each action.
Defaults to None which implies action is sampled uniformly.
device (str): Device to use for tensor operations.
"cpu" for cpu or "cuda" for cuda. Defaults to "cpu".
Raises:
ValueError: Raised if length of given probabilities is not
equal to the number of actions available in given bandit.
"""
super(FixedAgent, self).__init__(bandit, device)
if p is None:
p = [1 / self.n_actions for _ in range(self.n_actions)]
elif len(p) != self.n_actions:
raise ValueError(f"p should be of length {self.n_actions}")
self.p = p
self.t = 0
[docs] def select_action(self, context: torch.Tensor) -> int:
"""Select an action based on fixed probabilities.
Args:
context (torch.Tensor): The context vector to select action for.
In this agent, context vector is not considered.
Returns:
int: The action to take.
"""
self.t += 1
return np.random.choice(range(self.n_actions), p=self.p)
[docs] def update_db(self, *args, **kwargs):
pass
[docs] def update_params(self, *args, **kwargs):
pass