from typing import Optional
import numpy as np
import torch
from scipy.stats import invgamma
from genrl.agents.bandits.contextual.base import DCBAgent
from genrl.agents.bandits.contextual.common import TransitionDB
from genrl.utils.data_bandits.base import DataBasedBandit
[docs]class LinearPosteriorAgent(DCBAgent):
"""Deep contextual bandit agent using bayesian regression for posterior inference.
Args:
bandit (DataBasedBandit): The bandit to solve
init_pulls (int, optional): Number of times to select each action initially.
Defaults to 3.
lambda_prior (float, optional): Guassian prior for linear model. Defaults to 0.25.
a0 (float, optional): Inverse gamma prior for noise. Defaults to 6.0.
b0 (float, optional): Inverse gamma prior for noise. Defaults to 6.0.
device (str): Device to use for tensor operations.
"cpu" for cpu or "cuda" for cuda. Defaults to "cpu".
"""
def __init__(self, bandit: DataBasedBandit, **kwargs):
super(LinearPosteriorAgent, self).__init__(bandit, kwargs.get("device", "cpu"))
self.init_pulls = kwargs.get("init_pulls", 3)
self.lambda_prior = kwargs.get("lambda_prior", 0.25)
self.a0 = kwargs.get("a0", 6.0)
self.b0 = kwargs.get("b0", 6.0)
self.mu = torch.zeros(
size=(self.n_actions, self.context_dim + 1),
device=self.device,
dtype=torch.float,
)
self.cov = torch.stack(
[
(1.0 / self.lambda_prior)
* torch.eye(self.context_dim + 1, device=self.device, dtype=torch.float)
for _ in range(self.n_actions)
]
)
self.inv_cov = torch.stack(
[
self.lambda_prior
* torch.eye(self.context_dim + 1, device=self.device, dtype=torch.float)
for _ in range(self.n_actions)
]
)
self.a = self.a0 * torch.ones(
self.n_actions, device=self.device, dtype=torch.float
)
self.b = self.b0 * torch.ones(
self.n_actions, device=self.device, dtype=torch.float
)
self.db = TransitionDB(self.device)
self.t = 0
self.update_count = 0
[docs] def select_action(self, context: torch.Tensor) -> int:
"""Select an action based on given context.
Selecting action with highest predicted reward computed through
betas sampled from posterior.
Args:
context (torch.Tensor): The context vector to select action for.
Returns:
int: The action to take.
"""
self.t += 1
if self.t < self.n_actions * self.init_pulls:
return torch.tensor(
self.t % self.n_actions, device=self.device, dtype=torch.int
)
var = torch.tensor(
[self.b[i] * invgamma.rvs(self.a[i]) for i in range(self.n_actions)],
device=self.device,
dtype=torch.float,
)
try:
beta = (
torch.tensor(
np.stack(
[
np.random.multivariate_normal(
self.mu[i], var[i] * self.cov[i]
)
for i in range(self.n_actions)
]
)
)
.to(self.device)
.to(torch.float)
)
except np.linalg.LinAlgError as e: # noqa F841
beta = (
(
torch.stack(
[
torch.distributions.MultivariateNormal(
torch.zeros(self.context_dim + 1),
torch.eye(self.context_dim + 1),
).sample()
for i in range(self.n_actions)
]
)
)
.to(self.device)
.to(torch.float)
)
values = torch.mv(beta, torch.cat([context.view(-1), torch.ones(1)]))
action = torch.argmax(values).to(torch.int)
return action
[docs] def update_db(self, context: torch.Tensor, action: int, reward: int):
"""Updates transition database with given transition
Args:
context (torch.Tensor): Context recieved
action (int): Action taken
reward (int): Reward recieved
"""
self.db.add(context, action, reward)
[docs] def update_params(
self, action: int, batch_size: int = 512, train_epochs: Optional[int] = None
):
"""Update parameters of the agent.
Updated the posterior over beta though bayesian regression.
Args:
action (int): Action to update the parameters for.
batch_size (int, optional): Size of batch to update parameters with.
Defaults to 512
train_epochs (Optional[int], optional): Epochs to train neural network for.
Not applicable in this agent. Defaults to None
"""
self.update_count += 1
x, y = self.db.get_data_for_action(action, batch_size)
x = torch.cat([x, torch.ones(x.shape[0], 1)], dim=1)
inv_cov = torch.mm(x.T, x) + self.lambda_prior * torch.eye(self.context_dim + 1)
cov = torch.pinverse(inv_cov)
mu = torch.mm(cov, torch.mm(x.T, y))
a = self.a0 + self.t / 2
b = self.b0 + (torch.mm(y.T, y) - torch.mm(mu.T, torch.mm(inv_cov, mu))) / 2
self.mu[action] = mu.squeeze(1)
self.cov[action] = cov
self.inv_cov[action] = inv_cov
self.a[action] = a
self.b[action] = b