from typing import Optional
import numpy as np
import torch
from genrl.agents.bandits.contextual.base import DCBAgent
from genrl.agents.bandits.contextual.common import NeuralBanditModel, TransitionDB
from genrl.utils.data_bandits.base import DataBasedBandit
[docs]class BootstrapNeuralAgent(DCBAgent):
"""Bootstraped ensemble agentfor deep contextual bandits.
Args:
bandit (DataBasedBandit): The bandit to solve
init_pulls (int, optional): Number of times to select each action initially.
Defaults to 3.
hidden_dims (List[int], optional): Dimensions of hidden layers of network.
Defaults to [50, 50].
init_lr (float, optional): Initial learning rate. Defaults to 0.1.
lr_decay (float, optional): Decay rate for learning rate. Defaults to 0.5.
lr_reset (bool, optional): Whether to reset learning rate ever train interval.
Defaults to True.
max_grad_norm (float, optional): Maximum norm of gradients for gradient clipping.
Defaults to 0.5.
dropout_p (Optional[float], optional): Probability for dropout. Defaults to None
which implies dropout is not to be used.
eval_with_dropout (bool, optional): Whether or not to use dropout at inference.
Defaults to False.
n (int, optional): Number of models in ensemble. Defaults to 10.
add_prob (float, optional): Probability of adding a transition to a database.
Defaults to 0.95.
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(BootstrapNeuralAgent, self).__init__(bandit, kwargs.get("device", "cpu"))
self.init_pulls = kwargs.get("init_pulls", 3)
self.n = kwargs.get("n", 10)
self.add_prob = kwargs.get("add_prob", 0.95)
self.models = [
NeuralBanditModel(
context_dim=self.context_dim,
hidden_dims=kwargs.get("hidden_dims", [50, 50]),
n_actions=self.n_actions,
init_lr=kwargs.get("init_lr", 0.1),
max_grad_norm=kwargs.get("max_grad_norm", 0.5),
lr_decay=kwargs.get("lr_decay", 0.5),
lr_reset=kwargs.get("lr_reset", True),
dropout_p=kwargs.get("dropout_p", None),
)
.to(torch.float)
.to(self.device)
for _ in range(self.n)
]
self.eval_with_dropout = kwargs.get("eval_with_dropout", False)
self.dbs = [TransitionDB(self.device) for _ in range(self.n)]
self.t = 0
self.update_count = 0
[docs] def select_action(self, context: torch.Tensor) -> int:
"""Select an action based on given context.
Selects an action by computing a forward pass through a
randomly selected network from the ensemble.
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
)
selected_model = self.models[np.random.randint(self.n)]
selected_model.use_dropout = self.eval_with_dropout
results = selected_model(context)
action = torch.argmax(results["pred_rewards"]).to(torch.int)
return action
[docs] def update_db(self, context: torch.Tensor, action: int, reward: int):
"""Updates transition database with given transition
The transition is added to each database with a certain probability.
Args:
context (torch.Tensor): Context recieved
action (int): Action taken
reward (int): Reward recieved
"""
for db in self.dbs:
if np.random.random() < self.add_prob or self.update_count <= 1:
db.add(context, action, reward)
[docs] def update_params(
self,
action: Optional[int] = None,
batch_size: int = 512,
train_epochs: int = 20,
):
"""Update parameters of the agent.
Trains each neural network in the ensemble.
Args:
action (Optional[int], optional): Action to update the parameters for.
Not applicable in this agent. Defaults to None.
batch_size (int, optional): Size of batch to update parameters with.
Defaults to 512
train_epochs (int, optional): Epochs to train neural network for.
Defaults to 20
"""
self.update_count += 1
for i, model in enumerate(self.models):
model.train_model(self.dbs[i], train_epochs, batch_size)