Source code for genrl.agents.deep.dqn.prioritized

import collections

import torch

from genrl.agents.deep.dqn.base import DQN
from genrl.agents.deep.dqn.utils import prioritized_q_loss

[docs]class PrioritizedReplayDQN(DQN): """Prioritized Replay DQN Class Paper: Attributes: network (str): The network type of the Q-value function. Supported types: ["cnn", "mlp"] env (Environment): The environment that the agent is supposed to act on batch_size (int): Mini batch size for loading experiences gamma (float): The discount factor for rewards layers (:obj:`tuple` of :obj:`int`): Layers in the Neural Network of the Q-value function lr_value (float): Learning rate for the Q-value function replay_size (int): Capacity of the Replay Buffer buffer_type (str): Choose the type of Buffer: ["push", "prioritized"] max_epsilon (str): Maximum epsilon for exploration min_epsilon (str): Minimum epsilon for exploration epsilon_decay (str): Rate of decay of epsilon (in order to decrease exploration with time) alpha (float): Prioritization constant beta (float): Importance Sampling bias seed (int): Seed for randomness render (bool): Should the env be rendered during training? device (str): Hardware being used for training. Options: ["cuda" -> GPU, "cpu" -> CPU] """ def __init__(self, *args, alpha: float = 0.6, beta: float = 0.4, **kwargs): super(PrioritizedReplayDQN, self).__init__( *args, buffer_type="prioritized", **kwargs ) self.replay_buffer.alpha = alpha self.replay_buffer.beta = beta self.empty_logs() if self.create_model: self._create_model()
[docs] def get_q_loss(self, batch: collections.namedtuple) -> torch.Tensor: """Normal Function to calculate the loss of the Q-function Args: batch (:obj:`collections.namedtuple` of :obj:`torch.Tensor`): Batch of experiences Returns: loss (:obj:`torch.Tensor`): Calculateed loss of the Q-function """ return prioritized_q_loss(self, batch)