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

import torch

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

[docs]class DoubleDQN(DQN): """Double 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) 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, **kwargs): super(DoubleDQN, self).__init__(*args, **kwargs) self.empty_logs() if self.create_model: self._create_model()
[docs] def get_target_q_values( self, next_states: torch.Tensor, rewards: torch.Tensor, dones: torch.Tensor ) -> torch.Tensor: """Get target Q values for the DQN Args: next_states (:obj:`torch.Tensor`): Next states for which target Q-values need to be found rewards (:obj:`list`): Rewards at each timestep for each environment dones (:obj:`list`): Game over status for each environment Returns: target_q_values (:obj:`torch.Tensor`): Target Q values for the DQN """ return ddqn_q_target(self, next_states, rewards, dones)