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

from typing import Tuple

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

[docs]class NoisyDQN(DQN): """Noisy DQN Algorithm 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) noisy_layers (:obj:`tuple` of :obj:`int`): Noisy layers in the Neural Network of the Q-value function 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, noisy_layers: Tuple = (128, 128), **kwargs): super(NoisyDQN, self).__init__(*args, **kwargs) self.noisy_layers = noisy_layers self.noisy = True self.dqn_type = "noisy" self.empty_logs() if self.create_model: self._create_model(noisy_layers=self.noisy_layers)