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DQN

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DQN(
   env: VecEnv, eval_env: Optional[VecEnv] = None, tag: str = 'default', seed: int = 1,
   device: str = 'cpu', pretraining: bool = False, num_init_steps: int = 2000,
   storage_size: int = 10000, feature_dim: int = 50, batch_size: int = 32,
   lr: float = 0.001, eps: float = 1e-08, hidden_dim: int = 1024, tau: float = 1.0,
   update_every_steps: int = 4, target_update_freq: int = 1000, discount: float = 0.99,
   init_fn: str = 'orthogonal'
)

Deep Q-Network (DQN) agent.

Args

  • env (VecEnv) : Vectorized environments for training.
  • eval_env (VecEnv) : Vectorized environments for evaluation.
  • tag (str) : An experiment tag.
  • seed (int) : Random seed for reproduction.
  • device (str) : Device (cpu, cuda, ...) on which the code should be run.
  • pretraining (bool) : Turn on the pre-training mode.
  • num_init_steps (int) : Number of initial exploration steps.
  • storage_size (int) : The capacity of the storage.
  • feature_dim (int) : Number of features extracted by the encoder.
  • batch_size (int) : Number of samples per batch to load.
  • lr (float) : The learning rate.
  • eps (float) : Term added to the denominator to improve numerical stability.
  • hidden_dim (int) : The size of the hidden layers.
  • tau : The Q-function soft-update rate.
  • update_every_steps (int) : The update frequency of the policy.
  • target_update_freq (int) : The frequency of target Q-network update.
  • discount (float) : Discount factor.
  • init_fn (str) : Parameters initialization method.

Returns

DQN agent instance.

Methods:

.update

source 

.update()

Update the agent and return training metrics such as actor loss, critic_loss, etc.