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DDPG

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DDPG(
   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 = 1000000, feature_dim: int = 50, batch_size: int = 256,
   lr: float = 0.0001, eps: float = 1e-08, hidden_dim: int = 1024,
   critic_target_tau: float = 0.01, update_every_steps: int = 2, discount: float = 0.99,
   stddev_clip: float = 0.3, init_fn: str = 'orthogonal'
)

Deep Deterministic Policy Gradient (DDPG) 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.
  • critic_target_tau : The critic Q-function soft-update rate.
  • update_every_steps (int) : The agent update frequency.
  • discount (float) : Discount factor.
  • stddev_clip (float) : The exploration std clip range.
  • init_fn (str) : Parameters initialization method.

Returns

DDPG agent instance.

Methods:

.update

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.update()

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

.update_critic

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.update_critic(
   obs: th.Tensor, actions: th.Tensor, rewards: th.Tensor, terminateds: th.Tensor,
   truncateds: th.Tensor, next_obs: th.Tensor
)

Update the critic network.

Args

  • obs (th.Tensor) : Observations.
  • actions (th.Tensor) : Actions.
  • rewards (th.Tensor) : Rewards.
  • terminateds (th.Tensor) : Terminateds.
  • truncateds (th.Tensor) : Truncateds.
  • next_obs (th.Tensor) : Next observations.

Returns

None.

.update_actor

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.update_actor(
   obs: th.Tensor
)

Update the actor network.

Args

  • obs (th.Tensor) : Observations.

Returns

None.