lit_sb3.algorithms.dqn

Classes

CnnPolicy — Policy class for DQN when using images as input.
DQN — Deep Q-Network (DQN).
DQNPolicy — Policy class with Q-Value Net and target net for DQN.
MultiInputPolicy — Policy class for DQN when using dict observations as input.
QNetwork — Action-Value (Q-Value) network for DQN.

source class CnnPolicy(observation_space: spaces.Space, action_space: spaces.Discrete, lr_schedule: Schedule, net_arch: list[int] | None = None, activation_fn: type[nn.Module] = nn.ReLU, features_extractor_class: type[BaseFeaturesExtractor] = NatureCNN, features_extractor_kwargs: dict[str, Any] | None = None, normalize_images: bool = True, optimizer_class: type[torch.optim.Optimizer] = torch.optim.Adam, optimizer_kwargs: dict[str, Any] | None = None)

Bases : DQNPolicy

Policy class for DQN when using images as input.

Parameters

observation_space : spaces.Space — Observation space
action_space : spaces.Discrete — Action space
lr_schedule : Schedule — Learning rate schedule (could be constant)
net_arch : list[int] | None — The specification of the policy and value networks.
activation_fn : type[nn.Module] — Activation function
features_extractor_class : type[BaseFeaturesExtractor] — Features extractor to use.
normalize_images : bool — Whether to normalize images or not, dividing by 255.0 (True by default)
optimizer_class : type[torch.optim.Optimizer] — The optimizer to use, torch.optim.Adam by default
optimizer_kwargs : dict[str, Any] | None — Additional keyword arguments, excluding the learning rate, to pass to the optimizer

Attributes

device : torch.device — Infer which device this policy lives on by inspecting its parameters.
squash_output : bool — (bool) Getter for squash_output.

source class DQN(policy: str | type[DQNPolicy], learning_rate: float | Schedule = 0.0001, buffer_size: int = 1000000, learning_starts: int = 100, batch_size: int = 32, tau: float = 1.0, gamma: float = 0.99, train_freq: int | tuple[int, str] = 4, gradient_steps: int = 1, replay_buffer_class: type[ReplayBuffer] | None = None, replay_buffer_kwargs: dict[str, Any] | None = None, optimize_memory_usage: bool = False, n_steps: int = 1, target_update_interval: int = 10000, exploration_fraction: float = 0.1, exploration_initial_eps: float = 1.0, exploration_final_eps: float = 0.05, max_grad_norm: float = 10, stats_window_size: int = 100, policy_kwargs: dict[str, Any] | None = None, verbose: int = 0, seed: int | None = None, device: torch.device | str = 'auto', _init_setup_model: bool = True)

Bases : OffPolicyAlgorithm

Deep Q-Network (DQN).

Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236 Default hyperparameters are taken from the Nature paper, except for the optimizer and learning rate that were taken from Stable Baselines defaults.

Parameters

policy : str | type[DQNPolicy] — The policy model to use (MlpPolicy, CnnPolicy, ...)
env — The environment to learn from (if registered in Gym, can be str)
learning_rate : float | Schedule — The learning rate, it can be a function of the current progress remaining (from 1 to 0)
buffer_size : int — size of the replay buffer
learning_starts : int — how many steps of the model to collect transitions for before learning starts
batch_size : int — Minibatch size for each gradient update
tau : float — the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update
gamma : float — the discount factor
train_freq : int | tuple[int, str] — Update the model every train_freq steps. Alternatively pass a tuple of frequency and unit like (5, "step") or (2, "episode").
gradient_steps : int — How many gradient steps to do after each rollout (see train_freq) Set to -1 means to do as many gradient steps as steps done in the environment during the rollout.
replay_buffer_class : type[ReplayBuffer] | None — Replay buffer class to use (for instance HerReplayBuffer). If None, it will be automatically selected.
replay_buffer_kwargs : dict[str, Any] | None — Keyword arguments to pass to the replay buffer on creation.
optimize_memory_usage : bool — Enable a memory efficient variant of the replay buffer at a cost of more complexity. See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
n_steps : int — When n_step > 1, uses n-step return (with the NStepReplayBuffer) when updating the Q-value network.
target_update_interval : int — update the target network every target_update_interval environment steps.
exploration_fraction : float — fraction of entire training period over which the exploration rate is reduced
exploration_initial_eps : float — initial value of random action probability
exploration_final_eps : float — final value of random action probability
max_grad_norm : float — The maximum value for the gradient clipping
stats_window_size : int — Window size for the rollout logging, specifying the number of episodes to average the reported success rate, mean episode length, and mean reward over
tensorboard_log — the log location for tensorboard (if None, no logging)
policy_kwargs : dict[str, Any] | None — additional arguments to be passed to the policy on creation. See :ref:dqn_policies
verbose : int — Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for debug messages
seed : int | None — Seed for the pseudo random generators
device : torch.device | str — Device (cpu, cuda, ...) on which the code should be run. Setting it to auto, the code will be run on the GPU if possible.
_init_setup_model : bool — Whether or not to build the network at the creation of the instance

Attributes

device : torch.device — Return current device (Lightning-aware).
hparams : Union[AttributeDict, MutableMapping] — meth:save_hyperparameters. It is mutable by the user. For the frozen set of initial hyperparameters, use :attr:hparams_initial.
hparams_initial : AttributeDict — meth:save_hyperparameters. These contents are read-only. Manual updates to the saved hyperparameters can instead be performed through :attr:hparams.
example_input_array : Optional[Union[Tensor, tuple, dict]] — meth:forward method. The return type is interpreted as follows:
current_epoch : int — The current epoch in the Trainer, or 0 if not attached.
global_step : int — Total training batches seen across all epochs.
global_rank : int — The index of the current process across all nodes and devices.
local_rank : int — The index of the current process within a single node.
on_gpu : bool — Returns True if this model is currently located on a GPU.
strict_loading : bool — Determines how Lightning loads this model using .load_state_dict(..., strict=model.strict_loading).
logger : Optional[Union[Logger, FabricLogger]] — Reference to the logger object in the Trainer.
loggers : Union[list[Logger], list[FabricLogger]] — Reference to the list of loggers in the Trainer.
device_mesh : Optional['DeviceMesh'] — Strategies like ModelParallelStrategy will create a device mesh that can be accessed in the :meth:~lightning.pytorch.core.hooks.ModelHooks.configure_model hook to parallelize the LightningModule.
env : VecEnv | None — Return the active training env from the trainer, or None.

Methods

configure_optimizers
training_step — Off-policy DQN step: collect env transitions then do gradient updates.
predict — Overrides the base_class predict function to include epsilon-greedy exploration.
learn

source method DQN.configure_optimizers() → OptimizerLRScheduler

source method DQN.training_step(batch: Any, batch_idx: int) → None

Off-policy DQN step: collect env transitions then do gradient updates.

source method DQN.predict(observation: np.ndarray | dict[str, np.ndarray], state: tuple[np.ndarray, ...] | None = None, episode_start: np.ndarray | None = None, deterministic: bool = False) → tuple[np.ndarray, tuple[np.ndarray, ...] | None]

Overrides the base_class predict function to include epsilon-greedy exploration.

Parameters

observation : np.ndarray | dict[str, np.ndarray] — the input observation
state : tuple[np.ndarray, ...] | None — The last states (can be None, used in recurrent policies)
episode_start : np.ndarray | None — The last masks (can be None, used in recurrent policies)
deterministic : bool — Whether or not to return deterministic actions.

Returns

tuple[np.ndarray, tuple[np.ndarray, ...] | None] — the model's action and the next state (used in recurrent policies)

source method DQN.learn(total_timesteps: int, env: GymEnv | str | None = None, callback: MaybeCallback = None, log_every_n_steps: int | None = 4, tb_log_name: str = 'DQN', reset_num_timesteps: bool = True, progress_bar: bool = False, default_root_dir: str | Path | None = None) → SelfDQN

source class DQNPolicy(observation_space: spaces.Space, action_space: spaces.Discrete, lr_schedule: Schedule, net_arch: list[int] | None = None, activation_fn: type[nn.Module] = nn.ReLU, features_extractor_class: type[BaseFeaturesExtractor] = FlattenExtractor, features_extractor_kwargs: dict[str, Any] | None = None, normalize_images: bool = True, optimizer_class: type[torch.optim.Optimizer] = torch.optim.Adam, optimizer_kwargs: dict[str, Any] | None = None)

Bases : BasePolicy

Policy class with Q-Value Net and target net for DQN.

Parameters

observation_space : spaces.Space — Observation space
action_space : spaces.Discrete — Action space
lr_schedule : Schedule — Learning rate schedule (could be constant)
net_arch : list[int] | None — The specification of the policy and value networks.
activation_fn : type[nn.Module] — Activation function
features_extractor_class : type[BaseFeaturesExtractor] — Features extractor to use.
features_extractor_kwargs : dict[str, Any] | None — Keyword arguments to pass to the features extractor.
normalize_images : bool — Whether to normalize images or not, dividing by 255.0 (True by default)
optimizer_class : type[torch.optim.Optimizer] — The optimizer to use, torch.optim.Adam by default
optimizer_kwargs : dict[str, Any] | None — Additional keyword arguments, excluding the learning rate, to pass to the optimizer

Attributes

device : torch.device — Infer which device this policy lives on by inspecting its parameters.
squash_output : bool — (bool) Getter for squash_output.

Methods

make_q_net
forward
set_training_mode — Put the policy in either training or evaluation mode.

source method DQNPolicy.make_q_net() → QNetwork

source method DQNPolicy.forward(obs: PyTorchObs, deterministic: bool = True) → torch.Tensor

source method DQNPolicy.set_training_mode(mode: bool) → None

Put the policy in either training or evaluation mode.

This affects certain modules, such as batch normalisation and dropout.

Parameters

mode : bool — if true, set to training mode, else set to evaluation mode

source class MultiInputPolicy(observation_space: spaces.Dict, action_space: spaces.Discrete, lr_schedule: Schedule, net_arch: list[int] | None = None, activation_fn: type[nn.Module] = nn.ReLU, features_extractor_class: type[BaseFeaturesExtractor] = CombinedExtractor, features_extractor_kwargs: dict[str, Any] | None = None, normalize_images: bool = True, optimizer_class: type[torch.optim.Optimizer] = torch.optim.Adam, optimizer_kwargs: dict[str, Any] | None = None)

Bases : DQNPolicy

Policy class for DQN when using dict observations as input.

Parameters

observation_space : spaces.Dict — Observation space
action_space : spaces.Discrete — Action space
lr_schedule : Schedule — Learning rate schedule (could be constant)
net_arch : list[int] | None — The specification of the policy and value networks.
activation_fn : type[nn.Module] — Activation function
features_extractor_class : type[BaseFeaturesExtractor] — Features extractor to use.
normalize_images : bool — Whether to normalize images or not, dividing by 255.0 (True by default)
optimizer_class : type[torch.optim.Optimizer] — The optimizer to use, torch.optim.Adam by default
optimizer_kwargs : dict[str, Any] | None — Additional keyword arguments, excluding the learning rate, to pass to the optimizer

Attributes

device : torch.device — Infer which device this policy lives on by inspecting its parameters.
squash_output : bool — (bool) Getter for squash_output.

source class QNetwork(observation_space: spaces.Space, action_space: spaces.Discrete, features_extractor: BaseFeaturesExtractor, features_dim: int, net_arch: list[int] | None = None, activation_fn: type[nn.Module] = nn.ReLU, normalize_images: bool = True)

Bases : BasePolicy

Action-Value (Q-Value) network for DQN.

Parameters

observation_space : spaces.Space — Observation space
action_space : spaces.Discrete — Action space
net_arch : list[int] | None — The specification of the policy and value networks.
activation_fn : type[nn.Module] — Activation function
normalize_images : bool — Whether to normalize images or not, dividing by 255.0 (True by default)

Attributes

device : torch.device — Infer which device this policy lives on by inspecting its parameters.
squash_output : bool — (bool) Getter for squash_output.

Methods

forward — Predict the q-values.

source method QNetwork.forward(obs: PyTorchObs) → torch.Tensor

Predict the q-values.

Parameters

obs : PyTorchObs — Observation

Returns

torch.Tensor — The estimated Q-Value for each action.