arlbench.core.algorithms.dqn

class arlbench.core.algorithms.dqn.DQN(hpo_config, env, eval_env=None, deterministic_eval=True, eval_eps=0.05, cnn_policy=False, nas_config=None, track_trajectories=False, track_metrics=False)

Bases: Algorithm

JAX-based implementation of Deep Q-Network (DQN).

static get_checkpoint_factory(runner_state, train_result)

Creates a factory dictionary of all possible checkpointing options for DQN.

Parameters:

• runner_state (DQNRunnerState) – Algorithm runner state.

• train_result (DQNTrainingResult | None) – Training result.

Returns:

Dictionary of factory functions containing [opt_state, params, target_params, loss, trajectories].

Return type:

dict[str, Callable]

static get_default_hpo_config()

Returns the default hyperparameter configuration for DQN.

Return type:

Configuration

static get_default_nas_config()

Returns the default NAS configuration for DQN.

Return type:

Configuration

static get_hpo_config_space(seed=None)

Returns the hyperparameter optimization (HPO) configuration space for DQN.

Return type:

ConfigurationSpace

static get_nas_config_space(seed=None)

Returns the neural architecture search (NAS) configuration space for DQN.

Return type:

ConfigurationSpace

init(rng, buffer_state=None, network_params=None, target_params=None, opt_state=None)

Initializes DQN state. Passed parameters are not initialized and included in the final state.

Parameters:

• rng (chex.PRNGKey) – Random generator key.

• buffer_state (PrioritisedTrajectoryBufferState | None, optional) – Buffer state. Defaults to None.

• network_params (FrozenDict | dict | None, optional) – Networks parameters. Defaults to None.

• target_params (FrozenDict | dict | None, optional) – Target network parameters. Defaults to None.

• opt_state (optax.OptState | None, optional) – Optimizer state. Defaults to None.

Returns:

DQN state.

Return type:

DQNState

predict(runner_state, obs, rng, deterministic=True)

Predict action(s) based on the current observation(s).

Parameters:

• runner_state (DQNRunnerState) – Algorithm runner state.

• obs (jnp.ndarray) – Observation(s).

• rng (chex.PRNGKey | None, optional) – Not used in DQN. Random generator key in other algorithms. Defaults to None.

• deterministic (bool) – Return deterministic action. Defaults to True.

Returns:

Action(s).

Return type:

jnp.ndarray

train(runner_state, buffer_state, n_total_timesteps=1000000, n_eval_steps=100, n_eval_episodes=10)

Performs one full training.

Parameters:

• runner_state (DQNRunnerState) – DQN runner state.

• buffer_state (PrioritisedTrajectoryBufferState) – Buffer state.

• n_total_timesteps (int, optional) – Total number of training timesteps. Update steps = n_total_timesteps // n_envs. Defaults to 1000000.

• n_eval_steps (int, optional) – Number of evaluation steps during training. Defaults to 100.

• n_eval_episodes (int, optional) – Number of evaluation episodes per evaluation during training. Defaults to 10.

Returns:

Tuple of DQN algorithm state and training result.

Return type:

DQNTrainReturnT

update(train_state, observations, is_weights, actions, next_observations, rewards, dones)

Update the Q-network.

Parameters:

• train_state (DQNTrainState) – DQN training state.

• observations (jnp.ndarray) – Batch of observations.

• actions (jnp.ndarray) – Batch of actions.

• next_observations (jnp.ndarray) – Batch of next observations.

• rewards (jnp.ndarray) – Batch of rewards.

• dones (jnp.ndarray) – Batch of dones.

Returns:

Tuple of (train_state, loss, td_error, grads).

Return type:

tuple[DQNTrainState, jnp.ndarray, jnp.ndarray, jnp.ndarray]

class arlbench.core.algorithms.dqn.DQNMetrics(loss: jnp.ndarray, grads: jnp.ndarray | tuple, td_error: jnp.ndarray)

Bases: NamedTuple

DQN metrics returned by train function. Consists of (loss, grads, td_error).

grads: Array | tuple

Alias for field number 1

loss: Array

Alias for field number 0

td_error: Array

Alias for field number 2

class arlbench.core.algorithms.dqn.DQNRunnerState(rng: chex.PRNGKey, train_state: DQNTrainState, normalizer_state: RunningStatisticsState, env_state: Any, obs: jnp.ndarray, global_step: int)

Bases: NamedTuple

DQN runner state. Consists of (rng, train_state, env_state, obs, global_step).

env_state: Any

Alias for field number 3

global_step: int

Alias for field number 5

normalizer_state: RunningStatisticsState

Alias for field number 2

obs: Array

Alias for field number 4

rng: Array

Alias for field number 0

train_state: DQNTrainState

Alias for field number 1

class arlbench.core.algorithms.dqn.DQNState(runner_state: DQNRunnerState, buffer_state: PrioritisedTrajectoryBufferState)

Bases: NamedTuple

DQN algorithm state. Consists of (runner_state, buffer_state).

buffer_state: PrioritisedTrajectoryBufferState

Alias for field number 1

runner_state: DQNRunnerState

Alias for field number 0

arlbench.core.algorithms.dqn.DQNTrainReturnT

alias of tuple[DQNState, DQNTrainingResult]

class arlbench.core.algorithms.dqn.DQNTrainingResult(eval_rewards: jnp.ndarray, trajectories: Transition | None, metrics: DQNMetrics | None)

Bases: NamedTuple

DQN training result. Consists of (eval_rewards, trajectories, metrics).

eval_rewards: Array

Alias for field number 0

metrics: DQNMetrics | None

Alias for field number 2

trajectories: Transition | None

Alias for field number 1

Modules

dqn	DQN algorithm.
models	Q-Networks for DQN.

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