arlbench.core.algorithms.dqn package¶

Submodules¶

arlbench.core.algorithms.dqn.dqn module¶

DQN algorithm.

class arlbench.core.algorithms.dqn.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)[source]¶

Bases: Algorithm

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

static get_checkpoint_factory(runner_state, train_result)[source]¶

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()[source]¶

Returns the default hyperparameter configuration for DQN.

Return type:: Configuration

static get_default_nas_config()[source]¶

Returns the default NAS configuration for DQN.

Return type:: Configuration

static get_hpo_config_space(seed=None)[source]¶

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

Return type:: ConfigurationSpace

static get_nas_config_space(seed=None)[source]¶

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)[source]¶

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

name: str = 'dqn'¶

predict(runner_state, obs, rng, deterministic=True)[source]¶

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)[source]¶

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)[source]¶

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.dqn.DQNMetrics(loss: jnp.ndarray, grads: jnp.ndarray | tuple, td_error: jnp.ndarray)[source]¶

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.dqn.DQNRunnerState(rng: chex.PRNGKey, train_state: DQNTrainState, normalizer_state: RunningStatisticsState, env_state: Any, obs: jnp.ndarray, global_step: int)[source]¶

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.dqn.DQNState(runner_state: DQNRunnerState, buffer_state: PrioritisedTrajectoryBufferState)[source]¶

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

class arlbench.core.algorithms.dqn.dqn.DQNTrainState(step, apply_fn, params, tx, opt_state, target_params=None)[source]¶

Bases: TrainState

DQN training state.

classmethod create_with_opt_state(*, apply_fn, params, target_params, tx, opt_state, **kwargs)[source]¶: Creates a DQN training state with the given optimizer state.

opt_state: optax.OptState¶

replace(**updates)¶: “Returns a new object replacing the specified fields with new values.

target_params: None | chex.Array | dict | FrozenDict = None¶

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

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

class arlbench.core.algorithms.dqn.dqn.Transition(done: jnp.ndarray, action: jnp.ndarray, reward: jnp.ndarray, obs: jnp.ndarray, info: dict)[source]¶

Bases: NamedTuple

DQN Transition. Consists of (done, action, reward, obs, info).

action: Array¶: Alias for field number 1

done: Array¶: Alias for field number 0

info: dict¶: Alias for field number 4

obs: Array¶: Alias for field number 3

reward: Array¶: Alias for field number 2

arlbench.core.algorithms.dqn.models module¶

Q-Networks for DQN.

class arlbench.core.algorithms.dqn.models.CNNQ(action_dim, activation='tanh', hidden_size=512, discrete=True, parent=<flax.linen.module._Sentinel object>, name=None)[source]¶

Bases: Module

A CNN-based Q-Network for DQN.

__call__(x)[source]¶: Applies the CNN to the input.

action_dim: int¶

activation: str = 'tanh'¶

discrete: bool = True¶

hidden_size: int = 512¶

name: Optional[str] = None¶

parent: Union[Type[Module], Scope, Type[_Sentinel], None] = None¶

scope: Optional[Scope] = None¶

setup()[source]¶: Initializes the CNN Q-Network.

class arlbench.core.algorithms.dqn.models.MLPQ(action_dim, activation='tanh', hidden_size=64, discrete=True, parent=<flax.linen.module._Sentinel object>, name=None)[source]¶

Bases: Module

An MLP-based Q-Network for DQN.

__call__(x)[source]¶: Applies the MLP to the input.

action_dim: int¶

activation: str = 'tanh'¶

discrete: bool = True¶

hidden_size: int = 64¶

name: Optional[str] = None¶

parent: Union[Type[Module], Scope, Type[_Sentinel], None] = None¶

scope: Optional[Scope] = None¶

setup()[source]¶: Initializes the MLP Q-Network.

Module contents¶

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)[source]¶

Bases: Algorithm

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

static get_checkpoint_factory(runner_state, train_result)[source]¶

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()[source]¶

Returns the default hyperparameter configuration for DQN.

Return type:: Configuration

static get_default_nas_config()[source]¶

Returns the default NAS configuration for DQN.

Return type:: Configuration

static get_hpo_config_space(seed=None)[source]¶

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

Return type:: ConfigurationSpace

static get_nas_config_space(seed=None)[source]¶

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)[source]¶

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

name: str = 'dqn'¶

predict(runner_state, obs, rng, deterministic=True)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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

ARLBench Documentation

arlbench.core.algorithms.dqn package¶

Submodules¶

arlbench.core.algorithms.dqn.dqn module¶

arlbench.core.algorithms.dqn.models module¶

Module contents¶