arlbench.core.algorithms¶

RL algorithms.

class arlbench.core.algorithms.Algorithm(hpo_config, nas_config, env, eval_env=None, deterministic_eval=True, track_trajectories=False, track_metrics=False)[source]¶

Bases: ABC

Abstract base class for a reinforcement learning algorithm. Contains basic functionality that is shared among different algorithm implementations.

property action_type: tuple[int, bool]¶

The size and type of actions of the algorithm/environment.

Returns:: Tuple of (action_size, discrete). action_size is the number of possible actions and discrete defines whether the action space is discrete or not.
Return type:: tuple[int, bool]

eval(runner_state, num_eval_episodes)[source]¶

Evaluate the algorithm.

Parameters:

runner_state (Any) – Algorithm runner state.
num_eval_episodes (int) – Number of evaluation episodes.

Returns:

Cumulative reward per evaluation episodes. Shape: (n_eval_episodes,).

Return type:

jnp.ndarray

abstract static get_checkpoint_factory(runner_state, train_result)[source]¶

Creates a factory dictionary of all posssible checkpointing options for Algorithm.

Parameters:

runner_state (Any) – Algorithm runner state.
train_result (Any) – Training result.

Returns:

Dictionary of factory functions.

Return type:

dict[str, Callable]

abstract static get_default_hpo_config()[source]¶

Returns the default hyperparameter configuration of the agent.

Returns:: Default hyperparameter configuration.
Return type:: Configuration

abstract static get_default_nas_config()[source]¶

Returns the default neural architecture configuration of the agent.

Returns:: Default neural architecture configuration.
Return type:: Configuration

abstract static get_hpo_config_space(seed=None)[source]¶

Returns the hyperparameter configuration space of the algorithm.

Parameters:: seed (int | None, optional) – Random generator seed that is used to sample configurations. Defaults to None.
Returns:: Hyperparameter configuration space of the algorithm.
Return type:: ConfigurationSpace

abstract static get_nas_config_space(seed=None)[source]¶

Returns the neural architecture configuration space of the algorithm.

Parameters:: seed (int | None, optional) – Random generator seed that is used to sample configurations. Defaults to None.
Returns:: Neural architecture configuration space of the algorithm.
Return type:: ConfigurationSpace

abstract init(rng)[source]¶

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

Parameters:: rng (chex.PRNGKey) – Random generator key.
Returns:: Algorithm state.
Return type:: Any

abstract predict(runner_state, obs, rng=None, deterministic=True)[source]¶

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

Parameters:

runner_state (Any) – Algorithm runner state.
obs (jnp.ndarray) – Observation(s).
rng (chex.PRNGKey | None, optional) – Random generator key. Defaults to None.
deterministic (bool) – Return deterministic action. Defaults to True.

Returns:

Action(s).

Return type:

jnp.ndarray

abstract train(runner_state, buffer_state, n_total_timesteps=1000000, n_eval_steps=100, n_eval_episodes=10)[source]¶

Performs one iteration of training.

Parameters:

runner_state (Any) – Algorithm runner state.
buffer_state (Any) – Algorithm 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.
n_eval_episodes (int, optional) – Number of evaluation episodes per evaluation during training.

Returns:

(algorithm_state, training_result).

Return type:

tuple[Any, Any]

update_hpo_config(hpo_config)[source]¶

Update the hyperparameter configuration of the algorithm.

Parameters:: hpo_config (Configuration) – Hyperparameter configuration.

class arlbench.core.algorithms.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

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.PPO(hpo_config, env, eval_env=None, cnn_policy=False, nas_config=None, deterministic_eval=True, track_trajectories=False, track_metrics=False)[source]¶

Bases: Algorithm

JAX-based implementation of Proximal Policy Optimization (PPO).

static get_checkpoint_factory(runner_state, train_result)[source]¶

Creates a factory dictionary of all posssible checkpointing options for PPO.

Parameters:

runner_state (PPORunnerState) – Algorithm runner state.
train_result (PPOTrainingResult | None) – Training result.

Returns:

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

Return type:

dict[str, Callable]

static get_default_hpo_config()[source]¶

Returns the default hyperparameter configuration for PPO.

Return type:: Configuration

static get_default_nas_config()[source]¶

Returns the default neural architecture search configuration for PPO.

Return type:: Configuration

static get_hpo_config_space(seed=None)[source]¶

Returns the hyperparameter configuration space for PPO.

Return type:: ConfigurationSpace

static get_nas_config_space(seed=None)[source]¶

Returns the neural architecture search configuration space for PPO.

Return type:: ConfigurationSpace

init(rng, network_params=None, opt_state=None)[source]¶

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

Parameters:

rng (chex.PRNGKey) – Random generator key.
network_params (FrozenDict | dict | None, optional) – Network parameters. Defaults to None.
opt_state (optax.OptState | None, optional) – Optimizer state. Defaults to None.

Returns:

PPO state.

Return type:

PPOState

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

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

Parameters:

runner_state (PPORunnerState) – Algorithm runner state.
obs (jnp.ndarray) – Observation(s).
rng (chex.PRNGKey | None, optional) – Random generator key. Defaults to None.
deterministic (bool) – Return deterministic action. Defaults to True.

Returns:

Action(s).

Return type:

jnp.ndarray

train(runner_state, _, n_total_timesteps=1000000, n_eval_steps=10, n_eval_episodes=10)[source]¶

Performs one iteration of training.

Parameters:

runner_state (PPORunnerState) – PPO runner state.
_ (None) – Unused parameter (buffer_state in other algorithms).
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 PPO algorithm state and training result.

Return type:

PPOTrainReturnT

class arlbench.core.algorithms.SAC(hpo_config, env, eval_env=None, deterministic_eval=True, cnn_policy=False, nas_config=None, track_metrics=False, track_trajectories=False)[source]¶

Bases: Algorithm

JAX-based implementation of Soft-Actor-Critic (SAC).

static get_checkpoint_factory(runner_state, train_result)[source]¶

Creates a factory dictionary of all posssible checkpointing options for SAC.

Parameters:

runner_state (SACRunnerState) – Algorithm runner state.
train_result (SACTrainingResult | None) – Training result.

Returns:

Dictionary of factory functions containing:

actor_opt_state
critic_opt_state
alpha_opt_state
actor_network_params
critic_network_params
critic_target_params
alpha_network_params
actor_loss
critic_loss
alpha_loss
trajectories

Return type:

dict[str, Callable]

static get_default_hpo_config()[source]¶

Returns the default HPO configuration for SAC.

Return type:: Configuration

static get_default_nas_config()[source]¶

Returns the default NAS configuration for SAC.

Return type:: Configuration

static get_hpo_config_space(seed=None)[source]¶

Returns the hyperparameter configuration space for SAC.

Return type:: ConfigurationSpace

static get_nas_config_space(seed=None)[source]¶

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

Return type:: ConfigurationSpace

init(rng, buffer_state=None, actor_network_params=None, critic_network_params=None, critic_target_params=None, alpha_network_params=None, actor_opt_state=None, critic_opt_state=None, alpha_opt_state=None)[source]¶

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

Parameters:

actor_network_params (FrozenDict | dict | None, optional) – Actor network parameters. Defaults to None.
critic_network_params (FrozenDict | dict | None, optional) – Critic network parameters. Defaults to None.
critic_target_params (FrozenDict | dict | None, optional) – Critic target network parameters. Defaults to None.
alpha_network_params (FrozenDict | dict | None, optional) – Alpha network parameters. Defaults to None.
actor_opt_state (optax.OptState | None, optional) – Actor optimizer state. Defaults to None.
critic_opt_state (optax.OptState | None, optional) – Critic optimizer state. Defaults to None.
alpha_opt_state (optax.OptState | None, optional) – Alpha optimizer state. Defaults to None.

Returns:

SAC state.

Return type:

SACState

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

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

Parameters:

runner_state (SACRunnerState) – Algorithm runner state.
obs (jnp.ndarray) – Observation(s).
rng (chex.PRNGKey | None, optional) – Not used in DQN. Random generator key in other algorithmsDefaults to None.
deterministic (bool) – Not used in DQN. Return deterministic action in other algorithm. 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 (SACTrainReturnT) – SAC runner state.
_ (None) – Unused parameter (buffer_state in other algorithms).
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.
n_eval_episodes (int, optional) – Number of evaluation episodes per evaluation during training.

Returns:

Tuple of PPO algorithm state and training result.

Return type:

SACTrainReturnT

update_actor(actor_train_state, critic_train_state, alpha_train_state, experience, is_weights, rng)[source]¶

Updates the actor network parameters.

Parameters:

actor_train_state (SACTrainState) – Actor train state.
critic_train_state (SACTrainState) – Critic train state.
alpha_train_state (SACTrainState) – Alpha train state.
experience (TimeStep) – Experience (batch of TimeSteps).
is_weights (jnp.ndarray) – Whether to use weights for PER or not.
rng (chex.PRNGKey) – Random number generator key.

Returns:

_description_

Return type:

tuple[SACTrainState, jnp.ndarray, jnp.ndarray, FrozenDict, chex.PRNGKey]

update_alpha(alpha_train_state, entropy)[source]¶

Update alpha network parameters.

Parameters:

alpha_train_state (SACTrainState) – Alpha training state.
entropy (jnp.ndarray) – Entropy values.

Returns:

Updated trainingi state and metrics.

Return type:

tuple[SACTrainState, jnp.ndarray]

update_critic(actor_train_state, critic_train_state, alpha_train_state, experience, is_weights, rng)[source]¶

Updates the critic network parameters.

Parameters:

actor_train_state (SACTrainState) – Actor train state.
critic_train_state (SACTrainState) – Critic train state.
alpha_train_state (SACTrainState) – Alpha train state.
experience (Transition) – Experience (batch of transitions).
rng (chex.PRNGKey) – Random number generator key.

Returns:

Updated training state and metrics.

Return type:

tuple[SACTrainState, jnp.ndarray, jnp.ndarray, FrozenDict, chex.PRNGKey]

Modules

`algorithm`	Abstract base class for a reinforcement learning algorithm.
`buffers`	Replay buffers.
`common`	Common data structures for algorithms.
`dqn`
`ppo`
`prioritised_item_buffer`	Prioritised replay buffer.
`sac`

arlbench.core

arlbench.core.algorithms.algorithm

ARLBench Documentation

arlbench.core.algorithms¶