mlpack::rl::OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType > Class Template Reference

Forward declaration of OneStepQLearningWorker. More...

#include <async_learning.hpp>

Public Types
using	StateType = typename EnvironmentType::State
using	ActionType = typename EnvironmentType::Action
using	TransitionType = std::tuple< StateType, ActionType, double, StateType >

Public Member Functions
	OneStepQLearningWorker (const UpdaterType &updater, const EnvironmentType &environment, const TrainingConfig &config, bool deterministic)
	Construct one step Q-Learning worker with the given parameters and environment. More...
	OneStepQLearningWorker (const OneStepQLearningWorker &other)
	Copy another OneStepQLearningWorker. More...
	OneStepQLearningWorker (OneStepQLearningWorker &&other)
	Take ownership of another OneStepQLearningWorker. More...
OneStepQLearningWorker &	operator= (const OneStepQLearningWorker &other)
	Copy another OneStepQLearningWorker. More...
OneStepQLearningWorker &	operator= (OneStepQLearningWorker &&other)
	Take ownership of another OneStepQLearningWorker. More...
	~OneStepQLearningWorker ()
	Clean memory.
void	Initialize (NetworkType &learningNetwork)
	Initialize the worker. More...
bool	Step (NetworkType &learningNetwork, NetworkType &targetNetwork, size_t &totalSteps, PolicyType &policy, double &totalReward)
	The agent will execute one step. More...

Detailed Description

template<typename EnvironmentType, typename NetworkType, typename UpdaterType, typename PolicyType>
class mlpack::rl::OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType >

Forward declaration of OneStepQLearningWorker.

One step Q-Learning worker.

Template Parameters

EnvironmentType	The type of the reinforcement learning task.
NetworkType	The type of the network model.
UpdaterType	The type of the optimizer.
PolicyType	The type of the behavior policy.
EnvironmentType	The type of the reinforcement learning task.
NetworkType	The type of the network model.
UpdaterType	The type of the optimizer.
PolicyType	The type of the behavior policy. *

Constructor & Destructor Documentation

OneStepQLearningWorker() [1/3]

template<typename EnvironmentType , typename NetworkType , typename UpdaterType , typename PolicyType >

mlpack::rl::OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType >::OneStepQLearningWorker ( const UpdaterType &  updater, const EnvironmentType &  environment, const TrainingConfig &  config, bool  deterministic  )

inline

Construct one step Q-Learning worker with the given parameters and environment.

Parameters

updater	The optimizer.
environment	The reinforcement learning task.
config	Hyper-parameters.
deterministic	Whether it should be deterministic.

OneStepQLearningWorker() [2/3]

template<typename EnvironmentType , typename NetworkType , typename UpdaterType , typename PolicyType >

mlpack::rl::OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType >::OneStepQLearningWorker ( const OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType > &  other )

inline

Copy another OneStepQLearningWorker.

Parameters

other

OneStepQLearningWorker to copy.

OneStepQLearningWorker() [3/3]

template<typename EnvironmentType , typename NetworkType , typename UpdaterType , typename PolicyType >

mlpack::rl::OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType >::OneStepQLearningWorker ( OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType > &&  other )

inline

Take ownership of another OneStepQLearningWorker.

Parameters

other

OneStepQLearningWorker to take ownership of.

Member Function Documentation

Initialize()

template<typename EnvironmentType , typename NetworkType , typename UpdaterType , typename PolicyType >

void mlpack::rl::OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType >::Initialize ( NetworkType &  learningNetwork )

inline

Initialize the worker.

Parameters

learningNetwork

The shared network.

operator=() [1/2]

template<typename EnvironmentType , typename NetworkType , typename UpdaterType , typename PolicyType >

OneStepQLearningWorker& mlpack::rl::OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType >::operator= ( const OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType > &  other )

inline

Copy another OneStepQLearningWorker.

Parameters

other

OneStepQLearningWorker to copy.

operator=() [2/2]

template<typename EnvironmentType , typename NetworkType , typename UpdaterType , typename PolicyType >

OneStepQLearningWorker& mlpack::rl::OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType >::operator= ( OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType > &&  other )

inline

Take ownership of another OneStepQLearningWorker.

Parameters

other

OneStepQLearningWorker to take ownership of.

Step()

template<typename EnvironmentType , typename NetworkType , typename UpdaterType , typename PolicyType >

bool mlpack::rl::OneStepQLearningWorker< EnvironmentType, NetworkType, UpdaterType, PolicyType >::Step ( NetworkType &  learningNetwork, NetworkType &  targetNetwork, size_t &  totalSteps, PolicyType &  policy, double &  totalReward  )

inline

The agent will execute one step.

Parameters

learningNetwork	The shared learning network.
targetNetwork	The shared target network.
totalSteps	The shared counter for total steps.
policy	The shared behavior policy.
totalReward	This will be the episode return if the episode ends after this step. Otherwise this is invalid.

Returns

Indicate whether current episode ends after this step.

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The documentation for this class was generated from the following files:

• src/mlpack/methods/reinforcement_learning/async_learning.hpp
• src/mlpack/methods/reinforcement_learning/worker/one_step_q_learning_worker.hpp