mlpack::rl::RandomReplay< EnvironmentType > Class Template Reference

Implementation of random experience replay. More...

#include <random_replay.hpp>

Classes
struct	Transition

Public Types
using	ActionType = typename EnvironmentType::Action
	Convenient typedef for action.
using	StateType = typename EnvironmentType::State
	Convenient typedef for state.

Public Member Functions
	RandomReplay (const size_t batchSize, const size_t capacity, const size_t nSteps=1, const size_t dimension=StateType::dimension)
	Construct an instance of random experience replay class. More...
void	Store (StateType state, ActionType action, double reward, StateType nextState, bool isEnd, const double &discount)
	Store the given experience. More...
void	GetNStepInfo (double &reward, StateType &nextState, bool &isEnd, const double &discount)
	Get the reward, next state and terminal boolean for nth step. More...
void	Sample (arma::mat &sampledStates, std::vector< ActionType > &sampledActions, arma::rowvec &sampledRewards, arma::mat &sampledNextStates, arma::irowvec &isTerminal)
	Sample some experiences. More...
const size_t &	Size ()
	Get the number of transitions in the memory. More...
void	Update (arma::mat, std::vector< ActionType >, arma::mat, arma::mat &)
	Update the priorities of transitions and Update the gradients. More...
const size_t &	NSteps () const
	Get the number of steps for n-step agent.

Detailed Description

template<typename EnvironmentType>
class mlpack::rl::RandomReplay< EnvironmentType >

Implementation of random experience replay.

At each time step, interactions between the agent and the environment will be saved to a memory buffer. When necessary, we can simply sample previous experiences from the buffer to train the agent. Typically this would be a random sample and the memory will be a First-In-First-Out buffer.

For more information, see the following.

@phdthesis{lin1993reinforcement,
 title  = {Reinforcement learning for robots using neural networks},
 author = {Lin, Long-Ji},
 year   = {1993},
 school = {Fujitsu Laboratories Ltd}
}

Template Parameters

EnvironmentType

Desired task.

Constructor & Destructor Documentation

RandomReplay()

template<typename EnvironmentType >

mlpack::rl::RandomReplay< EnvironmentType >::RandomReplay ( const size_t  batchSize, const size_t  capacity, const size_t  nSteps = 1, const size_t  dimension = StateType::dimension  )

inline

Construct an instance of random experience replay class.

Parameters

batchSize	Number of examples returned at each sample.
capacity	Total memory size in terms of number of examples.
nSteps	Number of steps to look in the future.
dimension	The dimension of an encoded state.

Member Function Documentation

GetNStepInfo()

template<typename EnvironmentType >

void mlpack::rl::RandomReplay< EnvironmentType >::GetNStepInfo ( double &  reward, StateType &  nextState, bool &  isEnd, const double &  discount  )

inline

Get the reward, next state and terminal boolean for nth step.

Parameters

reward	Given reward.
nextState	Given next state.
isEnd	Whether next state is terminal state.
discount	The discount parameter.

Sample()

template<typename EnvironmentType >

void mlpack::rl::RandomReplay< EnvironmentType >::Sample ( arma::mat &  sampledStates, std::vector< ActionType > &  sampledActions, arma::rowvec &  sampledRewards, arma::mat &  sampledNextStates, arma::irowvec &  isTerminal  )

inline

Sample some experiences.

Parameters

sampledStates	Sampled encoded states.
sampledActions	Sampled actions.
sampledRewards	Sampled rewards.
sampledNextStates	Sampled encoded next states.
isTerminal	Indicate whether corresponding next state is terminal state.

Size()

template<typename EnvironmentType >

const size_t& mlpack::rl::RandomReplay< EnvironmentType >::Size ( )

inline

Get the number of transitions in the memory.

Returns

Actual used memory size

Store()

template<typename EnvironmentType >

void mlpack::rl::RandomReplay< EnvironmentType >::Store ( StateType  state, ActionType  action, double  reward, StateType  nextState, bool  isEnd, const double &  discount  )

inline

Store the given experience.

Parameters

state	Given state.
action	Given action.
reward	Given reward.
nextState	Given next state.
isEnd	Whether next state is terminal state.
discount	The discount parameter.

Update()

template<typename EnvironmentType >

void mlpack::rl::RandomReplay< EnvironmentType >::Update ( arma::mat  , std::vector< ActionType >  , arma::mat  , arma::mat &    )

inline

Update the priorities of transitions and Update the gradients.

Parameters

*	(target) The learned value
*	(sampledActions) Agent's sampled action
*	(nextActionValues) Agent's next action
*	(gradients) The model's gradients

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

• src/mlpack/methods/reinforcement_learning/replay/random_replay.hpp