Package ai.djl.modality.rl.agent

Class QAgent

java.lang.Object

ai.djl.modality.rl.agent.QAgent

All Implemented Interfaces:

RlAgent

public class QAgent
extends Object
implements RlAgent

An RlAgent that implements Q or Deep-Q Learning.

Deep-Q Learning estimates the total reward that will be given until the environment ends in a particular state after taking a particular action. Then, it is trained by ensuring that the prediction before taking the action match what would be predicted after taking the action. More information can be found in the paper.

It is one of the earliest successful techniques for reinforcement learning with Deep learning. It is also a good introduction to the field. However, many better techniques are commonly used now.

Constructor Summary

Constructors

Constructor	Description
QAgent(Trainer trainer, float rewardDiscount)	Constructs a QAgent.
QAgent(Trainer trainer, float rewardDiscount, Batchifier batchifier)	Constructs a QAgent with a custom Batchifier.

Method Summary

Modifier and Type	Method	Description
NDList	chooseAction(RlEnv env, boolean training)	Chooses the next action to take within the RlEnv.
void	trainBatch(RlEnv.Step[] batchSteps)	Trains this RlAgent on a batch of RlEnv.Steps.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

QAgent

public QAgent(Trainer trainer,
 float rewardDiscount)

Constructs a QAgent.

It uses the StackBatchifier as the default batchifier.

Parameters:

trainer - the trainer for the model to learn

rewardDiscount - the reward discount to apply to rewards from future states

QAgent

public QAgent(Trainer trainer,
 float rewardDiscount,
 Batchifier batchifier)

Constructs a QAgent with a custom Batchifier.

Parameters:

trainer - the trainer for the model to learn

rewardDiscount - the reward discount to apply to rewards from future states

batchifier - the batchifier to join inputs with

Method Details

chooseAction

public NDList chooseAction(RlEnv env,
 boolean training)

Chooses the next action to take within the RlEnv.

Specified by:

chooseAction in interface RlAgent

Parameters:

env - the current environment

training - true if the agent is currently traning

Returns:

the action to take

trainBatch

public void trainBatch(RlEnv.Step[] batchSteps)

Trains this RlAgent on a batch of RlEnv.Steps.

Specified by:

trainBatch in interface RlAgent

Parameters:

batchSteps - the steps to train on