burlap.behavior.singleagent.learnfromdemo.mlirl.support

Interface QGradientPlanner

All Superinterfaces:

QFunction, ValueFunction

All Known Implementing Classes:

DifferentiableDP, DifferentiableSparseSampling, DifferentiableVI

public interface QGradientPlanner
extends QFunction

An interface for a valueFunction that can produce Q-value gradients.

Author:

James MacGlashan.

Nested Class Summary

Nested classes/interfaces inherited from interface burlap.behavior.valuefunction.QFunction

QFunction.QFunctionHelper

Method Summary

Methods

Modifier and Type	Method and Description
java.util.List<QGradientTuple>	getAllQGradients(State s) Returns the list of Q-value gradients (returned as objects) for each action permissible in the given state.
QGradientTuple	getQGradient(State s, GroundedAction a) Returns the Q-value gradient (QGradientTuple) for the given state and action.
void	setBoltzmannBetaParameter(double beta) Sets this valueFunction's Boltzmann beta parameter used to compute gradients.

Methods inherited from interface burlap.behavior.valuefunction.QFunction

getQ, getQs

Methods inherited from interface burlap.behavior.valuefunction.ValueFunction

value

Method Detail

getAllQGradients

java.util.List<QGradientTuple> getAllQGradients(State s)

Returns the list of Q-value gradients (returned as objects) for each action permissible in the given state.

Parameters:

s - the state for which Q-value gradients are to be returned.

Returns:

the list of Q-value gradients for each action permissible in the given state.

getQGradient

QGradientTuple getQGradient(State s,
                          GroundedAction a)

Returns the Q-value gradient (QGradientTuple) for the given state and action.

Parameters:

s - the state for which the Q-value gradient is to be returned

a - the action for which the Q-value gradient is to be returned.

Returns:

the Q-value gradient for the given state and action.

setBoltzmannBetaParameter

void setBoltzmannBetaParameter(double beta)

Sets this valueFunction's Boltzmann beta parameter used to compute gradients. As beta gets larger, the policy becomes more deterministic.

Parameters:

beta - the value to which this valueFunction's Boltzmann beta parameter will be set