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The Brown-UMBC Reinforcement Learning and Planning (BURLAP) Java code library is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. The library uses a highly flexible state/observation representation where you define states with your own Java classes, enabling support for domains that discrete, continuous, relational, or anything else. Planning and learning algorithms range from classic forward search planning to value-function-based stochastic planning and learning algorithms.

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package burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit;

import burlap.behavior.functionapproximation.ParametricFunction;
import burlap.behavior.singleagent.learnfromdemo.mlirl.support.DifferentiableValueFunction;

/**
 * An interface for value function initialization that is differentiable with respect to some parameters. This
 * interface is useful for DifferentiableSparseSampling which may be used to learn the value of leaf nodes
 * in a finite horizon valueFunction.
 *
 * @author James MacGlashan.
 */
public interface DifferentiableVInit extends DifferentiableValueFunction, ParametricFunction {


}