aima.core.probability.mdp

Interface PolicyEvaluation<S,A extends Action>

Type Parameters:

S - the state type.

A - the action type.

All Known Implementing Classes:

ModifiedPolicyEvaluation

public interface PolicyEvaluation<S,A extends Action>

Artificial Intelligence A Modern Approach (3rd Edition): page 656.

Given a policy π_i, calculate U_i=U^π_i, the utility of each state if π_i were to be executed.

Author:

Ciaran O'Reilly, Ravi Mohan

Method Summary

Modifier and Type	Method and Description
java.util.Map<S,java.lang.Double>	evaluate(java.util.Map<S,A> pi_i, java.util.Map<S,java.lang.Double> U, MarkovDecisionProcess<S,A> mdp) Policy evaluation: given a policy πi, calculate Ui=Uπi, the utility of each state if πi were to be executed.

Method Detail

evaluate

java.util.Map<S,java.lang.Double> evaluate(java.util.Map<S,A> pi_i,
                                           java.util.Map<S,java.lang.Double> U,
                                           MarkovDecisionProcess<S,A> mdp)

Policy evaluation: given a policy π_i, calculate U_i=U^π_i, the utility of each state if π_i were to be executed.

Parameters:

pi_i - a policy vector indexed by state

U - a vector of utilities for states in S

mdp - an MDP with states S, actions A(s), transition model P(s'|s,a)

Returns:

U_i=U^π_i, the utility of each state if π_i were to be executed.