burlap.behavior.stochasticgames.madynamicprogramming.policies

Class EGreedyJointPolicy

java.lang.Object

burlap.behavior.stochasticgames.JointPolicy

burlap.behavior.stochasticgames.madynamicprogramming.MAQSourcePolicy

burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyJointPolicy

All Implemented Interfaces:

EnumerablePolicy, Policy

public class EGreedyJointPolicy
extends MAQSourcePolicy
implements EnumerablePolicy

An epsilon greedy joint policy, in which the joint action with the highest Q-value for a given target agent is returned a 1-epsilon fraction of the time, and a random joint action an epsilon fraction of the time. Ties are broken randomly.

Author:

James MacGlashan

Field Summary

Fields

Modifier and Type	Field and Description
protected double	epsilon The epsilon parameter specifying how often random joint actions are returned
protected MultiAgentQSourceProvider	qSourceProvider The multi-agent q-source provider
protected java.util.Random	rand A random object used for sampling
protected int	targetAgent The agent whose q-values dictate which joint actions to return

Fields inherited from class burlap.behavior.stochasticgames.JointPolicy

agentsInJointPolicy, agentsSynchronizedSoFar, lastSyncedState, lastSynchronizedJointAction

Constructor Summary

Constructors

Constructor and Description
EGreedyJointPolicy(double epsilon) Initializes for a given epsilon value.
EGreedyJointPolicy(MultiAgentQLearning actingAgent, double epsilon, int targetAgentNum) Initializes for a multi-agent Q-learning object and epsilon value.

Method Summary

Modifier and Type	Method and Description
Action	action(State s) This method will return an action sampled by the policy for the given state.
double	actionProb(State s, Action a) Returns the probability/probability density that the given action will be taken in the given state.
JointPolicy	copy() Creates a copy of this joint policy and returns it.
boolean	definedFor(State s) Specifies whether this policy is defined for the input state.
java.util.List<ActionProb>	policyDistribution(State s) This method will return action probability distribution defined by the policy.
void	setQSourceProvider(MultiAgentQSourceProvider provider) Sets the MultiAgentQSourceProvider that will be used to define this object's joint policy.
void	setTargetAgent(int agentNum) Sets the target privileged agent from which this joint policy is defined.

Methods inherited from class burlap.behavior.stochasticgames.JointPolicy

getAgentsInJointPolicy, getAgentSynchronizedActionSelection, getAllJointActions, setAgentsInJointPolicy, setAgentsInJointPolicyFromWorld, setAgentTypesInJointPolicy

Methods inherited from class java.lang.Object

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

Field Detail

qSourceProvider

protected MultiAgentQSourceProvider qSourceProvider

The multi-agent q-source provider

epsilon

protected double epsilon

The epsilon parameter specifying how often random joint actions are returned

rand

protected java.util.Random rand

A random object used for sampling

targetAgent

protected int targetAgent

The agent whose q-values dictate which joint actions to return

Constructor Detail

EGreedyJointPolicy

public EGreedyJointPolicy(double epsilon)

Initializes for a given epsilon value. The set of agents for which joint actions are returned, target agent whose q-values are maximized, and multi-agent q-source provider will need to be set manually with the methods JointPolicy.setAgentsInJointPolicy(List), setTargetAgent(int), and setQSourceProvider(MultiAgentQSourceProvider) before the policy can be queried. Note that the MultiAgentQLearning and MultiAgentDPPlanningAgent agents may do this themselves. Consult the documentation to check.

Parameters:

epsilon - the fraction of the time [0, 1] that the agent selections random actions.

EGreedyJointPolicy

public EGreedyJointPolicy(MultiAgentQLearning actingAgent,
                          double epsilon,
                          int targetAgentNum)

Initializes for a multi-agent Q-learning object and epsilon value. The set of agents for which joint actions are to be returned must be subsequently defined with the JointPolicy.setAgentsInJointPolicy(List). Note that the MultiAgentQLearning and MultiAgentDPPlanningAgent agents may do this themselves. Consult the documentation to check.

Parameters:

actingAgent - the agent whose Q-values are maximized.

epsilon - the fraction of the time [0, 1] that the agent selections random actions.

targetAgentNum - the target agent identifier whose Q-values should be used

Method Detail

action

public Action action(State s)

Description copied from interface: Policy

This method will return an action sampled by the policy for the given state. If the defined policy is stochastic, then multiple calls to this method for the same state may return different actions. The sampling should be with respect to defined action distribution that is returned by getActionDistributionForState

Specified by:

action in interface Policy

Parameters:

s - the state for which an action should be returned

Returns:

a sample action from the action distribution; null if the policy is undefined for s

actionProb

public double actionProb(State s,
                         Action a)

Description copied from interface: Policy

Returns the probability/probability density that the given action will be taken in the given state.

Specified by:

actionProb in interface Policy

Parameters:

s - the state of interest

a - the action that may be taken in the state

Returns:

the probability/probability density

policyDistribution

public java.util.List<ActionProb> policyDistribution(State s)

Description copied from interface: EnumerablePolicy

This method will return action probability distribution defined by the policy. The action distribution is represented by a list of ActionProb objects, each which specifies a grounded action and a probability of that grounded action being taken. The returned list does not have to include actions with probability 0.

Specified by:

policyDistribution in interface EnumerablePolicy

Parameters:

s - the state for which an action distribution should be returned

Returns:

a list of possible actions taken by the policy and their probability.

definedFor

public boolean definedFor(State s)

Description copied from interface: Policy

Specifies whether this policy is defined for the input state.

Specified by:

definedFor in interface Policy

Parameters:

s - the input state to test for whether this policy is defined

Returns:

true if this policy is defined for State s, false otherwise.

setQSourceProvider

public void setQSourceProvider(MultiAgentQSourceProvider provider)

Description copied from class: MAQSourcePolicy

Sets the MultiAgentQSourceProvider that will be used to define this object's joint policy.

Specified by:

setQSourceProvider in class MAQSourcePolicy

Parameters:

provider - the MultiAgentQSourceProvider that will be used to define this object's joint policy.

setTargetAgent

public void setTargetAgent(int agentNum)

Description copied from class: JointPolicy

Sets the target privileged agent from which this joint policy is defined.

Specified by:

setTargetAgent in class JointPolicy

Parameters:

agentNum - the target agent.

copy

public JointPolicy copy()

Description copied from class: JointPolicy

Creates a copy of this joint policy and returns it. This is useful when generating different agents using the same kind of policy, but have different target agents evaluating it.

Specified by:

copy in class JointPolicy

Returns:

a copy of this joint policy.