Java Environment类代码示例

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
@Override
public Episode runLearningEpisode(Environment env, int maxSteps) {

	Episode ea = maxSteps != -1 ? PolicyUtils.rollout(this.learningPolicy, env, maxSteps) : PolicyUtils.rollout(this.learningPolicy, env);

	this.updateDatasetWithLearningEpisode(ea);

	if(this.shouldRereunPolicyIteration(ea)){
		this.runPolicyIteration(this.maxNumPlanningIterations, this.maxChange);
		this.numStepsSinceLastLearningPI = 0;
	}
	else{
		this.numStepsSinceLastLearningPI += ea.numTimeSteps()-1;
	}

	if(episodeHistory.size() >= numEpisodesToStore){
		episodeHistory.poll();
	}
	episodeHistory.offer(ea);

	return ea;
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
/**
 * Collects nSamples of SARS tuples from an {@link burlap.mdp.singleagent.environment.Environment} and returns it in a {@link burlap.behavior.singleagent.learning.lspi.SARSData} object.
 * Each sequence of samples is no longer than maxEpisodeSteps and samples are collected using this object's {@link #collectDataFrom(burlap.mdp.singleagent.environment.Environment, int, SARSData)}
 * method. After each call to {@link #collectDataFrom(burlap.mdp.singleagent.environment.Environment, int, SARSData)}, the provided {@link burlap.mdp.singleagent.environment.Environment}
 * is sent the {@link burlap.mdp.singleagent.environment.Environment#resetEnvironment()} message.
 * @param env The {@link burlap.mdp.singleagent.environment.Environment} from which samples should be collected.
 * @param nSamples The number of samples to generate.
 * @param maxEpisodeSteps the maximum number of steps to take from any initial state of the {@link burlap.mdp.singleagent.environment.Environment}.
 * @param intoDataset the dataset into which the results will be collected. If null, a new dataset is created.
 * @return the intoDataset object, which is created if it is input as null.
 */
public SARSData collectNInstances(Environment env, int nSamples, int maxEpisodeSteps, SARSData intoDataset){

	if(intoDataset == null){
		intoDataset = new SARSData(nSamples);
	}

	while(nSamples > 0 && !env.isInTerminalState()){
		int maxSteps = Math.min(nSamples, maxEpisodeSteps);
		int oldSize = intoDataset.size();
		this.collectDataFrom(env, maxSteps, intoDataset);
		int delta = intoDataset.size() - oldSize;
		nSamples -= delta;
		env.resetEnvironment();
	}

	return intoDataset;

}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
@Override
public SARSData collectDataFrom(Environment env, int maxSteps, SARSData intoDataset) {

	if(intoDataset == null){
		intoDataset = new SARSData();
	}

	int nsteps = 0;
	while(!env.isInTerminalState() && nsteps < maxSteps){
		List<Action> gas = ActionUtils.allApplicableActionsForTypes(this.actionTypes, env.currentObservation());
		Action ga = gas.get(RandomFactory.getMapped(0).nextInt(gas.size()));
		EnvironmentOutcome eo = env.executeAction(ga);
		intoDataset.add(eo.o, eo.a, eo.r, eo.op);

		nsteps++;
	}

	return intoDataset;
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
public EnvironmentShell(Domain domain, Environment env, InputStream is, PrintStream os) {
	super(domain, is, os);
	this.env = env;

	this.welcomeMessage = "Welcome to the BURLAP agent environment shell. Type the command 'help' to bring " +
			"up additional information about using this shell.";

	this.helpText = "Use the command help to bring up this message again. " +
			"Here is a list of standard reserved commands:\n" +
			"cmds - list all known commands.\n" +
			"aliases - list all known command aliases.\n" +
			"alias - set an alias for a command.\n" +
			"quit - terminate this shell.\n\n" +
			"Other useful, but non-reserved, commands are:\n" +
			"obs - print the current observation of the environment\n" +
			"ex - execute an action\n\n" +
			"Usually, you can get help on an individual command by passing it the -h option.";

}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
public AtariDQN(DeepQLearner learner, DeepQTester tester, DQN vfa, ActionSet actionSet, Environment env,
                FrameExperienceMemory trainingMemory,
                FrameExperienceMemory testMemory) {
    super(learner, tester, vfa, actionSet, env);

    this.trainingMemory = trainingMemory;
    this.testMemory = testMemory;
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
public TrainingHelper(DeepQLearner learner, Tester tester, DQN vfa, ActionSet actionSet, Environment env) {
    this.learner = learner;
    this.vfa = vfa;
    this.tester = tester;
    this.env = env;
    this.actionSet = actionSet;

    this.stepCounter = 0;
    this.episodeCounter = 0;
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
public ALEVisualExplorer(SADomain domain, Environment env, Visualizer painter, boolean human) {
    super(domain, env, painter,
            (int)(ALEDomainConstants.ALEScreenWidth * widthRatio),
            (int)(ALEDomainConstants.ALEScreenHeight * heightRatio));

    if (human) {
        enableHumanInput();
    }
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
@Override
public Episode runLearningEpisode(Environment env, int maxSteps) {
	//initialize our episode object with the initial state of the environment
	Episode e = new Episode(env.currentObservation());

	//behave until a terminal state or max steps is reached
	State curState = env.currentObservation();
	int steps = 0;
	while(!env.isInTerminalState() && (steps < maxSteps || maxSteps == -1)){

		//select an action
		Action a = this.learningPolicy.action(curState);

		//take the action and observe outcome
		EnvironmentOutcome eo = env.executeAction(a);

		//record result
		e.transition(eo);

		//get the max Q value of the resulting state if it's not terminal, 0 otherwise
		double maxQ = eo.terminated ? 0. : this.value(eo.op);

		//update the old Q-value
		QValue oldQ = this.storedQ(curState, a);
		oldQ.q = oldQ.q + this.learningRate * (eo.r + this.gamma * maxQ - oldQ.q);


		//update state pointer to next environment state observed
		curState = eo.op;
		steps++;

	}

	return e;
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
public RelativeRewardEnvironmentDecorator(
        QProvider provider, Environment delegate,
        State baselineState, Action baselineAction) {
    this.provider = provider;
    this.delegate = delegate;
    this.baselineState = baselineState;
    this.baselineAction = baselineAction;
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
/**
 * Follows the policy in the given {@link burlap.mdp.singleagent.environment.Environment}. The policy will stop being followed once a terminal state
 * in the environment is reached.
 * @param p the {@link Policy}
 * @param env The {@link burlap.mdp.singleagent.environment.Environment} in which this policy is to be evaluated.
 * @return An {@link Episode} object specifying the interaction with the environment.
 */
public static Episode rollout(Policy p, Environment env){

	Episode ea = new Episode(env.currentObservation());

	do{
		followAndRecordPolicy(p, env, ea);
	}while(!env.isInTerminalState());

	return ea;
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
public static EnvironmentOptionOutcome control(Option o, Environment env, double discount){
	Random rand = RandomFactory.getMapped(0);
	State initial = env.currentObservation();
	State cur = initial;

	Episode episode = new Episode(cur);
	Episode history = new Episode(cur);
	double roll;
	double pT;
	int nsteps = 0;
	double r = 0.;
	double cd = 1.;
	do{
		Action a = o.policy(cur, history);
		EnvironmentOutcome eo = env.executeAction(a);
		nsteps++;
		r += cd*eo.r;
		cur = eo.op;
		cd *= discount;


		history.transition(a, eo.op, eo.r);

		AnnotatedAction annotatedAction = new AnnotatedAction(a, o.toString() + "(" + nsteps + ")");
		episode.transition(annotatedAction, eo.op, r);


		pT = o.probabilityOfTermination(eo.op, history);
		roll = rand.nextDouble();

	}while(roll > pT && !env.isInTerminalState());

	EnvironmentOptionOutcome eoo = new EnvironmentOptionOutcome(initial, o, cur, r, env.isInTerminalState(), discount, episode);

	return eoo;

}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
/**
 * Initializes.
 * The trialLength will be interpreted as the number of episodes, but it can be reinterpreted as a total number of steps per trial using the
 * {@link #toggleTrialLengthInterpretation(boolean)}.
 * @param testEnvironment the test {@link burlap.mdp.singleagent.environment.Environment} in which experiments will be performed.
 * @param nTrials the number of trials
 * @param trialLength the length of the trials (by default in episodes, but can be intereted as maximum step length)
 * @param agentFactories factories to generate the agents to be tested.
 */
public LearningAlgorithmExperimenter(Environment testEnvironment, int nTrials, int trialLength, LearningAgentFactory...agentFactories){
	
	if(agentFactories.length == 0){
		throw new RuntimeException("Zero agent factories provided. At least one must be given for an experiment");
	}
	
	this.testEnvironment = testEnvironment;
	this.nTrials = nTrials;
	this.trialLength = trialLength;
	this.agentFactories = agentFactories;
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
/**
 * If the input {@link burlap.mdp.singleagent.environment.Environment} is an instance {@link EnvironmentServerInterface},
 * then all the input observers are added to it and it is returned. Otherwise, a new {@link EnvironmentServer}
 * is created around it, with all of the observers added.
 * @param env the {@link burlap.mdp.singleagent.environment.Environment} that will have observers added to it
 * @param observers the {@link EnvironmentObserver} objects to add.
 * @return the input {@link burlap.mdp.singleagent.environment.Environment} or an {@link EnvironmentServer}.
 */
public static EnvironmentServerInterface constructServerOrAddObservers(Environment env, EnvironmentObserver...observers){
	if(env instanceof EnvironmentServerInterface){
		((EnvironmentServerInterface)env).addObservers(observers);
		return (EnvironmentServerInterface)env;
	}
	else{
		return constructor(env, observers);
	}
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
/**
 * Returns the root {@link burlap.mdp.singleagent.environment.Environment} delegate. Useful
 * if an {@link EnvironmentDelegation} is expected to have
 * a delegate that is an {@link EnvironmentDelegation}.
 * @param env the {@link EnvironmentDelegation} to inspect
 * @return the root {@link burlap.mdp.singleagent.environment.Environment} delegate
 */
public static Environment getRootEnvironmentDelegate(EnvironmentDelegation env){
	Environment envChild = env.getEnvironmentDelegate();
	if(envChild instanceof EnvironmentDelegation){
		envChild = getRootEnvironmentDelegate((EnvironmentDelegation)envChild);
	}
	return envChild;
}
 

import burlap.mdp.singleagent.environment.Environment; //导入依赖的package包/类
/**
 * Returns the {@link burlap.mdp.singleagent.environment.Environment} or {@link burlap.mdp.singleagent.environment.Environment}
 * delegate that implements the class/interface type, or null if none do.
 * @param env An {@link burlap.mdp.singleagent.environment.Environment} to inspect
 * @param type the class/interface type against which and {@link burlap.mdp.singleagent.environment.Environment} or
 *             its delegates are being compared.
 * @return the {@link burlap.mdp.singleagent.environment.Environment} delegate implementing the input type or null if none do.
 */
public static Environment getDelegateImplementing(Environment env, Class<?> type){

	if(type.isAssignableFrom(env.getClass())){
		return env;
	}
	else if(env instanceof EnvironmentDelegation){
		return getDelegateImplementing(((EnvironmentDelegation)env).getEnvironmentDelegate(), type);
	}

	return null;

}