public static void main(String[] args) {
GridWorldDomain gw = new GridWorldDomain(11, 11); // 11x11 grid world
gw.setMapToFourRooms(); // four rooms layout
gw.setProbSucceedTransitionDynamics(0.8); // stochastic transitions with
// 0.8 success rate
final Domain domain = gw.generateDomain(); // generate the grid world
// domain
// setup initial state
State s = GridWorldDomain.getOneAgentOneLocationState(domain);
GridWorldDomain.setAgent(s, 0, 0);
GridWorldDomain.setLocation(s, 0, 10, 10);
// ends when the agent reaches a location
final TerminalFunction tf =
new SinglePFTF(domain.getPropFunction(GridWorldDomain.PFATLOCATION));
// reward function definition
final RewardFunction rf = new GoalBasedRF(new TFGoalCondition(tf), 5., -0.1);
// initial state generator
final ConstantStateGenerator sg = new ConstantStateGenerator(s);
// set up the state hashing system for looking up states
final DiscreteStateHashFactory hashingFactory = new DiscreteStateHashFactory();
/** Create factory for Q-learning agent */
LearningAgentFactory qLearningFactory =
new LearningAgentFactory() {
@Override
public String getAgentName() {
return "Q-learning";
}
@Override
public LearningAgent generateAgent() {
return new QLearning(domain, rf, tf, 0.99, hashingFactory, 0.3, 0.1);
}
};
// define experiment
LearningAlgorithmExperimenter exp =
new LearningAlgorithmExperimenter((SADomain) domain, rf, sg, 10, 100, qLearningFactory);
exp.setUpPlottingConfiguration(
500,
250,
2,
1000,
TrialMode.MOSTRECENTANDAVERAGE,
PerformanceMetric.CUMULATIVESTEPSPEREPISODE,
PerformanceMetric.AVERAGEEPISODEREWARD);
// start experiment
exp.startExperiment();
}
