source: branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/HeuristicLab.Algorithms.Bandits/Policies/EpsGreedyPolicy.cs @ 12290

using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using HeuristicLab.Common;

namespace HeuristicLab.Algorithms.Bandits.BanditPolicies {
  public class EpsGreedyPolicy : IBanditPolicy {
    private readonly double eps;
    private readonly RandomPolicy randomPolicy;
    private readonly string desc;


    public EpsGreedyPolicy(double eps) : this(eps, string.Empty) { }

    public EpsGreedyPolicy(double eps, string desc) {
      this.eps = eps;
      this.randomPolicy = new RandomPolicy();
      this.desc = desc;
    }

    public int SelectAction(Random random, IEnumerable<IBanditPolicyActionInfo> actionInfos) {
      Debug.Assert(actionInfos.Any());
      if (random.NextDouble() >= eps) { // eps == 0 should be equivalent to pure exploitation, eps == 1 is pure exploration
        // select best
        var myActionInfos = actionInfos.OfType<DefaultPolicyActionInfo>();
        var bestActions = new List<int>();
        double bestQ = double.NegativeInfinity;

        int aIdx = -1;
        foreach (var aInfo in myActionInfos) {
          aIdx++;

          var q = aInfo.Value;

          if (q > bestQ) {
            bestActions.Clear();
            bestActions.Add(aIdx);
            bestQ = q;
          } else if (q.IsAlmost(bestQ)) {
            bestActions.Add(aIdx);
          }
        }
        Debug.Assert(bestActions.Any());
        return bestActions.SelectRandom(random);
      } else {
        // select random
        return randomPolicy.SelectAction(random, actionInfos);
      }
    }

    public IBanditPolicyActionInfo CreateActionInfo() {
      return new DefaultPolicyActionInfo();
    }


    public override string ToString() {
      return string.Format("EpsGreedyPolicy({0:F2},{1})", eps, desc);
    }
  }
}