source: branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/HeuristicLab.Algorithms.Bandits/Policies/UCB1Policy.cs @ 13620

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 {
  // policy for k-armed bandit (see Auer et al. 2002)
  public class UCB1Policy : IBanditPolicy {
    public double MaxReward { get; private set; }
    public UCB1Policy(double maxReward = 1.0) {
      this.MaxReward = maxReward;
    }
    public int SelectAction(Random random, IEnumerable<IBanditPolicyActionInfo> actionInfos) {
      var myActionInfos = actionInfos.OfType<DefaultPolicyActionInfo>();
      double bestQ = double.NegativeInfinity;
      int totalTries = myActionInfos.Sum(a => a.Tries);

      var bestActions = new List<int>();
      int aIdx = -1;
      foreach (var aInfo in myActionInfos) {
        aIdx++;
        double q;
        if (aInfo.Tries == 0) {
          q = double.PositiveInfinity;
        } else {

          //q = aInfo.SumReward / aInfo.Tries + MaxReward * Math.Sqrt((2 * Math.Log(totalTries)) / aInfo.Tries);
          q = aInfo.MaxReward + MaxReward * Math.Sqrt((2 * Math.Log(totalTries)) / aInfo.Tries);
        }
        if (q > bestQ) {
          bestQ = q;
          bestActions.Clear();
          bestActions.Add(aIdx);
        } else if (q.IsAlmost(bestQ)) {
          bestActions.Add(aIdx);
        }
      }
      Debug.Assert(bestActions.Any());
      return bestActions.SelectRandom(random);
    }

    public IBanditPolicyActionInfo CreateActionInfo() {
      return new DefaultPolicyActionInfo();
    }
    public override string ToString() {
      return string.Format("UCB1Policy({0})", MaxReward);
    }
  }
}