source: branches/HeuristicLab.Problems.GrammaticalOptimization-gkr/HeuristicLab.Algorithms.GrammaticalOptimization/SequentialDecisionPolicies/GenericFunctionApproximationGrammarPolicy.cs @ 12509

using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Runtime.ExceptionServices;
using System.Text;
using System.Text.RegularExpressions;
using System.Threading;
using System.Threading.Tasks;
using HeuristicLab.Common;
using HeuristicLab.Problems.GrammaticalOptimization;

namespace HeuristicLab.Algorithms.Bandits.GrammarPolicies {
  public sealed class GenericFunctionApproximationGrammarPolicy : IGrammarPolicy {
    private Dictionary<string, double> featureWeigths; // stores the necessary information for bandit policies for each state (=canonical phrase)
    private Dictionary<string, int> featureTries;
    private HashSet<string> done;
    private readonly bool useCanonicalPhrases;
    private readonly IProblem problem;



    public GenericFunctionApproximationGrammarPolicy(IProblem problem, bool useCanonicalPhrases = false) {
      this.useCanonicalPhrases = useCanonicalPhrases;
      this.problem = problem;
      this.featureWeigths = new Dictionary<string, double>();
      this.featureTries = new Dictionary<string, int>();
      this.done = new HashSet<string>();
    }

    private double[] activeAfterStates; // don't allocate each time
    private int[] actionIndexMap; // don't allocate each time

    public bool TrySelect(Random random, string curState, IEnumerable<string> afterStates, out int selectedStateIdx) {
      // fail if all states are done (corresponding state infos are disabled)
      if (afterStates.All(s => Done(s))) {
        // fail because all follow states have already been visited => also disable the current state (if we can be sure that it has been fully explored)
        MarkAsDone(curState);

        selectedStateIdx = -1;
        return false;
      }

      // determine active actions (not done yet) and create an array to map the selected index back to original actions
      if (activeAfterStates == null || activeAfterStates.Length < afterStates.Count()) {
        activeAfterStates = new double[afterStates.Count()];
        actionIndexMap = new int[afterStates.Count()];
      }
      var maxIdx = 0; int originalIdx = 0;
      foreach (var afterState in afterStates) {
        if (!Done(afterState)) {
          activeAfterStates[maxIdx] = 0.0;
          actionIndexMap[maxIdx] = originalIdx;


          activeAfterStates[maxIdx] = GetValue(afterState);

          maxIdx++;
        }
        originalIdx++;
      }
      
      
      // TODO: policy should be a parameter of the function approximation policy
      if (random.NextDouble() < 0.2) {
        selectedStateIdx = actionIndexMap[random.Next(maxIdx)];
      } else {
        // find max
        var bestQ = double.NegativeInfinity;
        var bestIdxs = new List<int>();
        for (int i = 0; i < maxIdx; i++) {
          if (activeAfterStates[i] > bestQ) {
            bestIdxs.Clear();
            bestIdxs.Add(i);
            bestQ = activeAfterStates[i];
          } else if (activeAfterStates[i].IsAlmost(bestQ)) {
            bestIdxs.Add(i);
          }
        }
        selectedStateIdx = actionIndexMap[bestIdxs[random.Next(bestIdxs.Count)]];
      }
      
      return true;
    }


    public void UpdateReward(IEnumerable<string> stateTrajectory, double reward) {
      foreach (var state in stateTrajectory) {
        UpdateWeights(state, reward);

        // only the last state can be terminal
        if (problem.Grammar.IsTerminal(state)) {
          MarkAsDone(state);
        }
      }
    }


    private IEnumerable<KeyValuePair<string, double>> Values {
      get { return featureWeigths.OrderByDescending(p => p.Value); }
    }

    public void Reset() {
      featureWeigths.Clear();
      done.Clear();
    }

    public int GetTries(string state) {
      return 0;
    }

    public int GetFeatureTries(string featureId) {
      int t;
      if (featureTries.TryGetValue(featureId, out t)) {
        return t;
      } else return 0;
    }

    public double GetValue(string state) {
      return problem.GetFeatures(state).Sum(feature => GetWeight(feature));
    }

    private double GetWeight(Feature feature) {
      double w;
      if (featureWeigths.TryGetValue(feature.Id, out w)) return w * feature.Value;
      else return 0.0;
    }
    private void UpdateWeights(string state, double reward) {
      double delta = reward - GetValue(state);
      foreach (var feature in problem.GetFeatures(state)) {
        //featureTries[feature.Id] = GetFeatureTries(feature.Id) + 1;
        //Debug.Assert(GetFeatureTries(feature.Id) >= 1);
        //double alpha = 1.0 / GetFeatureTries(feature.Id);
        //alpha = Math.Max(alpha, 0.001);
        
        // simple setting of constant alpha = 0.01 works very well for poly-10 (100% success rate for 20 runs within 40000 evaluations))
        var alpha = 0.01;

        double w;
        if (!featureWeigths.TryGetValue(feature.Id, out w)) {
          featureWeigths[feature.Id] = alpha * delta * feature.Value;
        } else {
          featureWeigths[feature.Id] += alpha * delta * feature.Value;
        }
      }
    }



    // the canonical states for the value function (banditInfos) and the done set must be distinguished
    // sequences of different length could have the same canonical representation and can have the same value (banditInfo)
    // however, if the canonical representation of a state is shorter than we must not mark the canonical state as done when all possible derivations from the initial state have been explored
    // eg. in the ant problem the canonical representation for ...lllA is ...rA
    // even though all possible derivations (of limited length) of lllA have been visited we must not mark the state rA as done
    private void MarkAsDone(string state) {
      var s = CanonicalState(state);
      // when the lengths of the canonical string and the original string are the same we also disable the actions
      // always disable terminals
      Debug.Assert(s.Length <= state.Length);
      if (s.Length == state.Length || problem.Grammar.IsTerminal(state)) {
        Debug.Assert(!done.Contains(s));
        done.Add(s);
      } else {
        // for non-terminals where the canonical string is shorter than the original string we can only disable the canonical representation for all states in the same level
        Debug.Assert(!done.Contains(s + state.Length));
        done.Add(s + state.Length); // encode the original length of the state, states in the same level of the tree are treated as equivalent
      }
    }

    // symmetric to MarkDone
    private bool Done(string state) {
      var s = CanonicalState(state);
      if (s.Length == state.Length || problem.Grammar.IsTerminal(state)) {
        return done.Contains(s);
      } else {
        // it is not necessary to visit states if the canonical representation has already been fully explored
        if (done.Contains(s)) return true;
        if (done.Contains(s + state.Length)) return true;
        for (int i = 1; i < state.Length; i++) {
          if (done.Contains(s + i)) return true;
        }
        return false;
      }
    }

    private string CanonicalState(string state) {
      if (useCanonicalPhrases) {
        return problem.CanonicalRepresentation(state);
      } else
        return state;
    }
  }
}