AIXI.Agent.ModelUpdateAction C# (CSharp) Method

        public abstract void ModelUpdateAction(int action);

        public double Sample(Agent agent, int horizon)
        {
            double reward = 0.0;

            if (horizon == 0)
            {
                return((int)reward);
            }
            else if (this.Type == ChanceNode)
            {
                var percept      = agent.GeneratePerceptAndUpdate();
                int observation  = percept.Item1;
                int randomReward = percept.Item2;

                if (!this.Children.ContainsKey(observation))  //new node ->add it as decision node
                {
                    this.Children[observation] = new MonteCarloSearchNode(DecisionNode);
                }
                MonteCarloSearchNode observationChild = this.Children[observation];

                reward = randomReward + observationChild.Sample(agent, horizon - 1);
            }
            else if (this.Visits == 0) //unvisited decision node or we have exceeded maximum tree depth
            {
                reward = agent.Playout(horizon);
//                Console.WriteLine("from playout: reward ="+reward);
            }
            else   //Previously visited decision node

            {
                int actionNullable = this.SelectAction(agent);
                int action         = actionNullable;

                agent.ModelUpdateAction(action);

                if (!this.Children.ContainsKey(action))     //this action is new chance child
                {
                    this.Children[action] = new MonteCarloSearchNode(ChanceNode);
                }
                MonteCarloSearchNode actionChild = this.Children[action];

                reward = actionChild.Sample(agent, horizon);   //it is not clear if not horizon-1. (asks pyaixi)
            }

            double visitsDouble = this.Visits;

            //Console.WriteLine("> {3} - {0}, {1}, {2}", this.mean, reward, (reward + (visitsDouble * this.mean) / (visitsDouble + 1.0)), visitsDouble);
            this.Mean   = (reward + (visitsDouble * this.Mean)) / (1.0 + visitsDouble);
            this.Visits = this.Visits + 1;

            return(reward);
        }