/// <summary>
/// Update the existing policy based on the available information
/// </summary>
/// <param name="PastState">The state that the agent transistion from</param>
/// <param name="CurrentState">The state that the agent has transitioned to</param>
/// <param name="Action">The action taken by the agent to cause transition</param>
/// <param name="Reward">The reward recieved from its transition</param>
public void UpdatePolicy(List <int> PastState, List <int> CurrentState, int Action, double Reward)
{
/*prediction error*/
double error = TD(PastState, CurrentState, Action, Reward);
/*current action state feature set*/
/*parameter increment values*/
List <double> parameters = Qfunc.Gradient(PastState.ConvertAll(v => Convert.ToDouble(v)), Action).ConvertAll(v => v * error * Epsilon);
/*update parameters in approximator*/
Qfunc.update(parameters, Action);
}
/// <summary>
/// Calculates the predicted Q value for the state action pair
/// </summary>
/// <param name="State">The current state assumed to exibit the markov property</typeparam>
/// <param name="Action">The select action</typeparam>
/// <returns>predicted Q value</returns>
public double GetQValue(List <int> State, int Action)
{
/*actions always come first as it assumed the action space is constant */
return(Qfunc.Value(State.ConvertAll(v => Convert.ToDouble(v)), Action));
}
