/// <summary>
/// Updates the Q-Learner model by reinforcing with the new state/action feedback values.
/// </summary>
/// <param name="x">State vector.</param>
/// <param name="y">Action label.</param>
/// <param name="r">Reward value.</param>
public override void Learn(Vector x, double y, double r)
{
var state = this.Q.Keys.Last();
var stateP = MDPConverter.GetState(x, this.FeatureProperties, this.FeatureDiscretizer);
var action = MDPConverter.GetAction(y, state.Id, stateP.Id);
this.Q.AddOrUpdate(stateP, action, r);
this.Q[state, action] = (1.0 - this.LearningRate) * Q[state, action]
+ this.LearningRate * (r + this.Lambda * Q[stateP, Q.GetMaxAction(stateP)]);
}
/// <summary>
/// Updates the Q-Learner model by reinforcing with the new state/action and transition state feedback values.
/// </summary>
/// <param name="x1">Item features, i.e. the original State.</param>
/// <param name="y">Action label.</param>
/// <param name="x2">Transition state value.</param>
/// <param name="r">Reward value.</param>
public override void Learn(Vector x1, double y, Vector x2, double r)
{
var state = MDPConverter.GetState(x1, this.FeatureProperties, this.FeatureDiscretizer);
var stateP = MDPConverter.GetState(x2, this.FeatureProperties, this.FeatureDiscretizer);
var action = MDPConverter.GetAction(y, state.Id, stateP.Id);
if (!Q.ContainsKey(state))
{
Q.AddOrUpdate(state, action, r);
}
if (!Q.ContainsKey(stateP))
{
Q.AddKey(stateP);
}
this.Q[state, action] = (1.0 - this.LearningRate) * Q[state, action]
+ this.LearningRate * (r + this.Lambda * Q[stateP, Q.GetMaxAction(stateP)]);
}
