Class LinearQLearningOptions<T>

Namespace

AiDotNet.Models.Options

Assembly

AiDotNet.dll

Configuration options for Linear Q-Learning agents.

public class LinearQLearningOptions<T> : ReinforcementLearningOptions<T>

Type Parameters

T

The numeric type used for calculations.

Inheritance

object

ReinforcementLearningOptions<T>

LinearQLearningOptions<T>

Inherited Members

ReinforcementLearningOptions<T>.LearningRate

ReinforcementLearningOptions<T>.DiscountFactor

ReinforcementLearningOptions<T>.LossFunction

ReinforcementLearningOptions<T>.Seed

ReinforcementLearningOptions<T>.BatchSize

ReinforcementLearningOptions<T>.ReplayBufferSize

ReinforcementLearningOptions<T>.TargetUpdateFrequency

ReinforcementLearningOptions<T>.UsePrioritizedReplay

ReinforcementLearningOptions<T>.EpsilonStart

ReinforcementLearningOptions<T>.EpsilonEnd

ReinforcementLearningOptions<T>.EpsilonDecay

ReinforcementLearningOptions<T>.WarmupSteps

ReinforcementLearningOptions<T>.MaxGradientNorm

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

Linear Q-Learning uses linear function approximation to estimate Q-values. Instead of maintaining a table, it learns weight vectors for each action and computes Q(s,a) = w_a^T * φ(s) where φ(s) are state features.

For Beginners: Linear Q-Learning extends tabular Q-learning to handle larger state spaces by using feature representations. Think of it as learning a formula instead of memorizing every single state.

Best for:

• Medium-sized continuous state spaces
• Problems where states can be represented as feature vectors
• Faster learning than tabular methods
• Generalization across similar states

Not suitable for:

• Very small discrete states (use tabular instead)
• Highly non-linear relationships (use neural networks)
• Continuous action spaces (use actor-critic)

Properties

ActionSize

Size of the action space (number of possible actions).

public int ActionSize { get; init; }

Property Value

int

FeatureSize

Number of features in the state representation.

public int FeatureSize { get; init; }

Property Value

int