Table of Contents

Class TabularQLearningAgent<T>

Namespace
AiDotNet.ReinforcementLearning.Agents.TabularQLearning
Assembly
AiDotNet.dll

Tabular Q-Learning agent using lookup table for Q-values.

public class TabularQLearningAgent<T> : ReinforcementLearningAgentBase<T>, IRLAgent<T>, IFullModel<T, Vector<T>, Vector<T>>, IModel<Vector<T>, Vector<T>, ModelMetadata<T>>, IModelSerializer, ICheckpointableModel, IParameterizable<T, Vector<T>, Vector<T>>, IFeatureAware, IFeatureImportance<T>, ICloneable<IFullModel<T, Vector<T>, Vector<T>>>, IGradientComputable<T, Vector<T>, Vector<T>>, IJitCompilable<T>, IDisposable

Type Parameters

T

The numeric type used for calculations.

Inheritance
TabularQLearningAgent<T>
Implements
IFullModel<T, Vector<T>, Vector<T>>
IModel<Vector<T>, Vector<T>, ModelMetadata<T>>
IParameterizable<T, Vector<T>, Vector<T>>
ICloneable<IFullModel<T, Vector<T>, Vector<T>>>
IGradientComputable<T, Vector<T>, Vector<T>>
Inherited Members
Extension Methods

Remarks

Tabular Q-Learning is the foundational RL algorithm that maintains a table of Q-values for each state-action pair. No neural networks required.

For Beginners: Q-Learning is like creating a cheat sheet: for every situation (state) and action you could take, you write down how good that choice is (Q-value). Over time, you update this sheet based on actual rewards you receive.

Key features:

  • Off-Policy: Learns optimal policy while following exploratory policy
  • Tabular: Uses lookup table, no function approximation
  • Model-Free: Doesn't need to know environment dynamics
  • Value-Based: Learns action values, derives policy from them

Perfect for: Small discrete state/action spaces (grid worlds, simple games) Famous for: Watkins 1989, the foundation of modern RL

Constructors

TabularQLearningAgent(TabularQLearningOptions<T>) 

public TabularQLearningAgent(TabularQLearningOptions<T> options)

Parameters

options TabularQLearningOptions<T>

Properties

FeatureCount

Gets the number of input features (state dimensions).

public override int FeatureCount { get; }

Property Value

int

ParameterCount

Gets the number of parameters in the agent.

public override int ParameterCount { get; }

Property Value

int

Remarks

Deep RL agents return parameter counts from neural networks. Classical RL agents (tabular, linear) may have different implementations.

Methods

ApplyGradients(Vector<T>, T)

Applies gradients to update the agent.

public override void ApplyGradients(Vector<T> gradients, T learningRate)

Parameters

gradients Vector<T>
learningRate T

Clone()

Clones the agent.

public override IFullModel<T, Vector<T>, Vector<T>> Clone()

Returns

IFullModel<T, Vector<T>, Vector<T>>

ComputeGradients(Vector<T>, Vector<T>, ILossFunction<T>?)

Computes gradients for the agent.

public override Vector<T> ComputeGradients(Vector<T> input, Vector<T> target, ILossFunction<T>? lossFunction = null)

Parameters

input Vector<T>
target Vector<T>
lossFunction ILossFunction<T>

Returns

Vector<T>

Deserialize(byte[])

Deserializes the agent from bytes.

public override void Deserialize(byte[] data)

Parameters

data byte[]

GetModelMetadata()

Gets model metadata.

public override ModelMetadata<T> GetModelMetadata()

Returns

ModelMetadata<T>

GetParameters()

Gets the agent's parameters.

public override Vector<T> GetParameters()

Returns

Vector<T>

LoadModel(string)

Loads the agent's state from a file.

public override void LoadModel(string filepath)

Parameters

filepath string

Path to load the agent from.

SaveModel(string)

Saves the agent's state to a file.

public override void SaveModel(string filepath)

Parameters

filepath string

Path to save the agent.

SelectAction(Vector<T>, bool)

Selects an action given the current state observation.

public override Vector<T> SelectAction(Vector<T> state, bool training = true)

Parameters

state Vector<T>

The current state observation as a Vector.

training bool

Whether the agent is in training mode (affects exploration).

Returns

Vector<T>

Action as a Vector (can be discrete or continuous).

Serialize()

Serializes the agent to bytes.

public override byte[] Serialize()

Returns

byte[]

SetParameters(Vector<T>)

Sets the agent's parameters.

public override void SetParameters(Vector<T> parameters)

Parameters

parameters Vector<T>

StoreExperience(Vector<T>, Vector<T>, T, Vector<T>, bool)

Stores an experience tuple for later learning.

public override void StoreExperience(Vector<T> state, Vector<T> action, T reward, Vector<T> nextState, bool done)

Parameters

state Vector<T>

The state before action.

action Vector<T>

The action taken.

reward T

The reward received.

nextState Vector<T>

The state after action.

done bool

Whether the episode terminated.

Train()

Performs one training step, updating the agent's policy/value function.

public override T Train()

Returns

T

The training loss for monitoring.

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