Table of Contents

Class A2CAgent<T>

Namespace
AiDotNet.ReinforcementLearning.Agents.A2C
Assembly
AiDotNet.dll

Advantage Actor-Critic (A2C) agent for reinforcement learning.

public class A2CAgent<T> : DeepReinforcementLearningAgentBase<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
A2CAgent<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

A2C is a synchronous, simpler version of A3C that combines policy gradients with value function learning. It's the foundation for many modern RL algorithms including PPO.

For Beginners: A2C learns two networks simultaneously: - **Actor**: Decides which action to take (policy) - **Critic**: Evaluates how good the current state is (value function)

The critic helps the actor learn faster by providing better feedback than rewards alone. Think of it like having a coach (critic) give you targeted advice instead of just saying "good" or "bad" after the game ends.

A2C is simpler than PPO but still very effective. Good starting point for actor-critic methods.

Reference: Mnih et al., "Asynchronous Methods for Deep Reinforcement Learning", 2016 (describes A3C, A2C is the synchronous version).

Constructors

A2CAgent(A2COptions<T>) 

public A2CAgent(A2COptions<T> options)

Parameters

options A2COptions<T>

Properties

FeatureCount

Gets the number of input features (state dimensions).

public override int FeatureCount { get; }

Property Value

int

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[]

GetMetrics()

Gets the current training metrics.

public override Dictionary<string, T> GetMetrics()

Returns

Dictionary<string, T>

Dictionary of metric names to values.

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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