Table of Contents

Class A2COptions<T>

Namespace
AiDotNet.Models.Options
Assembly
AiDotNet.dll

Configuration options for Advantage Actor-Critic (A2C) agents.

public class A2COptions<T>

Type Parameters

T

The numeric type used for calculations.

Inheritance
A2COptions<T>
Inherited Members

Remarks

A2C is a synchronous version of A3C that is simpler and often more sample-efficient. It combines policy gradients with value function learning for stable, efficient training.

For Beginners: A2C learns two things simultaneously: - **Actor (Policy)**: What action to take in each state - **Critic (Value Function)**: How good each state is

The critic helps the actor learn faster by providing better feedback than just rewards alone. Think of the critic as a coach giving targeted advice rather than just "good" or "bad".

A2C is the foundation for many modern RL algorithms including PPO.

Constructors

A2COptions()

public A2COptions()

Properties

ActionSize

public int ActionSize { get; set; }

Property Value

int

DiscountFactor

public T DiscountFactor { get; set; }

Property Value

T

EntropyCoefficient

public T EntropyCoefficient { get; set; }

Property Value

T

IsContinuous

public bool IsContinuous { get; set; }

Property Value

bool

PolicyHiddenLayers

public List<int> PolicyHiddenLayers { get; set; }

Property Value

List<int>

PolicyLearningRate

public T PolicyLearningRate { get; set; }

Property Value

T

Seed

public int? Seed { get; set; }

Property Value

int?

StateSize

public int StateSize { get; set; }

Property Value

int

StepsPerUpdate

public int StepsPerUpdate { get; set; }

Property Value

int

ValueHiddenLayers

public List<int> ValueHiddenLayers { get; set; }

Property Value

List<int>

ValueLearningRate

public T ValueLearningRate { get; set; }

Property Value

T

ValueLossCoefficient

public T ValueLossCoefficient { get; set; }

Property Value

T

ValueLossFunction

public ILossFunction<T> ValueLossFunction { get; set; }

Property Value

ILossFunction<T>