Forger - Reinforcement Learning Library in Rust

Introduction

Forger is a Reinforcement Learning (RL) library in Rust, offering a robust and efficient framework for implementing RL algorithms. It features a modular design with components for agents, environments, policies, and utilities, facilitating easy experimentation and development of RL models.

Features

• Modular Components: Includes agents, environments, and policies as separate modules.
• Efficient and Safe: Built in Rust, ensuring high performance and safety.
• Customizable Environments: Provides a framework to create and manage different RL environments.
• Flexible Agent Implementations: Supports various agent strategies and learning algorithms.
• Extensible Policy Framework: Allows for the implementation of diverse action selection policies.

Modules

1. Policy (policy):

   • Defines the interface for action selection policies.
   • Includes an implementation of Epsilon Greedy (with Decay) Policy.

2. Agent (agent):

   • Outlines the structure for RL agents.
   • Implements Value Iteration - Every Visit Monte Carlo (VEveryVisitMC) and Q-Learning - Every Visit Monte Carlo (QEveryVisitMC).

3. Environment (env):

   • Provides the Env trait to define RL environments.
   • Contains LineWorld, a simple linear world environment for experimentation.

4. Prelude (prelude):

   • Exports commonly used items from the env, agent, and policy modules for convenient access.

Getting Started

Prerequisites

• Rust Programming Environment

Installation

In your project directory, run the following command:

cargo add forger

Basic Usage

[Include a basic usage example that demonstrates creating an environment, an agent, and utilizing a policy to interact within the environment.]

Examples

1. Monte Carlo with Epsilon Decay in LineWorld:
   • Demonstrates the use of the Q-Learning Every Visit Monte Carlo (QEveryVisitMC) agent with an Epsilon Greedy Policy (with decay) in the LineWorld environment.
   • Illustrates the process of running multiple episodes, selecting actions, updating the agent, and decaying the epsilon value over time.
   • Includes data gathering and exporting functionality, showcasing practical aspects of RL experiments.

Contributing

Contributions to Forger are welcome! If you'd like to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome.

License

Forger is licensed under the MIT License or the Apache 2.0 License.