RLMatrix 0.1.0

🚀 RL Matrix - Elevate Your .NET Reinforcement Learning Experience with TorchSharp!

Dive into the future of type-safe Reinforcement Learning with RL Matrix, powered by the might of TorchSharp. RL Matrix stands out as a user-friendly toolkit offering a collection of RL algorithms—primed for plug, play, and prosper!

🌟 Featured Algorithms:

• PPO
• DQN

🎯 What Sparks RL Matrix?

While embarking on my RL journey, I sensed a gap in the reinforcement learning world even with TorchSharp's solid foundation. It struck me—C# is the ideal choice for RL outside research circles, thanks to its pristine and intuitive coding experience. No more guessing games in environment or agent building!

With RL Matrix, our vision is to offer a seamless experience. By simply incorporating the IEnvironment interface, you're equipped to rapidly craft and unleash Reinforcement Learning Agents (RL Agents). Switching between algorithms? A breeze! It’s our nod to the elegance of Matlab's toolkit methodology.

🚀 Getting Started:

Peek into the /examples/ directory for illustrative code. But to give you a quick start:

1. Craft an IEnvironment class: Comply with reinforcement learning guidelines, defining your observation shapes and action count:

public class CartPole : IEnvironment<float[]>
{
  public int stepCounter { get; set; }
  public int maxSteps { get; set; }
  public bool isDone { get; set; }
  public OneOf<int, (int, int)> stateSize { get; set; }
  public int actionSize { get; set; }

  CartPoleEnv myEnv;

  private float[] myState;

  public CartPole()
  {
      Initialise();
  }

  public float[] GetCurrentState()
  {
      if (myState == null)
          myState = new float[4] {0,0,0,0};
      return myState;
  }

  public void Initialise()
  {
      myEnv = new CartPoleEnv(WinFormEnvViewer.Factory);
      stepCounter = 0;
      maxSteps = 100000;
      stateSize = myEnv.ObservationSpace.Shape.Size;
      actionSize = myEnv.ActionSpace.Shape.Size;
      myEnv.Reset();
      isDone = false; 
  }

  public void Reset()
  {
  //For instance:
      myEnv.Reset();
      isDone = false;
      stepCounter = 0;
  }

  public float Step(int actionId)
  {
  //Whatever step logic, returns reward
      return reward;
  }
}

2. Agent Instance & Training: Spawn an agent for your environment and ignite the TrainEpisode method:

var opts = new DQNAgentOptions(batchSize: 64, memorySize: 10000, gamma: 0.99f, epsStart: 1f, epsEnd: 0.05f, epsDecay: 50f, tau: 0.005f, lr: 1e-4f, displayPlot: myChart);
var env = new CartPole();
var myAgent = new D2QNAgent<float[,]>(opts, env);

for (int i = 0; i < 400; i++)
{
    myAgent.TrainEpisode();
}

📌 Current Roadmap:

Adapting data to 1D (feed-forward networks) and 2D (convolutional networks) without source modifications. Support for discrete actions only. As we innovate, anticipate breaking changes. We'll keep you in the loop!

💌 Get in Touch:

Questions? Ideas? Collaborations? Drop a line at: 📧 contact@exmachinasoft.com

🤝 Join the Journey:

We believe in collective brilliance! If you're inspired to enhance RL Matrix, please open an issue or PR. Let’s shape the future of RL together!