A complete port of openai/gym to C#.
** WORK IN PROGRESS **

OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This is the gym open-source library, which gives you access to a standardized set of environments.

### For gym's abstract classes for RL, install:
PM> Install-Package Gym.NET

### For implemented environments, install:
PM> Install-Package Gym.NET.Environments

The following example runs and renders cartpole-v1 environment.

using NumSharp;
using SixLabors.ImageSharp;
using Gym.Environments;
using Gym.Environments.Envs.Classic;
using Gym.Rendering.WinForm;

CartPoleEnv cp = new CartPoleEnv(WinFormEnvViewer.Factory); //or AvaloniaEnvViewer.Factory
bool done = true;
for (int i = 0; i < 100_000; i++)
{
    if (done)
    {
        NDArray observation = cp.Reset();
        done = false;
    }
    else
    {
        var (observation, reward, _done, information) = cp.Step((i % 2)); //we switch between moving left and right
        done = _done;
        //do something with the reward and observation.
    }

    SixLabors.ImageSharp.Image img = cp.Render(); //returns the image that was rendered.
    Thread.Sleep(15); //this is to prevent it from finishing instantly !
}

• Implement Spaces

  • Space (base class)
  • Box
  • Discrete
  • multi.*.py

• Implement Env base classes

  • Env(object)
  • GoalEnv(Env)

• Implement environments
  To run an environment, see Gym.Tests

  • Convert Gym.Environments to a net-standard project.
  • classics
    • CartPole-v1
      • Compare visually against python's version
    • walker2d_v3
    • acrobot
    • continuous_mountain_car
    • mountain_car
    • pendulum
    • rendering
  • Mujco
    • ant_v3
    • half_cheetah_v3
    • hopper_v3
    • humanoid_v3
    • humanoidstandup
    • inverted_double_pendulum
    • inverted_pendulum
    • mujoco_env
    • pusher
    • reacher
    • striker
    • swimmer_v3
    • thrower
  • box2d
    • bipedal_walker
    • car_dynamics
    • car_racing
    • lunar_lander
  • atari