A port of openai/gym to C#.
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 PM> Install-Package Gym.NET.Rendering.Avalonia PM> Install-Package Gym.NET.Rendering.WinForm
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 ! } cp.Close();
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Implement Spaces
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Space(base class) -
Box -
Discrete -
multi.*.py
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Implement Env base classes
- Env(object)
- GoalEnv(Env)
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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
- CartPole-v1
- 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