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https://github.com/SciSharp/Gym.NET
openai/gym's popular toolkit for developing and comparing reinforcement learning algorithms port to C#.
https://github.com/SciSharp/Gym.NET
gym machine-learning openai reinforcement-learning scisharp
Last synced: 3 months ago
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openai/gym's popular toolkit for developing and comparing reinforcement learning algorithms port to C#.
- Host: GitHub
- URL: https://github.com/SciSharp/Gym.NET
- Owner: SciSharp
- License: apache-2.0
- Created: 2019-06-27T01:59:27.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2023-04-06T21:48:13.000Z (about 1 year ago)
- Last Synced: 2024-01-11T21:56:55.010Z (5 months ago)
- Topics: gym, machine-learning, openai, reinforcement-learning, scisharp
- Language: C#
- Homepage:
- Size: 3.91 MB
- Stars: 110
- Watchers: 17
- Forks: 15
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-dotnet-datascience - Gym.NET - A complete 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. Work in progress. (Reinforcement Learning)
README
# Gym.NET
[](https://www.nuget.org/packages/Gym.NET)
A port of [openai/gym](https://github.com/openai/gym) to C#.
##### openai/gym
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.
## Installation
```sh
### 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
```
## Example
The following example runs and renders cartpole-v1 environment.
```C#
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();
```
## Roadmap
- Implement [Spaces](https://github.com/openai/gym/tree/master/gym/spaces)
- [X] `Space` (base class)
- [X] `Box`
- [X] `Discrete`
- [ ] `multi.*.py`
- Implement [Env](https://github.com/openai/gym/blob/master/gym/core.py) base classes
- [X] Env(object)
- [ ] GoalEnv(Env)
- Implement environments
To run an environment, see [Gym.Tests](./tests/Gym.Tests/)
- [X] Convert Gym.Environments to a net-standard project.
- [ ] classics
- [X] 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
- [X] lunar_lander
- [ ] atari