ALR Custom Environments
This repository collects custom RL envs not included in Suits like OpenAI gym, rllab, etc. Creating a custom (Mujoco) gym environement can be done according to this guide: https://github.com/openai/gym/blob/master/docs/creating-environments.md
Environments
Currently we have the following environements:
Mujoco
| Name | Description |
|---|---|
ALRReacher-v0 |
modification (5 links) of Mujoco Gym's Reacher (2 links) |
Classic Control
| Name | Description |
|---|---|
SimpleReacher-v0 |
Simple Reaching Task without any physics simulation. Returns no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory. |
INSTALL
- Clone the repository
git clone git@github.com:ALRhub/alr_envs.git
- Go to the folder
cd alr_envs
- Install with
pip install -e .
- Use (see example.py):
env = gym.make('alr_envs:SimpleReacher-v0')