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

1. Clone the repository

git clone git@github.com:ALRhub/alr_envs.git

2. Go to the folder

cd alr_envs

3. Install with

pip install -e . 

4. Use (see example.py):

import gym

env = gym.make('alr_envs:SimpleReacher-v0')
state = env.reset()

for i in range(10000):
    state, reward, done, info = env.step(env.action_space.sample())
    if i % 5 == 0:
        env.render()

    if done:
        state = env.reset()