OpenaAI Gym robot reaching environment with PyBullet.

This a (sort-of) port of the fetch_reach_v1 environment featured in this article which I wanted to try for my RL experiments but I did not have a Mujoco license.

Anyway, here is the env in action, agent was trained with PPO.

	Agent performance at different training episodes

Install

Install with pip:

git clone https://github.com/mcarfagno/gym-panda-reach
cd gym-panda-reach
pip install .

Basic Usage

Example running of the environment:

import gym
import gym_panda_reach
env = gym.make('panda-reach-v0')
env.reset()
env.reward_type = "sparse" #default is "dense"
for _ in range(100):
    env.render()
    obs, reward, done, info = env.step(
        env.action_space.sample()) # take a random action
env.close()

References and Special Thanks:

• Mahyar Abdeetedal -> awesome tutorial and inspiration
• OpenAI -> original environment
• PyBullet -> my favourite robotics simulator, sorry gazebo