CDMCH/gym-fetch-stack

Python

Gym Fetch Stack

Mujoco Block Stacking Gym Reinforcement Learning Environments.
(Modified from OpenAI Robotics Gym Environments)

These environments are made for use with DDPG with Curiosity Driven Exploration and Multi-Criteria Hindsight Experience Replay

Setup

(You need a Mujoco License. Follow the instructions to set up Mujoco here.)

In the gym_fetch_stack root dir, use

pip install -e .

In your python code, use:

import gym
import gym_fetch_stack
env = gym.make("FetchStack2Stage3-v1")

Curriculum Stages

We use three curriculum stages to train an agent to stack blocks:

Stage 1: basic manipulation tasks without having to create stacks
Stage 2: stacking blocks where the environment is initialized at various stages of completion
Stage 3: stacking blocks where all blocks all initialized away from their target locations

A video example of the different stages can found here.

Environments Available:

Incremental Rewards (Sparse reward for each correctly placed block):

(FetchStacki has i blocks in the environment to stack)

FetchStack2Stage1-v1
FetchStack3Stage1-v1
FetchStack4Stage1-v1
FetchStack2Stage2-v1
FetchStack3Stage2-v1
FetchStack4Stage2-v1
FetchStack2Stage3-v1
FetchStack3Stage3-v1
FetchStack4Stage3-v1

Binary Rewards (Single Sparse reward for completed stack):

FetchStack2SparseStage1-v1
FetchStack3SparseStage1-v1
FetchStack4SparseStage1-v1
FetchStack2SparseStage2-v1
FetchStack3SparseStage2-v1
FetchStack4SparseStage2-v1
FetchStack2SparseStage3-v1
FetchStack3SparseStage3-v1
FetchStack4SparseStage3-v1