Multi_Task_Robot_Learning

Goal relabelling with intrinsic motivation for multi-task autonomous robot learning

PreRequisites :

Deep Reinforcement Learning (UC Berkeley CS 285 by Prof. Sergey Levine, Chelsea Finn’s advisor) https://www.youtube.com/playlist?list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH

"RND novelty rewards: https://arxiv.org/abs/1810.12894

Example of goal-conditioned autonomous robot learning: https://www.deepmind.com/blog/robocat-a-self-improving-robotic-agent"

Useful Simulations:

1. Meta World: https://meta-world.github.io/
2. Mujoco: https://mujoco.org/
3. EARL: https://architsharma97.github.io/earl_benchmark/
4. GYM: https://www.gymlibrary.dev/index.html

Project board

The project task board can be find here: https://github.com/users/mrgares/projects/1/views/1

Dependencies

For this project a docker container was created. Please follow these steps to setup the environment (you should be in the same path as the dockerfile):

1. Build Dockerfile

   docker build -t robomimic:v1.0 .

2. Create container (this assumes you want to run the project on GPU and with a DISPLAY)

   docker run --name robomimic_env -p 8888:8888 -p 5252:5252 -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v `pwd`:/project -it --env QT_X11_NO_MITSHM=1 --device /dev/dri --privileged --gpus all --ipc=host robomimic:v1.0

• Ports open to work with this container are set to 5151 and 5252 if you require different ports feel free to modify them.

3. Everytime we want to run container

   docker start robomimic_env

   docker exec -it robomimic_env bash