MY IMPLEMENTATION of Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments

This is my own implementation of EMRLD.

Code for Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments (NeurIPS 2022)

This code is based on a public meta-rl github repository learn2learn

Video of real world demonstrations on a TurtleBot

To run expirements you will need the the following packages:

• cherry-rl 0.1.4
• tensorboard
• learn2learn
• mujoco-py 2.0.2.13
• torch 1.10.0
• gym 0.21.1

To run the experiments, simply execute the following commands,

Particle2D Navigation:

python EMRLD_PN.py    --exp-num i 
python EMRLD-WS_PN.py --exp-num i

Two Wheeled Locomotion:

python EMRLD_TW.py    --exp-num i   
python EMRLD-WS_TW.py --exp-num i

HalfCheetah Forward-Backward:

python EMRLD_HC.py    --exp-num i   
python EMRLD-WS_HC.py --exp-num i

Where i = 1 is for Optimal demonstration data, and i = 2 is for sub-optimal demonstration data