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[NeurIPS 2022] Code for Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments

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Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments

Code for Enhanced Meta Reinforcement Learnin 58AB g 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

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[NeurIPS 2022] Code for Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments

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