hari-sikchi / rank-game

SOTA algorithms for imitation learning (LfD and LfO) - Ranking algorithms for imitation learning (TMLR 2023)

A Ranking Game for Imitation Learning

SOTA algorithms for learning from demonstration + (optionally) learning from preferences

Accepted in Transactions of Machine Learning Research (TMLR) 2023.

Harshit Sikchi,   Akanksha Saran,  Wonjoon Goo,   Scott Niekum

Paper ,   Blog Post

Citing

If you find this code useful, please reference in your paper:

@article{
sikchi2022a,
title={A Ranking Game for Imitation Learning},
author={Harshit Sikchi and Akanksha Saran and Wonjoon Goo and Scott Niekum},
journal={Transactions on Machine Learning Research},
year={2022},
url={https://openreview.net/forum?id=d3rHk4VAf0},
note={}
}

Install

via Conda

conda env create -f environment.yml

Install D4RL from source here: https://github.com/rail-berkeley/d4rl (In setup.py comment out dm_control)

Download expert data

Download and extract expert data and preferences data inside the rank-game/expert_data/ folder from this [link](https://drive.google.com/drive/folders/1KJayG61KqiHqtRbxnUrGPBxTX2oDccSn?usp=sharing)

File Structure

• rank-game (Core method)
• • Training code (Online Imitation Learning from Observations): rank_game_lfo.py
• • Training code (Online Imitation Learning from Demonstrations): rank_game_lfd.py
• • Ranking Loss (PAL/RAL): reward_agent/ranking_losses.py
• • Policy Agent: policy_agent/sac.py
• Environments: envs/
• Configurations: configs/
• Expert Data: expert_data/ (needs to downloaded from <>)

Instructions

• All the experiments are to be run under the root folder.
• Config files in configs/ are used to specify hyperparameters for each environment.
• Please keep all the other values in yml files consistent with hyperparamters given in paper to reproduce the results in our paper.

Running Experiments

Imitation Learning with rank-game

The following command will reproduce the results in our paper without changing the config:

python rank_game_lfo.py --config=<configs/env_name.yml> --seed=<seed>  --exp_name=<experiment name> --obj=<rank-pal-auto/rank-ral-auto> --regularization=${4} --expert_episodes=${5}

Note that as indicated in the paper, training the reward function until the cross-validation loss saturates also works well but takes longer to train. Hence, our default configs train the reward function for iterations that scale linearly with the preference dataset size.