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jangwon-kim-cocel/
BPQL

Name: BPQL
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reinforcement-learning

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Belief Projection-Based Q-Learning

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[NeurIPS 2023] Official Implementation of Belief Projection-Based Q-Learning (BPQL)

This repository contains the PyTorch implementation of BPQL introduced in the paper: Belief Projection-Based Reinforcement Learning for Environments with Delayed Feedback by Jangwon Kim et al., presented at Advances in Neural Information Processing Systems (NeurIPS), 2023.

📄 Paper Link

You can see the paper here: https://proceedings.neurips.cc/paper_files/paper/2023/hash/0252a434b18962c94910c07cd9a7fecc-Abstract-Conference.html

🚀 Achieves S.O.T.A. Performance, Yet Very Simple to Implement

Supports both observation delay, action delay, and their combination
Performance Plot ⬇️

▶️ How to Run?

Option 1: Run the script file

>chmod +x run.sh
>./run.sh

Option 2: Run main.py with arguments

python main.py --env-name HalfCheetah-v3 --random-seed 2023 --obs-delayed-steps 5 --act-delayed-steps 4 --max-step 1000000

✅Test Environment

python == 3.8.10
gym == 0.26.2
mujoco_py == 2.1.2.14
pytorch == 2.1.0
numpy == 1.24.3

📚 Citation Example

@inproceedings{kim2023cocel,
   author = {Kim, Jangwon and Kim, Hangyeol and Kang, Jiwook and Baek, Jongchan and Han, Soohee},
   booktitle = {Advances in Neural Information Processing Systems},
   pages = {678--696},
   title = {Belief Projection-Based Reinforcement Learning for Environments with Delayed Feedback},
   volume = {36},
   year = {2023}
}

jangwon-kim-cocel/BPQL