https://github.com/idsia/rtrl-elstm
Official repository for the paper "Exploring the Promise and Limits of Real-Time Recurrent Learning" (ICLR 2024)
https://github.com/idsia/rtrl-elstm
actor-critic-methods atari deepmind-lab procgen pytorch real-time-recurrent-learning recurrent-neural-networks reinforcement-learning rtrl torchbeast
Last synced: about 2 months ago
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Official repository for the paper "Exploring the Promise and Limits of Real-Time Recurrent Learning" (ICLR 2024)
- Host: GitHub
- URL: https://github.com/idsia/rtrl-elstm
- Owner: IDSIA
- Created: 2023-05-29T14:26:59.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-26T20:04:50.000Z (over 1 year ago)
- Last Synced: 2025-04-05T09:22:58.562Z (2 months ago)
- Topics: actor-critic-methods, atari, deepmind-lab, procgen, pytorch, real-time-recurrent-learning, recurrent-neural-networks, reinforcement-learning, rtrl, torchbeast
- Language: Python
- Homepage:
- Size: 102 KB
- Stars: 11
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Real-Time Recurrent Learning with eLSTM
This is the official repository containing code for the paper:
[Exploring the Promise and Limits of Real-Time Recurrent Learning (ICLR 2024)](https://arxiv.org/abs/2305.19044)
## Contents
* `diagnostic` directory contains code for the copy task (Sec 4.1)
* `reinforcement_learning` directory contains code for the RL experiments (Sec. 4.2 and 4.3)
Please refer to the readme file in each directory for further instructions.
Separate license files can be found in each directory.
## BibTex
```
@inproceedings{irie2023exploring,
title={Exploring the Promise and Limits of Real-Time Recurrent Learning},
author={Irie, Kazuki and Gopalakrishnan, Anand and Schmidhuber, J{\"u}rgen},
booktitle={International Conference on Learning Representations (ICLR)},
address={Vienna, Austria},
month=may,
year=2024
}
```