@inproceedings{
liang2024dynamiterl,
title={Dyna{MITE}-{RL}: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning},
author={Anthony Liang and Guy Tennenholtz and ChihWei Hsu and Yinlam Chow and Erdem Biyik and Craig Boutilier},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OPrPegYIZo}
}
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates. We model episode sessions---parts of the episode where the latent state is fixed---and propose three key modifications to existing meta-RL methods: (i) consistency of latent information within sessions, (ii) session masking, and (iii) prior latent conditioning. We demonstrate the importance of these modifications in various domains, ranging from discrete Gridworld environments to continuous-control and simulated robot assistive tasks, illustrating the efficacy of DynaMITE-RL over state-of-the-art baselines in both online and offline RL settings.
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
Anthony Liang Guy Tennenholtz ChihWei Hsu Yinlam Chow Erdem Biyik Craig Boutilier