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: consistency of latent information within sessions, session masking, and 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, demonstrating that DynaMITE-RL significantly outperforms state-of-the-art baselines in sample efficiency and inference returns.
@article{arxiv.2402.15957,
title = {DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning},
author = {Anthony Liang and Guy Tennenholtz and Chih-wei Hsu and Yinlam Chow and Erdem Bıyık and Craig Boutilier},
journal= {arXiv preprint arXiv:2402.15957},
year = {2024}
}