DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning

arXiv:2402.15957v1 Announce Type: new
Abstract: 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 …

abstract approximate inference arxiv cs.lg dynamic environments inference key meta reinforcement reinforcement learning state temporal type