ODE-based Recurrent Model-free Reinforcement Learning for POMDPs

Published: 21 Sept 2023, Last Modified: 20 Nov 2023NeurIPS 2023 posterEveryoneRevisionsBibTeX
Keywords: neural ode, POMDPs, reinforcement learning
TL;DR: We propose a novel ODE-based recurrent model combine with model-free RL framework to solve POMDP problems.
Abstract: Neural ordinary differential equations (ODEs) are widely recognized as the standard for modeling physical mechanisms, which help to perform approximate inference in unknown physical or biological environments. In partially observable (PO) environments, how to infer unseen information from raw observations puzzled the agents. By using a recurrent policy with a compact context, context-based reinforcement learning provides a flexible way to extract unobservable information from historical transitions. To help the agent extract more dynamics-related information, we present a novel ODE-based recurrent model combines with model-free reinforcement learning (RL) framework to solve partially observable Markov decision processes (POMDPs). We experimentally demonstrate the efficacy of our methods across various PO continuous control and meta-RL tasks. Furthermore, our experiments illustrate that our method is robust against irregular observations, owing to the ability of ODEs to model irregularly-sampled time series.
Supplementary Material: pdf
Submission Number: 10983