Predictive Control Using Learned State Space Models via Rolling Horizon Evolution

TL;DR

This paper presents a method combining learned state space models with evolutionary planning for effective online decision making in visual navigation tasks, advancing model-based reinforcement learning.

Contribution

It introduces a novel approach integrating deep learning-based predictive models with evolutionary algorithms for planning in complex environments.

Findings

Successful online planning in visual navigation tasks

Effective long-term decision making with learned models

Demonstrated reliability in dynamic environments

Abstract

A large part of the interest in model-based reinforcement learning derives from the potential utility to acquire a forward model capable of strategic long term decision making. Assuming that an agent succeeds in learning a useful predictive model, it still requires a mechanism to harness it to generate and select among competing simulated plans. In this paper, we explore this theme combining evolutionary algorithmic planning techniques with models learned via deep learning and variational inference. We demonstrate the approach with an agent that reliably performs online planning in a set of visual navigation tasks.