A generalized stacked reinforcement learning method for sampled systems
Abstract
A common setting of reinforcement learning (RL) is a Markov decision process (MDP) in which the environment is a stochastic discrete-time dynamical system. Whereas MDPs are suitable in such applications as video-games or puzzles, physical systems are time-continuous. A general variant of RL is of digital format, where updates of the value (or cost) and policy are performed at discrete moments in time. The agent-environment loop then amounts to a sampled system, whereby sample-and-hold is a specific case. In this paper, we propose and benchmark two RL methods suitable for sampled systems. Specifically, we hybridize model-predictive control (MPC) with critics learning the optimal Q- and value (or cost-to-go) function. Optimality is analyzed and performance comparison is done in an experimental case study with a mobile robot.
Cite
@article{arxiv.2108.10392,
title = {A generalized stacked reinforcement learning method for sampled systems},
author = {Pavel Osinenko and Dmitrii Dobriborsci and Grigory Yaremenko and Georgiy Malaniya},
journal= {arXiv preprint arXiv:2108.10392},
year = {2022}
}