Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning
Abstract
This paper proposes a novel scoring function for the planning module of MPC-based reinforcement learning methods to address the inherent bias of using the reward function to score trajectories. The proposed method enhances the learning efficiency of existing MPC-based MBRL methods using the discounted sum of values. The method utilizes optimal trajectories to guide policy learning and updates its state-action value function based on real-world and augmented onboard data. The learning efficiency of the proposed method is evaluated in selected MuJoCo Gym environments as well as in learning locomotion skills for a simulated model of the Cassie robot. The results demonstrate that the proposed method outperforms the current state-of-the-art algorithms in terms of learning efficiency and average reward return.
Cite
@article{arxiv.2209.08169,
title = {Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning},
author = {Mehran Raisi and Amirhossein Noohian and Luc Mccutcheon and Saber Fallah},
journal= {arXiv preprint arXiv:2209.08169},
year = {2023}
}