Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning

• URL: http://arxiv.org/abs/2209.08169v2
• Date: Wed, 19 Jul 2023 16:45:18 GMT
• Title: Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning
• Authors: Mehran Raisi, Amirhossein Noohian, Luc Mccutcheon, Saber Fallah
• Abstract summary: This paper proposes a novel scoring function for the planning module of MPC-based reinforcement learning methods. The proposed method enhances the learning efficiency of existing MPC-based MBRL methods using the discounted sum of values. The results demonstrate that the proposed method outperforms the current state-of-the-art algorithms in terms of learning efficiency and average reward return.
• Score: 4.473327661758546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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