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arXiv · 2312.10328

Deriving Rewards for Reinforcement Learning from Symbolic Behaviour Descriptions of Bipedal Walking

Daniel Harnack·Christoph Lüth·Lukas Gross·Shivesh Kumar·Frank Kirchner
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Abstract

Generating physical movement behaviours from their symbolic description is a long-standing challenge in artificial intelligence (AI) and robotics, requiring insights into numerical optimization methods as well as into formalizations from symbolic AI and reasoning. In this paper, a novel approach to finding a reward function from a symbolic description is proposed. The intended system behaviour is modelled as a hybrid automaton, which reduces the system state space to allow more efficient reinforcement learning. The approach is applied to bipedal walking, by modelling the walking robot as a hybrid automaton over state space orthants, and used with the compass walker to derive a reward that incentivizes following the hybrid automaton cycle. As a result, training times of reinforcement learning controllers are reduced while final walking speed is increased. The approach can serve as a blueprint how to generate reward functions from symbolic AI and reasoning.

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Daniel Harnack, Christoph Lüth, Lukas Gross, Shivesh Kumar, Frank Kirchner. 2023-12-16. Deriving Rewards for Reinforcement Learning from Symbolic Behaviour Descriptions of Bipedal Walking. https://arxiv.org/abs/2312.10328

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