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Publikation

Velocity-History-Based Soft Actor-Critic: Tackling IROS'24 Competition AI Olympics with RealAIGym

T.L. Faust; H. Maraqten; E. Aghadavoodi; B. Belousov; Jan Peters
In: IROS'24 Competition AI Olympics with RealAIGym. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2024), IROS, 2024.

Zusammenfassung

The “AI Olympics with RealAIGym” competition challenges participants to stabilize chaotic underactuated dynamical systems with advanced control algorithms. In this paper, we present a novel solution submitted to IROS’24 competition, which builds upon Soft Actor-Critic (SAC), a popular model-free entropy-regularized Reinforcement Learning (RL) algorithm. We add a ‘context’ vector to the state, which encodes the immediate history via a Convolutional Neural Network (CNN) to counteract the unmodeled effects on the real system. Our method achieves high performance scores and competitive robustness scores on both tracks of the competition: Pendubot and Acrobot.