1 citations · 2 across the 3 of their papers we have counts for
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Gain Tuning Is Not What You Need: Reward Gain Adaptation for Constrained Locomotion Learning
Arthicha Srisuchinnawong, Poramate Manoonpong
Existing robot locomotion learning techniques rely heavily on the offline selection of proper reward weighting gains and cannot guarantee constraint satisfaction (i.e., constraint…
Growable and Interpretable Neural Control with Online Continual Learning for Autonomous Lifelong Locomotion Learning Machines
Arthicha Srisuchinnawong, Poramate Manoonpong
Continual locomotion learning faces four challenges: incomprehensibility, sample inefficiency, lack of knowledge exploitation, and catastrophic forgetting. Thus, this work introduc…
An Interpretable Neural Control Network with Adaptable Online Learning for Sample Efficient Robot Locomotion Learning
Arthicha Srisuchinnawong, Poramate Manoonpong
Robot locomotion learning using reinforcement learning suffers from training sample inefficiency and exhibits the non-understandable/black-box nature. Thus, this work presents a no…