5 papers
Average-Reward Maximum Entropy Reinforcement Learning for Global Policy in Double Pendulum Tasks
Jean Seong Bjorn Choe, Bumkyu Choi, Jong-kook Kim
This report presents our reinforcement learning-based approach for the swing-up and stabilisation tasks of the acrobot and pendubot, tailored specifcially to the updated guidelines…
Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition
Felix Wiebe, Niccolò Turcato, Alberto Dalla Libera +17
In the field of robotics many different approaches ranging from classical planning over optimal control to reinforcement learning (RL) are developed and borrowed from other fields…
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks
Jean Seong Bjorn Choe, Bumkyu Choi, Jong-kook Kim
This report presents a solution for the swing-up and stabilisation tasks of the acrobot and the pendubot, developed for the AI Olympics competition at IROS 2024. Our approach emplo…
Maximum Entropy On-Policy Actor-Critic via Entropy Advantage Estimation
Jean Seong Bjorn Choe, Jong-Kook Kim
Entropy Regularisation is a widely adopted technique that enhances policy optimisation performance and stability. A notable form of entropy regularisation is augmenting the objecti…
The Bid Picture: Auction-Inspired Multi-player Generative Adversarial Networks Training
Joo Yong Shim, Jean Seong Bjorn Choe, Jong-Kook Kim
This article proposes auction-inspired multi-player generative adversarial networks training, which mitigates the mode collapse problem of GANs. Mode collapse occurs when an over-f…