2 citations · 3 across the 5 of their papers we have counts for
5 papers
Learning Human-Like RL Agents Through Trajectory Optimization With Action Quantization
Jian-Ting Guo, Yu-Cheng Chen, Ping-Chun Hsieh +4
Human-like agents have long been one of the goals in pursuing artificial intelligence. Although reinforcement learning (RL) has achieved superhuman performance in many domains, rel…
PPO-Clip Attains Global Optimality: Towards Deeper Understandings of Clipping
Nai-Chieh Huang, Ping-Chun Hsieh, Kuo-Hao Ho +1
Proximal Policy Optimization algorithm employing a clipped surrogate objective (PPO-Clip) is a prominent exemplar of the policy optimization methods. However, despite its remarkabl…
Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem
Kuo-Hao Ho, Ruei-Yu Jheng, Ji-Han Wu +4
Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing, and flexible JSP (FJSP) is also a common variant. Since they…
Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games
Kuo-Hao Ho, Ping-Chun Hsieh, Chiu-Chou Lin +3
In this paper, we propose a new approach called Adaptive Behavioral Costs in Reinforcement Learning (ABC-RL) for training a human-like agent with competitive strength. While deep r…
Neural PPO-Clip Attains Global Optimality: A Hinge Loss Perspective
Nai-Chieh Huang, Ping-Chun Hsieh, Kuo-Hao Ho +4
Policy optimization is a fundamental principle for designing reinforcement learning algorithms, and one example is the proximal policy optimization algorithm with a clipped surroga…