3 papers
cs.LG2026
Lifted Bellman Linear Programming for Offline Reinforcement Learning
Hyukjun Yang, Jongchan Park, Narim Jeong +1
Offline reinforcement learning (RL) typically trains a critic by minimizing a regression loss against bootstrapped value targets stabilized by target networks with exponential movi…
stat.ML2026
Scalable Reinforcement Learning via Adaptive Batch Scaling
Jongchan Park
Conventional wisdom holds that large-batch training is fundamentally incompatible with Reinforcement Learning (RL) - beyond a modest threshold, increasing batch sizes typically yie…
cs.AI2025
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning
Jongchan Park, Mingyu Park, Donghwan Lee
Offline reinforcement learning (RL) aims to learn a policy from a static dataset without further interactions with the environment. Collecting sufficiently large datasets for offli…