7 papers
Understanding and Stabilizing Deep Q-Learning via Controlled Bootstrapping and Regulated Value Dynamics
Bozhou Chen, Yongyi Wang, Hanyu Liu +2
Deep Q-learning (DQL) has achieved remarkable empirical success in reinforcement learning, yet its training process remains notoriously unstable. Existing studies often attribute i…
A Change of Frame Makes the Capture Point Proprioceptive: Distillation-Free Humanoid Single-Leg Balance
Yikai Zhou, Xingyun Wang, Jieming Cui +4
Unified humanoid policies track agile whole-body motion, yet few can hold a clean single-leg stance. On the single-leg balance benchmark introduced here, eight released state-of-th…
Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer
Yongyi Wang, Hanyu Liu, Lingfeng Li +6
Decision Transformer (DT) formulates offline reinforcement learning as autoregressive sequence modeling, achieving promising results by predicting actions from a sequence of Return…
ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling
Ang Li, Xinyang Gong, Bozhou Chen +5
We present ShuttleEnv, an interactive and data-driven simulation environment for badminton, designed to support reinforcement learning and strategic behavior analysis in fast-paced…
Pareto-guided Pipeline for Distilling Featherweight AI Agents in Mobile MOBA Games
Xionghui Yang, Bozhou Chen, Yunlong Lu +8
Recent advances in game AI have demonstrated the feasibility of training agents that surpass top-tier human professionals in complex environments such as Honor of Kings (HoK), a le…
Decoupling Return-to-Go for Efficient Decision Transformer
Yongyi Wang, Hanyu Liu, Lingfeng Li +5
The Decision Transformer (DT) has established a powerful sequence modeling approach to offline reinforcement learning. It conditions its action predictions on Return-to-Go (RTG), u…
Synthetic POMDPs to Challenge Memory-Augmented RL: Memory Demand Structure Modeling
Yongyi Wang, Lingfeng Li, Bozhou Chen +5
Recent benchmarks for memory-augmented reinforcement learning (RL) have introduced partially observable Markov decision process (POMDP) environments in which agents must use histor…