3 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network
Qian Chen, Junqiao Zhao, Hongtu Zhou +4
Long-horizon, sparse-reward tasks pose a fundamental challenge for reinforcement learning, since single-step TD learning suffers from bootstrapping error accumulation across succes…
Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning
Qingjun Wang, Hongtu Zhou, Hang Yu +5
Offline reinforcement learning (RL) faces a critical challenge of overestimating the value of out-of-distribution (OOD) actions. Existing methods mitigate this issue by penalizing…
Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning
Chang Huang, Shatong Zhu, Junqiao Zhao +6
Value function factorization is widely used in cooperative multi-agent reinforcement learning (MARL). Existing approaches often impose monotonicity constraints between the joint ac…
How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization
Hai Zhang, Hang Yu, Junqiao Zhao +5
Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high couplin…
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery
Xiao Zhang, Hai Zhang, Hongtu Zhou +4
Safety is one of the main challenges in applying reinforcement learning to realistic environmental tasks. To ensure safety during and after training process, existing methods tend…