13 papers
FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion
Guanchen Lu, Yajuan Dun, Yi Zhou +4
Scalable reinforcement learning has popularized high-throughput sampling architectures, which significantly compresses the training time for off-policy methods in robotic locomotio…
STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens
Shiqi Liu, Zeyu He, Guojian Zhan +10
Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regu…
On the Optimization Landscape of Observer-based Dynamic Linear Quadratic Control
Jingliang Duan, Jie Li, Yinsong Ma +5
Understanding the optimization landscape of linear quadratic regulation (LQR) problems is fundamental to the design of efficient reinforcement learning solutions. Recent work has m…
Off-policy Reinforcement Learning with Model-based Exploration Augmentation
Likun Wang, Xiangteng Zhang, Yinuo Wang +5
Exploration is fundamental to reinforcement learning (RL), as it determines how effectively an agent discovers and exploits the underlying structure of its environment to achieve o…
Mind Your Entropy: From Maximum Entropy to Trajectory Entropy-Constrained RL
Guojian Zhan, Likun Wang, Pengcheng Wang +4
Maximum entropy has become a mainstream off-policy reinforcement learning (RL) framework for balancing exploitation and exploration. However, two bottlenecks still limit further pe…
Enhanced DACER Algorithm with High Diffusion Efficiency
Yinuo Wang, Likun Wang, Mining Tan +11
Due to their expressive capacity, diffusion models have shown great promise in offline RL and imitation learning. Diffusion Actor-Critic with Entropy Regulator (DACER) extended thi…