5 papers
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…
Jump-Start Reinforcement Learning with Self-Evolving Priors for Extreme Monopedal Locomotion
Ziang Zheng, Guojian Zhan, Shiqi Liu +3
Reinforcement learning (RL) has shown great potential in enabling quadruped robots to perform agile locomotion. However, directly training policies to simultaneously handle dual ex…
Transferable Latent-to-Latent Locomotion Policy for Efficient and Versatile Motion Control of Diverse Legged Robots
Ziang Zheng, Guojian Zhan, Bin Shuai +4
Reinforcement learning (RL) has demonstrated remarkable capability in acquiring robot skills, but learning each new skill still requires substantial data collection for training. T…
Predictive Lagrangian Optimization for Constrained Reinforcement Learning
Tianqi Zhang, Puzhen Yuan, Guojian Zhan +6
Constrained optimization is popularly seen in reinforcement learning for addressing complex control tasks. From the perspective of dynamic system, iteratively solving a constrained…
An Explicit Discrete-Time Dynamic Vehicle Model with Assured Numerical Stability
Guojian Zhan, Qiang Ge, Haoyu Gao +3
Numerical stability is of great significance for discrete-time dynamic vehicle model. Among the unstable factors, low-speed singularity stands out as one of the most challenging is…