4 papers · 1 filter
Absolute State-wise Constrained Policy Optimization: High-Probability State-wise Constraints Satisfaction
Weiye Zhao, Feihan Li, Yifan Sun +4
Enforcing state-wise safety constraints is critical for the application of reinforcement learning (RL) in real-world problems, such as autonomous driving and robot manipulation. Ho…
GUARD: A Safe Reinforcement Learning Benchmark
Weiye Zhao, Yifan Sun, Feihan Li +4
Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-critical real-world applications, such as autonomous driving, human-robot interactio…
State-wise Constrained Policy Optimization
Weiye Zhao, Rui Chen, Yifan Sun +2
Reinforcement Learning (RL) algorithms have shown tremendous success in simulation environments, but their application to real-world problems faces significant challenges, with saf…
Absolute Policy Optimization
Weiye Zhao, Feihan Li, Yifan Sun +3
In recent years, trust region on-policy reinforcement learning has achieved impressive results in addressing complex control tasks and gaming scenarios. However, contemporary state…