Search Results for author: Haitong Ma

Found 6 papers, 3 papers with code

Reachability Constrained Reinforcement Learning

no code implementations16 May 2022 Dongjie Yu, Haitong Ma, Shengbo Eben Li, Jianyu Chen

We characterize the feasible set by the established self-consistency condition, then a safety value function can be learned and used as constraints in CRL.

reinforcement-learning

Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning

no code implementations25 Nov 2021 Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Wenchao Sun, Jianyu Chen

Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.

reinforcement-learning

Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning

no code implementations15 Nov 2021 Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Jianyu Chen

This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.

reinforcement-learning

Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety

1 code implementation22 May 2021 Haitong Ma, Yang Guan, Shegnbo Eben Li, Xiangteng Zhang, Sifa Zheng, Jianyu Chen

The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks.

reinforcement-learning Safe Exploration +1

Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence

2 code implementations18 Mar 2021 Yang Guan, Yangang Ren, Qi Sun, Shengbo Eben Li, Haitong Ma, Jingliang Duan, Yifan Dai, Bo Cheng

In this paper, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically.

Autonomous Driving Model-based Reinforcement Learning +1

Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function

1 code implementation2 Mar 2021 Haitong Ma, Jianyu Chen, Shengbo Eben Li, Ziyu Lin, Yang Guan, Yangang Ren, Sifa Zheng

Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks, like autonomous driving.

Autonomous Driving reinforcement-learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.