Closing the Planning–Learning Loop With Application to Autonomous Driving

Author:

Cai Panpan1ORCID,Hsu David2ORCID

Affiliation:

1. Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China

2. School of Computing and the Smart Systems Institute, National University of Singapore, Singapore

Funder

National Research Foundation (NRF), Singapore

DSO National Laboratories

AI Singapore Program AISG

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference44 articles.

1. A Framework for Multi-Agent UAV Exploration and Target-Finding in GPS-Denied and Partially Observable Environments

2. Soft actor-critic for discrete action settings;christodoulou,2019

3. Online Planning for Target Object Search in Clutter under Partial Observability

4. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor;haarnoja;Proc Int Conf Mach Learn,0

5. Value Iteration Networks

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1. Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving;IEEE Robotics and Automation Letters;2024-08

2. MBAPPE: MCTS-Built-Around Prediction for Planning Explicitly;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

3. Constrained Hierarchical Monte Carlo Belief-State Planning;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

4. Reward Machine Reinforcement Learning for Autonomous Highway Driving: An Unified Framework for Safety and Performance;2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI);2023-10-27

5. Real-Time Motion Planning Framework for Autonomous Vehicles with Learned Committed Trajectory Distribution;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

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