no code implementations • 30 Dec 2023 • S P Sharan, Francesco Pittaluga, Vijay Kumar B G, Manmohan Chandraker
Although planning is a crucial component of the autonomous driving stack, researchers have yet to develop robust planning algorithms that are capable of safely handling the diverse range of possible driving scenarios.
1 code implementation • 28 Apr 2023 • Wenqing Zheng, S P Sharan, Ajay Kumar Jaiswal, Kevin Wang, Yihan Xi, Dejia Xu, Zhangyang Wang
For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding carefully generated syntactic structures and variables in a hierarchy.
1 code implementation • 30 Dec 2022 • Wenqing Zheng, S P Sharan, Zhiwen Fan, Kevin Wang, Yihan Xi, Zhangyang Wang
Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes.
1 code implementation • 24 Oct 2022 • S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang
At the core of our proposal is a novel symbolic branching algorithm that enables the rule to be aware of the context in terms of various network conditions, eventually converting the NN policy into a symbolic tree.
no code implementations • 29 Sep 2021 • Wenqing Zheng, S P Sharan, Zhiwen Fan, Zhangyang Wang
Deep vision models are nowadays widely integrated into visual reinforcement learning (RL) to parameterize the policy networks.