no code implementations • 2 Mar 2023 • Zikang Xiong, Daniel Lawson, Joe Eappen, Ahmed H. Qureshi, Suresh Jagannathan
Synthesizing planning and control policies in robotics is a fundamental task, further complicated by factors such as complex logic specifications and high-dimensional robot dynamics.
Hierarchical Reinforcement Learning reinforcement-learning +2
1 code implementation • 28 Jun 2022 • Joe Eappen, Suresh Jagannathan
While notable progress has been made in specifying and learning objectives for general cyber-physical systems, applying these methods to distributed multi-agent systems still pose significant challenges.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 14 Jun 2022 • Zikang Xiong, Joe Eappen, He Zhu, Suresh Jagannathan
We focus our attention on well-trained deterministic and stochastic neural network policies in the context of continuous control benchmarks subject to four well-studied observation space adversarial attacks.
1 code implementation • 2 Mar 2022 • Zikang Xiong, Joe Eappen, Ahmed H. Qureshi, Suresh Jagannathan
Model-free Deep Reinforcement Learning (DRL) controllers have demonstrated promising results on various challenging non-linear control tasks.
no code implementations • 11 Jun 2020 • Zikang Xiong, Joe Eappen, He Zhu, Suresh Jagannathan
We consider shield-based defenses as a means to improve controller robustness in the face of such perturbations.