1 code implementation • 6 Dec 2021 • Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan, Kai Liang Tan, Aditya Balu, Young M. Lee, Chinmay Hegde, Soumik Sarkar
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations.
Multi-agent Reinforcement Learning Policy Gradient Methods +3
1 code implementation • 13 Nov 2020 • Xian Yeow Lee, Yasaman Esfandiari, Kai Liang Tan, Soumik Sarkar
As the complexity of CPS evolved, the focus has shifted from traditional control methods to deep reinforcement learning-based (DRL) methods for control of these systems.
no code implementations • 14 Jul 2020 • Kai Liang Tan, Yasaman Esfandiari, Xian Yeow Lee, Aakanksha, Soumik Sarkar
While robust control has a long history of development, robust ML is an emerging research area that has already demonstrated its relevance and urgency.
no code implementations • 14 Nov 2019 • Kai Liang Tan, Subhadipto Poddar, Anuj Sharma, Soumik Sarkar
In this paper, we propose a DRL-based adaptive traffic signal control framework that explicitly considers realistic traffic scenarios, sensors, and physical constraints.
1 code implementation • 5 Sep 2019 • Xian Yeow Lee, Sambit Ghadai, Kai Liang Tan, Chinmay Hegde, Soumik Sarkar
In this work, we first frame the problem as an optimization problem of minimizing the cumulative reward of an RL agent with decoupled constraints as the budget of attack.