Self-evolving Autoencoder Embedded Q-Network

no code implementations • 18 Feb 2024 • J. Senthilnath, Bangjian Zhou, Zhen Wei Ng, Deeksha Aggarwal, Rajdeep Dutta, Ji Wei Yoon, Aye Phyu Phyu Aung, Keyu Wu, Min Wu, XiaoLi Li

During the evolution of the autoencoder architecture, a bias-variance regulatory strategy is employed to elicit the optimal response from the RL agent.

Decision Making Reinforcement Learning (RL)

S-REINFORCE: A Neuro-Symbolic Policy Gradient Approach for Interpretable Reinforcement Learning

no code implementations • 12 May 2023 • Rajdeep Dutta, Qincheng Wang, Ankur Singh, Dhruv Kumarjiguda, Li Xiaoli, Senthilnath Jayavelu

This paper presents a novel RL algorithm, S-REINFORCE, which is designed to generate interpretable policies for dynamic decision-making tasks.

Decision Making reinforcement-learning

Robust Consensus of Higher-Order Multi-Agent Systems With Attrition and Inclusion of Agents and Switching Topologies

no code implementations • 13 Feb 2022 • Jinraj V Pushpangathan, Harikumar Kandath, Rajdeep Dutta, Rajarshi Bardhan, J. Senthilnath

To solve this RAI consensus problem, first, the sufficient condition for the existence of the RAIDD protocol is obtained using the $\nu$-gap metric-based simultaneous stabilization approach.