A latent linear model for nonlinear coupled oscillators on graphs

1 code implementation • 25 Nov 2023 • Agam Goyal, Zhaoxing Wu, Richard P. Yim, Binhao Chen, Zihong Xu, Hanbaek Lyu

A system of coupled oscillators on an arbitrary graph is locally driven by the tendency to mutual synchronization between nearby oscillators, but can and often exhibit nonlinear behavior on the whole graph.

Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration

no code implementations • 27 Sep 2021 • Zhaorun Chen, Binhao Chen, Shenghan Xie, Liang Gong, Chengliang Liu, Zhengfeng Zhang, Junping Zhang

In complex environments with high dimension, training a reinforcement learning (RL) model from scratch often suffers from lengthy and tedious collection of agent-environment interactions.

Reinforcement Learning (RL)

POAR: Efficient Policy Optimization via Online Abstract State Representation Learning

1 code implementation • 17 Sep 2021 • Zhaorun Chen, Siqi Fan, Yuan Tan, Liang Gong, Binhao Chen, Te Sun, David Filliat, Natalia Díaz-Rodríguez, Chengliang Liu

Firstly, We engage RL loss to assist in updating SRL model so that the states can evolve to meet the demand of RL and maintain a good physical interpretation.

reinforcement-learning Reinforcement Learning (RL) +1