29 citations · 30 across the 5 of their papers we have counts for
5 papers
Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All Networks
Zhile Yang, Shangqi Guo, Ying Fang +1
One stream of reinforcement learning research is exploring biologically plausible models and algorithms to simulate biological intelligence and fit neuromorphic hardware. Among the…
Adjacency constraint for efficient hierarchical reinforcement learning
Tianren Zhang, Shangqi Guo, Tian Tan +2
Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training in…
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
Chongkai Gao, Haichuan Gao, Shangqi Guo +2
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations. However, they need multi-task demonstrations to be provided a…
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
Tianren Zhang, Shangqi Guo, Tian Tan +2
Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training in…
Subjectivity Learning Theory towards Artificial General Intelligence
Xin Su, Shangqi Guo, Feng Chen
The construction of artificial general intelligence (AGI) was a long-term goal of AI research aiming to deal with the complex data in the real world and make reasonable judgments i…