19 citations · 60 across the 23 of their papers we have counts for
7 papers · 1 filter
A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process
Jiayu Chen, Vaneet Aggarwal, Tian Lan
Learning rich skills under the option framework without supervision of external rewards is at the frontier of reinforcement learning research. Existing works mainly fall into two d…
Multi-agent Deep Covering Skill Discovery
Jiayu Chen, Marina Haliem, Tian Lan +1
The use of skills (a.k.a., options) can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery m…
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control
Jiayu Chen, Tian Lan, Vaneet Aggarwal
Hierarchical Imitation Learning (HIL) has been proposed to recover highly-complex behaviors in long-horizon tasks from expert demonstrations by modeling the task hierarchy with the…
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning
Hanhan Zhou, Tian Lan, Vaneet Aggarwal
Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods. It allows optimizing a joint action-value…
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications
Yuzhu Mao, Zihao Zhao, Meilin Yang +5
Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency…
Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs
Jiayu Chen, Jingdi Chen, Tian Lan +1
Covering skill (a.k.a., option) discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios with sparse reward signals, through conn…
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients
Hanhan Zhou, Tian Lan, Vaneet Aggarwal
Value function factorization via centralized training and decentralized execution is promising for solving cooperative multi-agent reinforcement tasks. One of the approaches in thi…