53 citations · 186 across the 17 of their papers we have counts for
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cs.LG2020★ 1 cited
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
Hongyao Tang, Zhaopeng Meng, Jianye Hao +9
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only…
cs.AI2020★ 19 cited
Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing
Hangyu Mao, Zhibo Gong, Zhen Xiao
In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem. The global…
cs.MA2020★ 6 cited
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
Tianpei Yang, Weixun Wang, Hongyao Tang +9
Transfer Learning has shown great potential to enhance single-agent Reinforcement Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can also be accelerated if agents can sh…