16 citations · 60 across the 13 of their papers we have counts for
6 papers · 1 filter
Population-size-Aware Policy Optimization for Mean-Field Games
Pengdeng Li, Xinrun Wang, Shuxin Li +2
In this work, we attempt to bridge the two fields of finite-agent and infinite-agent games, by studying how the optimal policies of agents evolve with the number of agents (populat…
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Wei Qiu, Xinrun Wang, Runsheng Yu +5
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (…
MetaInfoNet: Learning Task-Guided Information for Sample Reweighting
Hongxin Wei, Lei Feng, Rundong Wang +1
Deep neural networks have been shown to easily overfit to biased training data with label noise or class imbalance. Meta-learning algorithms are commonly designed to alleviate this…
Efficient Reservoir Management through Deep Reinforcement Learning
Xinrun Wang, Tarun Nair, Haoyang Li +7
Dams impact downstream river dynamics through flow regulation and disruption of upstream-downstream linkages. However, current dam operation is far from satisfactory due to the ina…
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
Xu He, Bo An, Yanghua Li +6
With the rise of online e-commerce platforms, more and more customers prefer to shop online. To sell more products, online platforms introduce various modules to recommend items wi…
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
Xu He, Bo An, Yanghua Li +4
Online recommendation services recommend multiple commodities to users. Nowadays, a considerable proportion of users visit e-commerce platforms by mobile devices. Due to the limite…