activity
20212025
most citedTiming is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NI2025

UavNetSim-v1: A Python-based Simulation Platform for UAV Communication Networks

Zihao Zhou, Zipeng Dai, Linyi Huang +4

In unmanned aerial vehicle (UAV) networks, communication protocols and algorithms are essential for cooperation and collaboration between UAVs. Simulation provides a cost-effective…

cs.LG2023★ 1 cited

HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning

Hao Wang, Bo Tang, Chi Harold Liu +7

Online display advertising platforms service numerous advertisers by providing real-time bidding (RTB) for the scale of billions of ad requests every day. The bidding strategy hand…

cs.MA2022

Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction

Taher Jafferjee, Juliusz Ziomek, Tianpei Yang +6

Centralised training with decentralised execution (CT-DE) serves as the foundation of many leading multi-agent reinforcement learning (MARL) algorithms. Despite its popularity, it…

cs.LG2022★ 2 cited

Timing is Everything: Learning to Act Selectively with Costly Actions and Budgetary Constraints

David Mguni, Aivar Sootla, Juliusz Ziomek +4

Many real-world settings involve costs for performing actions; transaction costs in financial systems and fuel costs being common examples. In these settings, performing actions at…

cs.LG2021

Learning to Shape Rewards using a Game of Two Partners

David Mguni, Taher Jafferjee, Jianhong Wang +9

Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. However, RS typically relies on manually engi…