5 citations · 25 across the 36 of their papers we have counts for
9 papers · 1 filter
Contrastive Explanations for Comparing Preferences of Reinforcement Learning Agents
Jasmina Gajcin, Rahul Nair, Tejaswini Pedapati +3
In complex tasks where the reward function is not straightforward and consists of a set of objectives, multiple reinforcement learning (RL) policies that perform task adequately, b…
Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems
Alberto Castagna, Ivana Dusparic
Reinforcement learning (RL) has been used in a range of simulated real-world tasks, e.g., sensor coordination, traffic light control, and on-demand mobility services. However, real…
Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points
Boris Galkin, Babatunji Omoniwa, Ivana Dusparic
Unmanned Aerial Vehicles (UAVs) promise to become an intrinsic part of next generation communications, as they can be deployed to provide wireless connectivity to ground users to s…
Analyse or Transmit: Utilising Correlation at the Edge with Deep Reinforcement Learning
Jernej Hribar, Ryoichi Shinkuma, George Iosifidis +1
Millions of sensors, cameras, meters, and other edge devices are deployed in networks to collect and analyse data. In many cases, such devices are powered only by Energy Harvesting…
Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning
Babatunji Omoniwa, Boris Galkin, Ivana Dusparic
Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be deployed to provide wireless connectivity to ground devices in events of increased network demand, points-…
A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT
Babatunji Omoniwa, Maxime Gueriau, Ivana Dusparic
Recent research has shown the potential of using available mobile fog devices (such as smartphones, drones, domestic and industrial robots) as relays to minimize communication outa…