1 citations · 1 across the 3 of their papers we have counts for
5 papers
Uniform State Abstraction For Reinforcement Learning
John Burden, Daniel Kudenko
Potential Based Reward Shaping combined with a potential function based on appropriately defined abstract knowledge has been shown to significantly improve learning speed in Reinfo…
Generating Stereotypes Automatically For Complex Categorical Features
Nourah ALRossais, Daniel Kudenko
In the context of stereotypes creation for recommender systems, we found that certain types of categorical variables pose particular challenges if simple clustering procedures were…
Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems
Kleanthis Malialis, Sam Devlin, Daniel Kudenko
Real-world congestion problems (e.g. traffic congestion) are typically very complex and large-scale. Multiagent reinforcement learning (MARL) is a promising candidate for dealing w…
Artificial Intelligence for Prosthetics - challenge solutions
Łukasz Kidziński, Carmichael Ong, Sharada Prasanna Mohanty +47
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a giv…
Deep Multi-Agent Reinforcement Learning with Relevance Graphs
Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn +2
Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domain…