Potential-based difference rewards for multiagent reinforcement learning S Devlin, L Yliniemi, D Kudenko, K Tumer Proceedings of the 2014 international conference on Autonomous agents and …, 2014 | 293 | 2014 |
Dynamic potential-based reward shaping SM Devlin, D Kudenko 11th International Conference on Autonomous Agents and Multiagent Systems …, 2012 | 259 | 2012 |
Potential-based reward shaping for finite horizon online pomdp planning A Eck, LK Soh, S Devlin, D Kudenko Autonomous Agents and Multi-Agent Systems 30 (3), 403-445, 2016 | 212 | 2016 |
Generalization in reinforcement learning with selective noise injection and information bottleneck M Igl, K Ciosek, Y Li, S Tschiatschek, C Zhang, S Devlin, K Hofmann Advances in neural information processing systems 32, 2019 | 176 | 2019 |
Theoretical considerations of potential-based reward shaping for multi-agent systems S Devlin, D Kudenko Tenth International Conference on Autonomous Agents and Multi-Agent Systems …, 2011 | 153 | 2011 |
From value chains to technological platforms: The effects of crowdfunding in the digital game industry A Nucciarelli, F Li, KJ Fernandes, N Goumagias, I Cabras, S Devlin, ... Journal of Business Research 78, 341-352, 2017 | 152 | 2017 |
Expressing arbitrary reward functions as potential-based advice A Harutyunyan, S Devlin, P Vrancx, A Nowé Proceedings of the AAAI conference on artificial intelligence 29 (1), 2015 | 118 | 2015 |
Imitating human behaviour with diffusion models T Pearce, T Rashid, A Kanervisto, D Bignell, M Sun, R Georgescu, ... arXiv preprint arXiv:2301.10677, 2023 | 113 | 2023 |
An Empirical Study Of Potential-Based Reward Shaping And Advice In Complex, Multi-Agent Systems S Devlin, D Kudenko, M Grześ Advances in Complex Systems (ACS) 14 (02), 251-278, 2011 | 108 | 2011 |
Game Intelligence S Devlin, PI Cowling, D Kudenko, N Goumagias, A Nucciareli, I Cabras, ... 2014 IEEE Conference on Computational Intelligence and Games, 1-8, 2014 | 97 | 2014 |
Win prediction in multiplayer esports: Live professional match prediction VJ Hodge, S Devlin, N Sephton, F Block, PI Cowling, A Drachen IEEE Transactions on Games 13 (4), 368-379, 2019 | 96 | 2019 |
Predicting player disengagement and first purchase with event-frequency based data representation H Xie, S Devlin, D Kudenko, P Cowling 2015 IEEE Conference on Computational Intelligence and Games (CIG), 230-237, 2015 | 70 | 2015 |
Narrative bytes: Data-driven content production in esports F Block, V Hodge, S Hobson, N Sephton, S Devlin, MF Ursu, A Drachen, ... Proceedings of the 2018 ACM international conference on interactive …, 2018 | 67 | 2018 |
Reward shaping for knowledge-based multi-objective multi-agent reinforcement learning P Mannion, S Devlin, J Duggan, E Howley The Knowledge Engineering Review 33, e23, 2018 | 58 | 2018 |
Policy invariance under reward transformations for multi-objective reinforcement learning P Mannion, S Devlin, K Mason, J Duggan, E Howley Neurocomputing 263, 60-73, 2017 | 55 | 2017 |
Exploring survival rates of companies in the UK video-games industry: An empirical study I Cabras, ND Goumagias, K Fernandes, P Cowling, F Li, D Kudenko, ... Technological Forecasting and Social Change 117, 305-314, 2017 | 46 | 2017 |
The Multi-Agent Reinforcement Learning in Malm\" O (MARL\" O) Competition D Perez-Liebana, K Hofmann, SP Mohanty, N Kuno, A Kramer, S Devlin, ... arXiv preprint arXiv:1901.08129, 2019 | 40 | 2019 |
Resource abstraction for reinforcement learning in multiagent congestion problems K Malialis, S Devlin, D Kudenko arXiv preprint arXiv:1903.05431, 2019 | 39 | 2019 |
The text-based adventure AI competition T Atkinson, H Baier, T Copplestone, S Devlin, J Swan IEEE Transactions on Games 11 (3), 260-266, 2019 | 39 | 2019 |
Deep interactive bayesian reinforcement learning via meta-learning L Zintgraf, S Devlin, K Ciosek, S Whiteson, K Hofmann arXiv preprint arXiv:2101.03864, 2021 | 38 | 2021 |