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ORCIDSam Devlin
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- affiliation: Microsoft Research, Cambridge, UK
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2020 – today
- 2024
[i29]Adam Jelley, Yuhan Cao, David Bignell, Sam Devlin, Tabish Rashid:
Aligning Agents like Large Language Models. CoRR abs/2406.04208 (2024)- [i28]Adam Jelley, Trevor McInroe, Sam Devlin, Amos J. Storkey:
Efficient Offline Reinforcement Learning: The Critic is Critical. CoRR abs/2406.13376 (2024) - 2023
- [c47]Mingfei Sun, Sam Devlin, Jacob Beck, Katja Hofmann, Shimon Whiteson:
Trust Region Bounds for Decentralized PPO Under Non-stationarity. AAMAS 2023: 5-13 - [c46]Stephanie Milani
, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzepecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann:
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games. CHI 2023: 572:1-572:18 - [c45]Adam Jelley, Amos J. Storkey, Antreas Antoniou, Sam Devlin:
Contrastive Meta-Learning for Partially Observable Few-Shot Learning. ICLR 2023 - [c44]Tim Pearce, Tabish Rashid, Anssi Kanervisto, David Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin:
Imitating Human Behaviour with Diffusion Models. ICLR 2023 - [i27]Tim Pearce, Tabish Rashid, Anssi Kanervisto, David Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin:
Imitating Human Behaviour with Diffusion Models. CoRR abs/2301.10677 (2023) - [i26]Adam Jelley, Amos J. Storkey, Antreas Antoniou, Sam Devlin:
Contrastive Meta-Learning for Partially Observable Few-Shot Learning. CoRR abs/2301.13136 (2023) - [i25]Mingfei Sun, Benjamin Ellis, Anuj Mahajan, Sam Devlin, Katja Hofmann, Shimon Whiteson:
Trust-Region-Free Policy Optimization for Stochastic Policies. CoRR abs/2302.07985 (2023) - [i24]Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzepecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann:
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games. CoRR abs/2303.02160 (2023) - [i23]Ahana Ghosh, Sebastian Tschiatschek, Sam Devlin, Adish Singla:
Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes. CoRR abs/2303.16359 (2023) - [i22]Lukas Schäfer, Logan Jones, Anssi Kanervisto, Yuhan Cao, Tabish Rashid, Raluca Georgescu, David Bignell, Siddhartha Sen, Andrea Treviño Gavito, Sam Devlin:
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games. CoRR abs/2312.02312 (2023) - 2022
- [j20]Daniel Hernández, Kevin Denamganaï, Sam Devlin, Spyridon Samothrakis, James Alfred Walker:
A Comparison of Self-Play Algorithms Under a Generalized Framework. IEEE Trans. Games 14(2): 221-231 (2022) - [j19]Raluca D. Gaina, Sam Devlin, Simon M. Lucas, Diego Pérez-Liébana:
Rolling Horizon Evolutionary Algorithms for General Video Game Playing. IEEE Trans. Games 14(2): 232-242 (2022) - [c43]Mingfei Sun, Sam Devlin, Katja Hofmann, Shimon Whiteson:
Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency. AAAI 2022: 8378-8385 - [c42]Ahana Ghosh, Sebastian Tschiatschek, Sam Devlin, Adish Singla:
Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes. AIED (1) 2022: 28-40 - [c41]Evelyn Zuniga, Stephanie Milani, Guy Leroy, Jaroslaw Rzepecki, Raluca Georgescu, Ida Momennejad, David Bignell, Mingfei Sun, Alison Shaw, Gavin Costello, Mikhail Jacob, Sam Devlin, Katja Hofmann:
How Humans Perceive Human-like Behavior in Video Game Navigation. CHI Extended Abstracts 2022: 391:1-391:11 - [c40]Marko Tot, Michelangelo Conserva, Diego Perez Liebana, Sam Devlin:
Turning Zeroes into Non-Zeroes: Sample Efficient Exploration with Monte Carlo Graph Search. CoG 2022: 300-306 - [c39]Mark Ferguson, Sam Devlin, Daniel Kudenko, James Alfred Walker:
Imitating Playstyle with Dynamic Time Warping Imitation. FDG 2022: 41:1-41:11 - [c38]Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew J. Hausknecht, Anca D. Dragan, Sam Devlin:
Uni[MASK]: Unified Inference in Sequential Decision Problems. NeurIPS 2022 - [i21]Mingfei Sun, Vitaly Kurin, Guoqing Liu, Sam Devlin, Tao Qin, Katja Hofmann, Shimon Whiteson:
You May Not Need Ratio Clipping in PPO. CoRR abs/2202.00079 (2022) - [i20]Mingfei Sun, Sam Devlin, Katja Hofmann, Shimon Whiteson:
Monotonic Improvement Guarantees under Non-stationarity for Decentralized PPO. CoRR abs/2202.00082 (2022) - [i19]Micah Carroll, Jessy Lin, Orr Paradise, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew J. Hausknecht, Anca D. Dragan, Sam Devlin:
Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers. CoRR abs/2204.13326 (2022) - [i18]Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew J. Hausknecht, Anca D. Dragan, Sam Devlin:
UniMASK: Unified Inference in Sequential Decision Problems. CoRR abs/2211.10869 (2022) - 2021
- [j18]Victoria J. Hodge, Sam Devlin, Nick Sephton, Florian Block, Peter I. Cowling, Anders Drachen:
Win Prediction in Multiplayer Esports: Live Professional Match Prediction. IEEE Trans. Games 13(4): 368-379 (2021) - [c37]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning Divergences for Variational Inference. AISTATS 2021: 4024-4032 - [c36]Jacopo Castellini, Sam Devlin, Frans A. Oliehoek, Rahul Savani:
Difference Rewards Policy Gradients. AAMAS 2021: 1475-1477 - [c35]Paul Knott, Micah Carroll, Sam Devlin, Kamil Ciosek, Katja Hofmann, Anca D. Dragan, Rohin Shah:
Evaluating the Robustness of Collaborative Agents. AAMAS 2021: 1560-1562 - [c34]Luisa M. Zintgraf, Sam Devlin, Kamil Ciosek, Shimon Whiteson, Katja Hofmann:
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning. AAMAS 2021: 1712-1714 - [c33]Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann:
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation. ICML 2021: 2644-2653 - [c32]Robert Tyler Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann:
Strategically efficient exploration in competitive multi-agent reinforcement learning. UAI 2021: 1587-1596 - [i17]Luisa M. Zintgraf, Sam Devlin, Kamil Ciosek, Shimon Whiteson, Katja Hofmann:
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning. CoRR abs/2101.03864 (2021) - [i16]Paul Knott, Micah Carroll, Sam Devlin, Kamil Ciosek, Katja Hofmann, Anca D. Dragan, Rohin Shah:
Evaluating the Robustness of Collaborative Agents. CoRR abs/2101.05507 (2021) - [i15]William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada P. Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals:
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors. CoRR abs/2101.11071 (2021) - [i14]Sam Devlin, Raluca Georgescu, Ida Momennejad, Jaroslaw Rzepecki, Evelyn Zuniga, Gavin Costello, Guy Leroy, Ali Shaw, Katja Hofmann:
Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation. CoRR abs/2105.09637 (2021) - [i13]Robert Tyler Loftin, Aadirupa Saha, Sam Devlin, Katja Hofmann:
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning. CoRR abs/2107.14698 (2021) - [i12]Mingfei Sun, Sam Devlin, Katja Hofmann, Shimon Whiteson:
Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency. CoRR abs/2112.06054 (2021) - 2020
- [c31]Mikhail Jacob, Sam Devlin, Katja Hofmann:
"It's Unwieldy and It Takes a Lot of Time" - Challenges and Opportunities for Creating Agents in Commercial Games. AIIDE 2020: 88-94 - [c30]Mark Ferguson, Sam Devlin, Daniel Kudenko, James Alfred Walker:
Player Style Clustering without Game Variables. FDG 2020: 66:1-66:4 - [c29]Mark Ferguson, Sebastian Deterding, Andreas Lieberoth, Marc Malmdorf Andersen, Sam Devlin, Daniel Kudenko, James Alfred Walker:
Automatic Similarity Detection in LEGO Ducks. ICCC 2020: 106-109 - [c28]Jacob Beck, Kamil Ciosek, Sam Devlin, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann:
AMRL: Aggregated Memory For Reinforcement Learning. ICLR 2020 - [i11]Raluca D. Gaina, Sam Devlin, Simon M. Lucas, Diego Pérez-Liébana:
Rolling Horizon Evolutionary Algorithms for General Video Game Playing. CoRR abs/2003.12331 (2020) - [i10]Daniel Hernández, Kevin Denamganaï, Sam Devlin, Spyridon Samothrakis, James Alfred Walker:
A Comparison of Self-Play Algorithms Under a Generalized Framework. CoRR abs/2006.04471 (2020) - [i9]Rika Antonova, Maksim Maydanskiy, Danica Kragic, Sam Devlin, Katja Hofmann:
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control. CoRR abs/2006.08718 (2020) - [i8]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning for Variational Inference. CoRR abs/2007.02912 (2020) - [i7]Mikhail Jacob, Sam Devlin, Katja Hofmann:
"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games. CoRR abs/2009.00541 (2020) - [i6]Jacopo Castellini, Sam Devlin, Frans A. Oliehoek, Rahul Savani:
Difference Rewards Policy Gradients. CoRR abs/2012.11258 (2020)
2010 – 2019
- 2019
- [j17]Athanasios Zolotas, Nicholas Matragkas, Sam Devlin, Dimitrios S. Kolovos, Richard F. Paige:
Type inference in flexible model-driven engineering using classification algorithms. Softw. Syst. Model. 18(1): 345-366 (2019) - [j16]Timothy Atkinson, Hendrik Baier, Tara Copplestone, Sam Devlin, Jerry Swan:
The Text-Based Adventure AI Competition. IEEE Trans. Games 11(3): 260-266 (2019) - [j15]Victoria J. Hodge, Feng Li, Nick Sephton, Sam Devlin, Peter I. Cowling, Nikolaos Goumagias, Jianhua Shao, Kieran Purvis, Ignazio Cabras, Kiran Jude Fernandes:
How the Business Model of Customizable Card Games Influences Player Engagement. IEEE Trans. Games 11(4): 374-385 (2019) - [j14]Hendrik Baier, Adam Sattaur, Edward J. Powley, Sam Devlin, Jeff Rollason, Peter I. Cowling:
Emulating Human Play in a Leading Mobile Card Game. IEEE Trans. Games 11(4): 386-395 (2019) - [c27]Luke Harries, Sebastian Lee, Jaroslaw Rzepecki, Katja Hofmann, Sam Devlin:
MazeExplorer: A Customisable 3D Benchmark for Assessing Generalisation in Reinforcement Learning. CoG 2019: 1-4 - [c26]Daniel Hernández, Kevin Denamganaï, Alex Yuan Gao, Peter York, Sam Devlin, Spyridon Samothrakis, James Alfred Walker:
A Generalized Framework for Self-Play Training. CoG 2019: 1-8 - [c25]Dino Stephen Ratcliffe, Katja Hofmann, Sam Devlin:
Win or Learn Fast Proximal Policy Optimisation. CoG 2019: 1-4 - [c24]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. NeurIPS 2019: 13956-13968 - [i5]Diego Pérez-Liébana, Katja Hofmann, Sharada Prasanna Mohanty, Noburu Kuno, André Kramer, Sam Devlin, Raluca D. Gaina, Daniel Ionita:
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition. CoRR abs/1901.08129 (2019) - [i4]Kleanthis Malialis, Sam Devlin, Daniel Kudenko:
Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems. CoRR abs/1903.05431 (2019) - [i3]Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin, Katja Hofmann:
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. CoRR abs/1910.12911 (2019) - 2018
- [j13]Patrick Mannion, Sam Devlin, Jim Duggan, Enda Howley:
Reward shaping for knowledge-based multi-objective multi-agent reinforcement learning. Knowl. Eng. Rev. 33: e23 (2018) - [c23]Florian Block, Victoria J. Hodge, Stephen Hobson, Nick Sephton, Sam Devlin, Marian Florin Ursu, Anders Drachen, Peter I. Cowling:
Narrative Bytes: Data-Driven Content Production in Esports. TVX 2018: 29-41 - [i2]Timothy Atkinson, Hendrik Baier, Tara Copplestone, Sam Devlin, Jerry Swan:
The Text-Based Adventure AI Competition. CoRR abs/1808.01262 (2018) - 2017
- [j12]Patrick Mannion, Sam Devlin, Karl Mason, Jim Duggan, Enda Howley:
Policy invariance under reward transformations for multi-objective reinforcement learning. Neurocomputing 263: 60-73 (2017) - [j11]Patrick Mannion, Sam Devlin, Jim Duggan, Enda Howley:
Multi-agent credit assignment in stochastic resource management games. Knowl. Eng. Rev. 32: e16 (2017) - [c22]Dino Stephen Ratcliffe, Sam Devlin, Udo Kruschwitz, Luca Citi:
Clyde: A Deep Reinforcement Learning DOOM Playing Agent. AAAI Workshops 2017 - [c21]Tom Stafford, Sam Devlin, Rafet Sifa, Anders Drachen:
Exploration and Skill Acquisition in a Major Online Game. CogSci 2017 - [i1]Victoria J. Hodge, Sam Devlin, Nick Sephton, Florian Block, Anders Drachen, Peter I. Cowling:
Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games. CoRR abs/1711.06498 (2017) - 2016
- [j10]Adam Eck, Leen-Kiat Soh, Sam Devlin, Daniel Kudenko:
Potential-based reward shaping for finite horizon online POMDP planning. Auton. Agents Multi Agent Syst. 30(3): 403-445 (2016) - [j9]Kyriakos Efthymiadis, Sam Devlin, Daniel Kudenko:
Overcoming incorrect knowledge in plan-based reward shaping. Knowl. Eng. Rev. 31(1): 31-43 (2016) - [j8]Sam Devlin, Daniel Kudenko:
Plan-based reward shaping for multi-agent reinforcement learning. Knowl. Eng. Rev. 31(1): 44-58 (2016) - [j7]Yann-Michaël De Hauwere, Sam Devlin, Daniel Kudenko, Ann Nowé:
Context-sensitive reward shaping for sparse interaction multi-agent systems. Knowl. Eng. Rev. 31(1): 59-76 (2016) - [c20]Sam Devlin, Anastasija Anspoka, Nick Sephton, Peter I. Cowling, Jeff Rollason:
Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play. AIIDE 2016: 16-22 - [c19]Kleanthis Malialis, Sam Devlin, Daniel Kudenko:
Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems. AAMAS 2016: 503-511 - [c18]Patrick Mannion, Karl Mason, Sam Devlin, Jim Duggan, Enda Howley:
Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning: (Extended Abstract). AAMAS 2016: 1345-1346 - [c17]Nick Sephton, Peter I. Cowling, Sam Devlin, Victoria J. Hodge, Nicholas H. Slaven:
Using association rule mining to predict opponent deck content in android: Netrunner. CIG 2016: 1-8 - 2015
- [j6]Sam Devlin, Daniel Hennes, Samuel Barrett:
Preface to the special issue: Adaptive Learning Agents Part 3. Connect. Sci. 27(3): 213-214 (2015) - [j5]Kleanthis Malialis, Sam Devlin, Daniel Kudenko:
Distributed reinforcement learning for adaptive and robust network intrusion response. Connect. Sci. 27(3): 234-252 (2015) - [j4]Peter I. Cowling, Sam Devlin, Edward Jack Powley, Daniel Whitehouse, Jeff Rollason:
Player Preference and Style in a Leading Mobile Card Game. IEEE Trans. Comput. Intell. AI Games 7(3): 233-242 (2015) - [c16]Anna Harutyunyan, Sam Devlin, Peter Vrancx, Ann Nowé:
Expressing Arbitrary Reward Functions as Potential-Based Advice. AAAI 2015: 2652-2658 - [c15]Hanting Xie, Sam Devlin, Daniel Kudenko, Peter I. Cowling:
Predicting player disengagement and first purchase with event-frequency based data representation. CIG 2015: 230-237 - [c14]Athanasios Zolotas, Nicholas Drivalos Matragkas, Sam Devlin, Dimitrios S. Kolovos, Richard F. Paige:
Type Inference in Flexible Model-Driven Engineering. ECMFA 2015: 75-91 - [c13]Athanasios Zolotas, Nicholas Drivalos Matragkas, Sam Devlin, Dimitrios S. Kolovos, Richard F. Paige:
Type Inference Using Concrete Syntax Properties in Flexible Model-Driven Engineering. FlexMDE@MoDELS 2015: 22-31 - 2014
- [j3]Sam Devlin, Daniel Hennes, Enda Howley:
Preface to the special issue: Adaptive Learning Agents, Part 1. Connect. Sci. 26(1): 5-6 (2014) - [j2]Sam Devlin, Daniel Hennes, Enda Howley:
Preface to the special issue: Adaptive Learning Agents Part 2. Connect. Sci. 26(2): 101-102 (2014) - [c12]Sam Devlin, Logan Michael Yliniemi, Daniel Kudenko, Kagan Tumer:
Potential-based difference rewards for multiagent reinforcement learning. AAMAS 2014: 165-172 - [c11]Kyriakos Efthymiadis, Sam Devlin, Daniel Kudenko:
Knowledge revision for reinforcement learning with abstract MDPs. AAMAS 2014: 1535-1536 - [c10]Sam Devlin, Peter I. Cowling, Daniel Kudenko, Nikolaos Goumagias, Alberto Nucciarelli, Ignazio Cabras, Kiran Jude Fernandes, Feng Li:
Game intelligence. CIG 2014: 1-8 - [c9]Hanting Xie, Daniel Kudenko, Sam Devlin, Peter I. Cowling:
Predicting Player Disengagement in Online Games. CGW@ECAI 2014: 133-149 - [c8]Kleanthis Malialis, Sam Devlin, Daniel Kudenko:
Coordinated Team Learning and Difference Rewards for Distributed Intrusion Response. ECAI 2014: 1063-1064 - [c7]Nikolaos Goumagias, Ignazio Cabras, Kiran Jude Fernandes, Feng Li, Alberto Nucciarelli, Peter I. Cowling, Sam Devlin, Daniel Kudenko:
A Phylogenetic Classification of the Video-Game Industry's Business Model Ecosystem. PRO-VE 2014: 285-294 - 2013
- [b1]Sam Devlin:
Potential-based reward shaping for knowledge-based, multi-agent reinforcement learning. University of York, UK, 2013 - [c6]Adam Eck, Leen-Kiat Soh, Sam Devlin, Daniel Kudenko:
Potential-based reward shaping for POMDPs. AAMAS 2013: 1123-1124 - [c5]Kyriakos Efthymiadis, Sam Devlin, Daniel Kudenko:
Overcoming erroneous domain knowledge in plan-based reward shaping. AAMAS 2013: 1245-1246 - 2012
- [c4]Sam Devlin, Daniel Kudenko:
Dynamic potential-based reward shaping. AAMAS 2012: 433-440 - 2011
- [j1]Sam Devlin, Daniel Kudenko, Marek Grzes:
An Empirical Study of Potential-Based Reward Shaping and Advice in Complex, Multi-Agent Systems. Adv. Complex Syst. 14(2): 251-278 (2011) - [c3]Sam Devlin, Daniel Kudenko:
Theoretical considerations of potential-based reward shaping for multi-agent systems. AAMAS 2011: 225-232 - [c2]Sam Devlin, Marek Grzes, Daniel Kudenko:
Multi-agent, reward shaping for RoboCup KeepAway. AAMAS 2011: 1227-1228
2000 – 2009
- 2009
- [c1]Sam Devlin, Marek Grzes, Daniel Kudenko:
Reinforcement Learning in RoboCup KeepAway with Partial Observability. IAT 2009: 201-208
Coauthor Index
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