Reinforcement Learning in RoboCup KeepAway with Partial Observability. - Sam Devlin; Marek Grzes; Daniel Kudenko (IAT, 2009)

Multi-agent, reward shaping for RoboCup KeepAway. - Sam Devlin; Marek Grzes; Daniel Kudenko (AAMAS, 2011)

Theoretical considerations of potential-based reward shaping for multi-agent systems. - Sam Devlin; Daniel Kudenko (AAMAS, 2011)

An Empirical Study of Potential-Based Reward Shaping and Advice in Complex, Multi-Agent Systems. - Sam Devlin; Daniel Kudenko; Marek Grzes (Adv. Complex Syst., 2011)

Dynamic potential-based reward shaping. - Sam Devlin; Daniel Kudenko (AAMAS, 2012)

Potential-based reward shaping for POMDPs. - Adam Eck; Leen-Kiat Soh; Sam Devlin; Daniel Kudenko (AAMAS, 2013)

Overcoming erroneous domain knowledge in plan-based reward shaping. - Kyriakos Efthymiadis; Sam Devlin; Daniel Kudenko (AAMAS, 2013)

Potential-based reward shaping for knowledge-based, multi-agent reinforcement learning. - Sam Devlin (2013)

Potential-based difference rewards for multiagent reinforcement learning. - Sam Devlin; Logan Michael Yliniemi; Daniel Kudenko; Kagan Tumer (AAMAS, 2014)

Knowledge revision for reinforcement learning with abstract MDPs. - Kyriakos Efthymiadis; Sam Devlin; Daniel Kudenko (AAMAS, 2014)

Game intelligence. - Sam Devlin; Peter I. Cowling; Daniel Kudenko; Nikolaos Goumagias; Alberto Nucciarelli; Ignazio Cabras; Kiran Jude Fernandes 001; Feng Li 021 (CIG, 2014)

Coordinated Team Learning and Difference Rewards for Distributed Intrusion Response. - Kleanthis Malialis; Sam Devlin; Daniel Kudenko (ECAI, 2014)

Predicting Player Disengagement in Online Games. - Hanting Xie; Daniel Kudenko; Sam Devlin; Peter I. Cowling (CGW@ECAI, 2014)

A Phylogenetic Classification of the Video-Game Industry's Business Model Ecosystem. - Nikolaos Goumagias; Ignazio Cabras; Kiran Jude Fernandes 001; Feng Li 021; Alberto Nucciarelli; Peter I. Cowling; Sam Devlin; Daniel Kudenko (PRO-VE, 2014)

Preface to the special issue: Adaptive Learning Agents, Part 1. - Sam Devlin; Daniel Hennes; Enda Howley (Connect. Sci., 2014)

Preface to the special issue: Adaptive Learning Agents Part 2. - Sam Devlin; Daniel Hennes; Enda Howley (Connect. Sci., 2014)

Expressing Arbitrary Reward Functions as Potential-Based Advice. - Anna Harutyunyan; Sam Devlin; Peter Vrancx; Ann Nowé (AAAI, 2015)

Predicting player disengagement and first purchase with event-frequency based data representation. - Hanting Xie; Sam Devlin; Daniel Kudenko; Peter I. Cowling (CIG, 2015)

Type Inference in Flexible Model-Driven Engineering. - Athanasios Zolotas; Nicholas Drivalos Matragkas; Sam Devlin; Dimitrios S. Kolovos; Richard F. Paige (ECMFA, 2015)

Type Inference Using Concrete Syntax Properties in Flexible Model-Driven Engineering. - Athanasios Zolotas; Nicholas Drivalos Matragkas; Sam Devlin; Dimitrios S. Kolovos; Richard F. Paige (FlexMDE@MoDELS, 2015)

Preface to the special issue: Adaptive Learning Agents Part 3. - Sam Devlin; Daniel Hennes; Samuel Barrett (Connect. Sci., 2015)

Distributed reinforcement learning for adaptive and robust network intrusion response. - Kleanthis Malialis; Sam Devlin; Daniel Kudenko (Connect. Sci., 2015)

Player Preference and Style in a Leading Mobile Card Game. - Peter I. Cowling; Sam Devlin; Edward Jack Powley; Daniel Whitehouse; Jeff Rollason (IEEE Trans. Comput. Intell. AI Games, 2015)

Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play. - Sam Devlin; Anastasija Anspoka; Nick Sephton; Peter I. Cowling; Jeff Rollason (AIIDE, 2016)

Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems. - Kleanthis Malialis; Sam Devlin; Daniel Kudenko (AAMAS, 2016)

Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning: (Extended Abstract). - Patrick Mannion; Karl Mason; Sam Devlin; Jim Duggan; Enda Howley (AAMAS, 2016)

Using association rule mining to predict opponent deck content in android: Netrunner. - Nick Sephton; Peter I. Cowling; Sam Devlin; Victoria J. Hodge; Nicholas H. Slaven (CIG, 2016)

Potential-based reward shaping for finite horizon online POMDP planning. - Adam Eck; Leen-Kiat Soh; Sam Devlin; Daniel Kudenko (Auton. Agents Multi Agent Syst., 2016)

Plan-based reward shaping for multi-agent reinforcement learning. - Sam Devlin; Daniel Kudenko (Knowl. Eng. Rev., 2016)

Overcoming incorrect knowledge in plan-based reward shaping. - Kyriakos Efthymiadis; Sam Devlin; Daniel Kudenko (Knowl. Eng. Rev., 2016)

Context-sensitive reward shaping for sparse interaction multi-agent systems. - Yann-Michaël De Hauwere; Sam Devlin; Daniel Kudenko; Ann Nowé (Knowl. Eng. Rev., 2016)

Clyde: A Deep Reinforcement Learning DOOM Playing Agent. - Dino Stephen Ratcliffe; Sam Devlin; Udo Kruschwitz; Luca Citi (AAAI Workshops, 2017)

Exploration and Skill Acquisition in a Major Online Game. - Tom Stafford 002; Sam Devlin; Rafet Sifa; Anders Drachen (CogSci, 2017)

Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games. - Victoria J. Hodge; Sam Devlin; Nick Sephton; Florian Block; Anders Drachen; Peter I. Cowling (CoRR, 2017)

Policy invariance under reward transformations for multi-objective reinforcement learning. - Patrick Mannion; Sam Devlin; Karl Mason; Jim Duggan; Enda Howley (Neurocomputing, 2017)

Multi-agent credit assignment in stochastic resource management games. - Patrick Mannion; Sam Devlin; Jim Duggan; Enda Howley (Knowl. Eng. Rev., 2017)

Narrative Bytes: Data-Driven Content Production in Esports. - Florian Block; Victoria J. Hodge; Stephen Hobson; Nick Sephton; Sam Devlin; Marian Florin Ursu; Anders Drachen; Peter I. Cowling (TVX, 2018)

Reward shaping for knowledge-based multi-objective multi-agent reinforcement learning. - Patrick Mannion; Sam Devlin; Jim Duggan; Enda Howley (Knowl. Eng. Rev., 2018)

MazeExplorer: A Customisable 3D Benchmark for Assessing Generalisation in Reinforcement Learning. - Luke Harries; Sebastian Lee; Jaroslaw Rzepecki; Katja Hofmann; Sam Devlin (CoG, 2019)

A Generalized Framework for Self-Play Training. - Daniel Hernández 008; Kevin Denamganaï; Alex Yuan Gao; Peter York; Sam Devlin; Spyridon Samothrakis; James Alfred Walker (CoG, 2019)

Win or Learn Fast Proximal Policy Optimisation. - Dino Stephen Ratcliffe; Katja Hofmann; Sam Devlin (CoG, 2019)

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. - Maximilian Igl; Kamil Ciosek; Yingzhen Li; Sebastian Tschiatschek; Cheng Zhang 005; Sam Devlin; Katja Hofmann (NeurIPS, 2019)

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition. - Diego Pérez-Liébana; Katja Hofmann; Sharada Prasanna Mohanty; Noburu Kuno; André Kramer; Sam Devlin; Raluca D. Gaina; Daniel Ionita (CoRR, 2019)

Type inference in flexible model-driven engineering using classification algorithms. - Athanasios Zolotas; Nicholas Matragkas; Sam Devlin; Dimitrios S. Kolovos; Richard F. Paige (Softw. Syst. Model., 2019)

The Text-Based Adventure AI Competition. - Timothy Atkinson 001; Hendrik Baier; Tara Copplestone; Sam Devlin; Jerry Swan (IEEE Trans. Games, 2019)

Emulating Human Play in a Leading Mobile Card Game. - Hendrik Baier; Adam Sattaur; Edward J. Powley; Sam Devlin; Jeff Rollason; Peter I. Cowling (IEEE Trans. Games, 2019)

How the Business Model of Customizable Card Games Influences Player Engagement. - Victoria J. Hodge; Feng Li 021; Nick Sephton; Sam Devlin; Peter I. Cowling; Nikolaos Goumagias; Jianhua Shao; Kieran Purvis; Ignazio Cabras; Kiran Jude Fernandes 001 (IEEE Trans. Games, 2019)

"It's Unwieldy and It Takes a Lot of Time" - Challenges and Opportunities for Creating Agents in Commercial Games. - Mikhail Jacob; Sam Devlin; Katja Hofmann (AIIDE, 2020)

Player Style Clustering without Game Variables. - Mark Ferguson; Sam Devlin; Daniel Kudenko; James Alfred Walker (FDG, 2020)

Automatic Similarity Detection in LEGO Ducks. - Mark Ferguson; Sebastian Deterding; Andreas Lieberoth; Marc Malmdorf Andersen; Sam Devlin; Daniel Kudenko; James Alfred Walker (ICCC, 2020)

AMRL: Aggregated Memory For Reinforcement Learning. - Jacob Beck; Kamil Ciosek; Sam Devlin; Sebastian Tschiatschek; Cheng Zhang 005; Katja Hofmann (ICLR, 2020)

Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control. - Rika Antonova; Maksim Maydanskiy; Danica Kragic; Sam Devlin; Katja Hofmann (CoRR, 2020)

Meta-Learning for Variational Inference. - Ruqi Zhang; Yingzhen Li; Christopher De Sa; Sam Devlin; Cheng Zhang 005 (CoRR, 2020)

Meta-Learning Divergences for Variational Inference. - Ruqi Zhang; Yingzhen Li; Christopher De Sa; Sam Devlin; Cheng Zhang 005 (AISTATS, 2021)

Difference Rewards Policy Gradients. - Jacopo Castellini; Sam Devlin; Frans A. Oliehoek; Rahul Savani (AAMAS, 2021)

Evaluating the Robustness of Collaborative Agents. - Paul Knott; Micah Carroll; Sam Devlin; Kamil Ciosek; Katja Hofmann; Anca D. Dragan; Rohin Shah (AAMAS, 2021)

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning. - Luisa M. Zintgraf; Sam Devlin; Kamil Ciosek; Shimon Whiteson; Katja Hofmann (AAMAS, 2021)

Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation. - Sam Devlin; Raluca Georgescu; Ida Momennejad; Jaroslaw Rzepecki; Evelyn Zuniga; Gavin Costello; Guy Leroy; Ali Shaw; Katja Hofmann (ICML, 2021)

Strategically efficient exploration in competitive multi-agent reinforcement learning. - Robert Tyler Loftin; Aadirupa Saha; Sam Devlin; Katja Hofmann (UAI, 2021)

The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors. - 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; et al. (CoRR, 2021)

Win Prediction in Multiplayer Esports: Live Professional Match Prediction. - Victoria J. Hodge; Sam Devlin; Nick Sephton; Florian Block; Peter I. Cowling; Anders Drachen (IEEE Trans. Games, 2021)

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency. - Mingfei Sun; Sam Devlin; Katja Hofmann; Shimon Whiteson (AAAI, 2022)

Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes. - Ahana Ghosh; Sebastian Tschiatschek; Sam Devlin; Adish Singla (AIED (1), 2022)

How Humans Perceive Human-like Behavior in Video Game Navigation. - Evelyn Zuniga; Stephanie Milani; Guy Leroy; Jaroslaw Rzepecki; Raluca Georgescu; Ida Momennejad; David Bignell; Mingfei Sun; Alison Shaw; Gavin Costello; Mikhail Jacob; Sam Devlin; et al. (CHI Extended Abstracts, 2022)

Turning Zeroes into Non-Zeroes: Sample Efficient Exploration with Monte Carlo Graph Search. - Marko Tot; Michelangelo Conserva; Diego Perez Liebana; Sam Devlin (CoG, 2022)

Imitating Playstyle with Dynamic Time Warping Imitation. - Mark Ferguson; Sam Devlin; Daniel Kudenko; James Alfred Walker (FDG, 2022)

Uni[MASK]: Unified Inference in Sequential Decision Problems. - Micah Carroll; Orr Paradise; Jessy Lin; Raluca Georgescu; Mingfei Sun; David Bignell; Stephanie Milani; Katja Hofmann; Matthew J. Hausknecht; Anca D. Dragan; Sam Devlin (NeurIPS, 2022)

You May Not Need Ratio Clipping in PPO. - Mingfei Sun; Vitaly Kurin; Guoqing Liu; Sam Devlin; Tao Qin 001; Katja Hofmann; Shimon Whiteson (CoRR, 2022)

Monotonic Improvement Guarantees under Non-stationarity for Decentralized PPO. - Mingfei Sun; Sam Devlin; Katja Hofmann; Shimon Whiteson (CoRR, 2022)

Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers. - Micah Carroll; Jessy Lin; Orr Paradise; Raluca Georgescu; Mingfei Sun; David Bignell; Stephanie Milani; Katja Hofmann; Matthew J. Hausknecht; Anca D. Dragan; Sam Devlin (CoRR, 2022)

UniMASK: Unified Inference in Sequential Decision Problems. - Micah Carroll; Orr Paradise; Jessy Lin; Raluca Georgescu; Mingfei Sun; David Bignell; Stephanie Milani; Katja Hofmann; Matthew J. Hausknecht; Anca D. Dragan; Sam Devlin (CoRR, 2022)

Rolling Horizon Evolutionary Algorithms for General Video Game Playing. - Raluca D. Gaina; Sam Devlin; Simon M. Lucas; Diego Pérez-Liébana (IEEE Trans. Games, 2022)

A Comparison of Self-Play Algorithms Under a Generalized Framework. - Daniel Hernández 008; Kevin Denamganaï; Sam Devlin; Spyridon Samothrakis; James Alfred Walker (IEEE Trans. Games, 2022)

Trust Region Bounds for Decentralized PPO Under Non-stationarity. - Mingfei Sun; Sam Devlin; Jacob Beck; Katja Hofmann; Shimon Whiteson (AAMAS, 2023)

Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games. - Stephanie Milani; Arthur Juliani; Ida Momennejad; Raluca Georgescu; Jaroslaw Rzepecki; Alison Shaw; Gavin Costello; Fei Fang 001; Sam Devlin; Katja Hofmann (CHI, 2023)

Contrastive Meta-Learning for Partially Observable Few-Shot Learning. - Adam Jelley; Amos J. Storkey; Antreas Antoniou; Sam Devlin (ICLR, 2023)

Imitating Human Behaviour with Diffusion Models. - Tim Pearce; Tabish Rashid; Anssi Kanervisto; David Bignell; Mingfei Sun; Raluca Georgescu; Sergio Valcarcel Macua; Shan Zheng Tan; Ida Momennejad; Katja Hofmann; Sam Devlin (ICLR, 2023)

Trust-Region-Free Policy Optimization for Stochastic Policies. - Mingfei Sun; Benjamin Ellis; Anuj Mahajan; Sam Devlin; Katja Hofmann; Shimon Whiteson (CoRR, 2023)

Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games. - Lukas Schäfer 001; Logan Jones; Anssi Kanervisto; Yuhan Cao; Tabish Rashid; Raluca Georgescu; David Bignell; Siddhartha Sen 001; Andrea Treviño Gavito; Sam Devlin (CoRR, 2023)

Aligning Agents like Large Language Models. - Adam Jelley; Yuhan Cao; David Bignell; Sam Devlin; Tabish Rashid (CoRR, 2024)

Efficient Offline Reinforcement Learning: The Critic is Critical. - Adam Jelley; Trevor McInroe; Sam Devlin; Amos J. Storkey (CoRR, 2024)