Sam Devlin

1.7k total citations
54 papers, 879 citations indexed

About

Sam Devlin is a scholar working on Artificial Intelligence, Sociology and Political Science and Management Science and Operations Research. According to data from OpenAlex, Sam Devlin has authored 54 papers receiving a total of 879 indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Artificial Intelligence, 12 papers in Sociology and Political Science and 10 papers in Management Science and Operations Research. Recurrent topics in Sam Devlin's work include Reinforcement Learning in Robotics (31 papers), Artificial Intelligence in Games (19 papers) and Digital Games and Media (9 papers). Sam Devlin is often cited by papers focused on Reinforcement Learning in Robotics (31 papers), Artificial Intelligence in Games (19 papers) and Digital Games and Media (9 papers). Sam Devlin collaborates with scholars based in United Kingdom, United States and Ireland. Sam Devlin's co-authors include Daniel Kudenko⋆, Peter Cowling, Anders Drachen, Marek Grześ, Patrick Mannion, Joanna Hodge, Enda Howley, Jim Duggan, Ann Nowé and Florian Block and has published in prestigious journals such as Journal of Business Research, Technological Forecasting and Social Change and Neurocomputing.

In The Last Decade

Sam Devlin

53 papers receiving 835 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sam Devlin United Kingdom	15	515	180	109	101	92	54	879
Mei‐Hui Wang Taiwan	18	751 1.5×	99 0.6×	145 1.3×	155 1.5×	55 0.6×	97	1.3k
Nasser Ghasem-Aghaee Iran	17	600 1.2×	106 0.6×	30 0.3×	104 1.0×	116 1.3×	50	1.1k
Fariba Sadri United Kingdom	14	751 1.5×	64 0.4×	66 0.6×	104 1.0×	68 0.7×	38	1.1k
Anand S. Rao United States	11	1.2k 2.3×	113 0.6×	28 0.3×	152 1.5×	135 1.5×	23	1.6k
John-Jules Meyer Netherlands	17	1.1k 2.1×	95 0.5×	62 0.6×	150 1.5×	287 3.1×	82	1.3k
Hao Lin China	14	411 0.8×	143 0.8×	29 0.3×	107 1.1×	17 0.2×	51	976
Özgür Şimşek United States	17	498 1.0×	45 0.3×	80 0.7×	153 1.5×	49 0.5×	36	898
Ronald P. Loui United States	15	1.3k 2.4×	62 0.3×	55 0.5×	83 0.8×	95 1.0×	52	1.6k
Liz Sonenberg Australia	20	883 1.7×	135 0.8×	24 0.2×	202 2.0×	26 0.3×	73	1.3k
John‐Jules Ch. Meyer Netherlands	20	1.1k 2.1×	148 0.8×	29 0.3×	127 1.3×	121 1.3×	104	1.4k

Countries citing papers authored by Sam Devlin

Since Specialization

Citations

This map shows the geographic impact of Sam Devlin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sam Devlin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sam Devlin more than expected).

Fields of papers citing papers by Sam Devlin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sam Devlin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sam Devlin. The network helps show where Sam Devlin may publish in the future.

Co-authorship network of co-authors of Sam Devlin

This figure shows the co-authorship network connecting the top 25 collaborators of Sam Devlin. A scholar is included among the top collaborators of Sam Devlin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sam Devlin. Sam Devlin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Pérez-Liébana, Diego, et al.. (2022). Turning Zeroes into Non-Zeroes: Sample Efficient Exploration with Monte Carlo Graph Search. 300–306.

Devlin, Sam, et al.. (2022). Difference rewards policy gradients. Neural Computing and Applications. 37(19). 13163–13186. 2 indexed citations

Beck, Jacob, et al.. (2020). AMRL: Aggregated Memory For Reinforcement Learning. International Conference on Learning Representations. 4 indexed citations

Mannion, Patrick, Sam Devlin, Jim Duggan, & Enda Howley. (2018). Reward shaping for knowledge-based multi-objective multi-agent reinforcement learning. The Knowledge Engineering Review. 33. 36 indexed citations

Mannion, Patrick, Sam Devlin, Karl Mason, Jim Duggan, & Enda Howley. (2017). Policy invariance under reward transformations for multi-objective reinforcement learning. Neurocomputing. 263. 60–73. 27 indexed citations

Stafford, Tom, Sam Devlin, Rafet Sifa, & Anders Drachen. (2017). Exploration and Skill Acquisition in a Major Online Game. Cognitive Science. 9 indexed citations

Mannion, Patrick, Sam Devlin, Jim Duggan, & Enda Howley. (2017). Multi-agent credit assignment in stochastic resource management games. The Knowledge Engineering Review. 32. 5 indexed citations

Devlin, Sam, et al.. (2017). Clyde: A Deep Reinforcement Learning DOOM Playing Agent.. White Rose Research Online (University of Leeds, The University of Sheffield, University of York). 6 indexed citations

Fernandes, Kiran, Ignazio Cabras, Feng Li, et al.. (2016). A Conceptual Framework of Business Model Emerging Resilience. Northumbria Research Link (Northumbria University). 3 indexed citations

10.

Fernandes, Kiran, Ignazio Cabras, Feng Li, et al.. (2016). A strategic roadmap for BM change for the video-games industry. Northumbria Research Link (Northumbria University). 2 indexed citations

11.

Devlin, Sam, et al.. (2016). Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 12(1). 16–22. 18 indexed citations

12.

Devlin, Sam, et al.. (2016). Predicting Disengagement in Free-to-Play Games with Highly Biased Data. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 12(2). 143–150. 6 indexed citations

13.

Mannion, Patrick, Karl Mason, Sam Devlin, Jim Duggan, & Enda Howley. (2016). Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning: (Extended Abstract). Adaptive Agents and Multi-Agents Systems. 1345–1346. 14 indexed citations

14.

Devlin, Sam, et al.. (2016). Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems. arXiv (Cornell University). 503–511. 19 indexed citations

15.

Devlin, Sam, et al.. (2014). Potential-based difference rewards for multiagent reinforcement learning. Adaptive Agents and Multi-Agents Systems. 165–172. 43 indexed citations

16.

Efthymiadis, Kyriakos, Sam Devlin, & Daniel Kudenko⋆. (2014). Knowledge revision for reinforcement learning with abstract MDPs. Adaptive Agents and Multi-Agents Systems. 763–770. 4 indexed citations

17.

Efthymiadis, Kyriakos, Sam Devlin, & Daniel Kudenko⋆. (2013). Overcoming erroneous domain knowledge in plan-based reward shaping. Adaptive Agents and Multi-Agents Systems. 1245–1246. 2 indexed citations

18.

Soh, Leen‐Kiat, et al.. (2013). Potential-based reward shaping for POMDPs. Adaptive Agents and Multi-Agents Systems. 1123–1124. 5 indexed citations

19.

Devlin, Sam & Daniel Kudenko⋆. (2012). Dynamic potential-based reward shaping. Adaptive Agents and Multi-Agents Systems. 433–440. 75 indexed citations

20.

Devlin, Sam & Daniel Kudenko⋆. (2011). Theoretical considerations of potential-based reward shaping for multi-agent systems. Adaptive Agents and Multi-Agents Systems. 225–232. 48 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact