Johan Källström

425 total citations · 1 hit paper
11 papers, 214 citations indexed

About

Johan Källström is a scholar working on Artificial Intelligence, Control and Systems Engineering and Management Science and Operations Research. According to data from OpenAlex, Johan Källström has authored 11 papers receiving a total of 214 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Control and Systems Engineering and 2 papers in Management Science and Operations Research. Recurrent topics in Johan Källström's work include Reinforcement Learning in Robotics (5 papers), Guidance and Control Systems (2 papers) and Evolutionary Algorithms and Applications (2 papers). Johan Källström is often cited by papers focused on Reinforcement Learning in Robotics (5 papers), Guidance and Control Systems (2 papers) and Evolutionary Algorithms and Applications (2 papers). Johan Källström collaborates with scholars based in Sweden, Australia and United States. Johan Källström's co-authors include Fredrik Heintz, Patrick Mannion, Peter Vamplew, Conor F. Hayes, Gabriel de Oliveira Ramos, Diederik M. Roijers, Richard Dazeley, Roxana Rădulescu, Luisa Zintgraf and Athirai A. Irissappane and has published in prestigious journals such as Autonomous Agents and Multi-Agent Systems, The Aeronautical Journal and Virtual Community of Pathological Anatomy (University of Castilla La Mancha).

In The Last Decade

Johan Källström

11 papers receiving 202 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A practical guide to multi-objective reinforcement learning and planning

2022 158 citations Conor F. Hayes, Roxana Rădulescu et al. Virtual Community of Pathological Anatomy (University of Castilla La Mancha) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Johan Källström Sweden	5	83	39	30	25	24	11	214
Eugenio Bargiacchi Netherlands	4	71 0.9×	33 0.8×	29 1.0×	30 1.2×	5 0.2×	8	190
Conor F. Hayes Ireland	5	86 1.0×	52 1.3×	29 1.0×	28 1.1×	5 0.2×	11	209
Mathieu Reymond Ireland	5	74 0.9×	45 1.2×	29 1.0×	33 1.3×	5 0.2×	7	198
Hangyu Mao China	8	129 1.6×	24 0.6×	63 2.1×	27 1.1×	15 0.6×	24	232
Guangping Zeng China	9	104 1.3×	14 0.4×	54 1.8×	21 0.8×	12 0.5×	56	280
G. Jeyakumar India	9	118 1.4×	70 1.8×	11 0.4×	32 1.3×	11 0.5×	71	246
Antonín Komenda Czechia	10	189 2.3×	17 0.4×	60 2.0×	9 0.4×	34 1.4×	50	317
Arwin Datumaya Wahyudi Sumari Indonesia	8	94 1.1×	31 0.8×	29 1.0×	52 2.1×	16 0.7×	83	260
Huáscar Espinoza France	7	52 0.6×	17 0.4×	28 0.9×	8 0.3×	11 0.5×	15	186
Jamil Abedalrahim Jamil Alsayaydeh Malaysia	8	46 0.6×	11 0.3×	51 1.7×	45 1.8×	15 0.6×	48	242

Countries citing papers authored by Johan Källström

Since Specialization

Citations

This map shows the geographic impact of Johan Källström'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 Johan Källström with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johan Källström more than expected).

Fields of papers citing papers by Johan Källström

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Johan Källström. 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 Johan Källström. The network helps show where Johan Källström may publish in the future.

Co-authorship network of co-authors of Johan Källström

This figure shows the co-authorship network connecting the top 25 collaborators of Johan Källström. A scholar is included among the top collaborators of Johan Källström 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 Johan Källström. Johan Källström is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Källström, Johan. (2023). Reinforcement Learning for Improved Utility of Simulation-Based Training. Linköping studies in science and technology. Dissertations. 1 indexed citations

2.

Hayes, Conor F., Roxana Rădulescu, Eugenio Bargiacchi, et al.. (2022). A practical guide to multi-objective reinforcement learning and planning. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 158 indexed citations breakdown →

3.

Vamplew, Peter, Benjamin J. Smith, Johan Källström, et al.. (2022). Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021). Autonomous Agents and Multi-Agent Systems. 36(2). 18 indexed citations

4.

Källström, Johan, Rego Granlund, & Fredrik Heintz. (2022). Design of simulation-based pilot training systems using machine learning agents. The Aeronautical Journal. 126(1300). 907–931. 7 indexed citations

5.

Källström, Johan. (2020). Adaptive Agent-Based Simulation for Individualized Training. KTH Publication Database DiVA (KTH Royal Institute of Technology). 2193–2195. 1 indexed citations

6.

Källström, Johan & Fredrik Heintz. (2020). Learning Agents for Improved Efficiency and Effectiveness in Simulation-Based Training. 1–2. 1 indexed citations

7.

Källström, Johan & Fredrik Heintz. (2020). Agent Coordination in Air Combat Simulation using Multi-Agent Deep Reinforcement Learning. KTH Publication Database DiVA (KTH Royal Institute of Technology). 2157–2164. 11 indexed citations

8.

Källström, Johan, et al.. (2020). Improving Usability of Search and Rescue Decision Support Systems: WARA-PS Case Study. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1251–1254. 3 indexed citations

9.

Källström, Johan & Fredrik Heintz. (2019). Tunable Dynamics in Agent-Based Simulation using Multi-Objective Reinforcement Learning. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1–7. 7 indexed citations

10.

Källström, Johan & Fredrik Heintz. (2019). Multi-Agent Multi-Objective Deep Reinforcement Learning for Efficient and Effective Pilot Training. Linköping electronic conference proceedings. 162. 101–111. 4 indexed citations

11.

Källström, Johan & Fredrik Heintz. (2019). Reinforcement Learning for Computer Generated Forces using Open-Source Software. 1–11. 3 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.

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