Evolving simple programs for playing Atari games
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Abstract (arXiv)
Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision. A similar approach can be applied to Atari playing. Programs are evolved using mixed type CGP with a function set suited for matrix operations, including image processing, but allowing for controller behavior to emerge. While the programs are relatively small, many controllers are competitive with state of the art methods for the Atari benchmark set and require less training time. By evaluating the programs of the best evolved individuals, simple but effective strategies can be found.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Jun 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2; 2025: 0; 2026: 1.
8 papers.
- 2023Evolutionary Reinforcement Learning: A Surveycited by 29
- 2024End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanationscited by 17
- 2022Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Searchcited by 9
- 2024From Code to Play: Benchmarking Program Search for Games Using Large Language Modelscited by 5
- 2023Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysiscited by 4
- 2021Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactorcited by 1
- 2022Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agentscited by 1
- 2026Synthesizing Reactive Character Behaviors for Continuous Games via Programmatic Policy Searchcited by 0
- 2026Synthesizing Reactive Character Behaviors for Continuous Games via Programmatic Policy Search21 Sep 2026
- 2024From Code to Play: Benchmarking Program Search for Games Using Large Language Models5 Dec 2024
- 2024End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations19 Mar 2024
- 2023Evolutionary Reinforcement Learning: A Survey7 Mar 2023
- 2023Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image Analysis28 Feb 2023
- 2022Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search30 Dec 2022
- 2022Qualitative Differences Between Evolutionary Strategies and Reinforcement Learning Methods for Control of Autonomous Agents16 May 2022
- 2021Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactor30 Aug 2021