- author = "Stephen Kelly and Malcolm I. Heywood",
- title = "Emergent Solutions to High-Dimensional Multi-Task Reinforcement Learning",
- journal = "Evolutionary Computation",
- year = "2018",
- volume = "26",
- number = "3",
- pages = "347--380",
- month = "Fall",
- keywords = "genetic algorithms, genetic programming, Emergent modularity, cooperative coevolution, reinforcement learning, multi-task learning",
- ISSN = "1063-6560",
-
URL = "
http://www.human-competitive.org/sites/default/files/kelly-paper.pdf",
-
URL = "
https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00232",
-
DOI = "
doi:10.1162/evco_a_00232",
- size = "33 pages",
- abstract = "Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning, increasingly face the challenge of scaling to dynamic, high-dimensional, and partially observable environments. Significant attention is being paid to frameworks from deep learning, which scale to high-dimensional data by decomposing the task through multi-layered neural networks. While effective, the representation is complex and computationally demanding. In this work we propose a framework based on Genetic Programming which adaptively complexifies policies through interaction with the task. We make a direct comparison with several deep reinforcement learning frameworks in the challenging Atari video game environment as well as more traditional reinforcement learning frameworks based on a priori engineered features. Results indicate that the proposed approach matches the quality of deep learning while being a minimum of three orders of magnitude simpler with respect to model complexity. This results in real-time operation of the champion RL agent without recourse to specialized hardware support. Moreover, the approach is capable of evolving solutions to multiple game titles simultaneously with no additional computational cost. In this case, agent behaviours for an individual game as well as single agents capable of playing all games emerge from the same evolutionary",
-
notes = "Silver Winner 2018 HUMIES
Extended Paper Invited from GECCO 2017 Also known as \cite{8637202}",
