| 研究課題/領域番号 |
21F21768
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| 配分区分 | 補助金 |
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| 研究機関 | お茶の水女子大学 |
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研究代表者 |
オベル加藤 ナタナエル お茶の水女子大学, 基幹研究院, 講師 (10749659)
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| 研究分担者 |
DA ROLD FEDERICO お茶の水女子大学, 基幹研究院, 外国人特別研究員
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| 研究期間 (年度) |
2021-11-18 – 2026-03-31
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| キーワード | Evolutionary Strategy / Synaptic Pruning / Quality-Diversity |
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| 研究実績の概要 |
During this fiscal year, we developed a specialized Evolutionary Strategy optimization algorithm that prunes deep neural network models during the optimization process. The proposed approach combines quality diversity algorithms and pruning operators that achieve a model compression equivalent to state-of-the-art gradient-based pruning techniques. This work thus suggests the validity of gradient-free optimization methods as an alternative to machine learning pruning approaches to reinforcement learning problems. Results were submitted and published at The Genetic and Evolutionary Computation Conference (GECCO 2022), a top-tier international conference.
To further understand the interaction between pruning and parameters optimization, we devised an experiment implementing a population-based genetic algorithm. The initial population is initialized with randomly pruned neural models that successfully learn the task in the experiments described above.
We are attempting to find correlations between information-theoretic measures and sub-network that rapidly converge to a solution to the given task.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The project implementation complies with the research plan with no significant delays for Work Package 1, which defined the workflow of the first year of the fellowship.
Task 1.1, "model selection and ES-GA training" was completed within month eight as expected.
The first objective of Task 1.2 is to refine the algorithm developed during the first months of the project implementation.
The technique utilized in the article published in GECCO 2022 is further refined by testing different genetic algorithm operators, such as selection criteria, mutation, and quality discriminators. Furthermore, the algorithm is applied to a larger set of reinforcement learning scenarios.
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| 今後の研究の推進方策 |
The implementation of Work Package 1 will continue with the application of existing and novel benchmarks to test the energy efficiency of our method. The implementation of Work Package 2 will focus on a robotic scenario to further assess the performance of our approach to mode compression via synaptic pruning.
The first experiment will be based on a robotic arm performing simple tasks such as reaching and avoiding objects.
This scenario aims at porting the model used in the experiments utilized in the first Work Package to an agent with a more complex kinematic. To facilitate the learning process, vision will not be implemented, and the robot will perceive the environment with Cartesian coordinates.
The second robotic experiment will use a generative model to extrapolate information from a camera. Furthermore, the experimental setup will include challenging tasks such s as grasping and placing objects in a target area.
The expected outcome is a publication in a top-tier international conference and a submission to a highly reputed robotic journal.
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