車速追従制御のための強化学習における転移可能な方策の学習手法

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  • Learning Transferable Policy in Reinforcement Learning for Vehicle Velocity Tracking Control
  • シャソク ツイジュウ セイギョ ノ タメ ノ キョウカ ガクシュウ ニ オケル テンイ カノウ ナ ホウサク ノ ガクシュウ シュホウ

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<p>We propose the control system for driving robot using Hierarchical Reinforcement Learning. Driving Robots are playing an active role in test driving for evaluating fuel consumption and exhaust gas of automobiles. We can consider Reinforcement Learning as one of the control methods for driving robot. The control system using Reinforcement Learning has the advantage that there is no need to adjust parameters manually. However, Reinforcement Learning suffer from poor sample efficiency because it requires a lot of trials. In this research, we propose the control system for driving robot using the algorithm for learning hierarchical policy. Moreover, we introduce State Abstraction in Hierarchical Reinforcement Learning. By using abstract state, each low-level policy specialize in distinct behavior. The advantage of this method is that we can improve the sample efficiency by transferring low-level policies learned using multiple vehicles. The experimental result shows that the proposed method improve the sample efficiency in vehicle velocity tracking task.</p>

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