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http://dx.doi.org/10.7746/jkros.2020.15.4.398

Deep Reinforcement Learning of Ball Throwing Robot's Policy Prediction  

Kang, Yeong-Gyun (Mechanical Engineering, Sogang University)
Lee, Cheol-Soo (Mechanical Engineering, Sogang University)
Publication Information
The Journal of Korea Robotics Society / v.15, no.4, 2020 , pp. 398-403 More about this Journal
Abstract
Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.
Keywords
Robotic Arm; Deep Reinforcement Learning; Ball Throwing;
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Times Cited By KSCI : 2  (Citation Analysis)
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