Abstract
In this paper, we attempt to make a compact humanoid robot acquire the giant swing motion without any robotic models by using reinforcement learning; in particular, this paper applies Q-learning algorithm. The reinforcement learning method is said to be not suitable for dynamic tasks because Markov property is not necessarily guaranteed. However, we tried to avoid this problem by considering angular velocity in state definition and averaging Q-value at every step to reduce the dynamic noise. The ODE-based simulator is also used to find the best reward for acquiring the giant motion. Further, learning process is divided into two stages, and finally they enable attractive giant swing motion like gymnast.
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