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Q Learning based Reinforcement Learning Approach to Bipedal Walking Control

Reinforcement learning has been active research area not only in machine learning but also in control engineering, operation research and robotics in recent years. It is a model free learning control method that can solve Markov decision problems. Q-learning is an incremental dynamic programming procedure that determines the optimal policy in a step-by-step manner. It is an online procedure for learning the optimal policy through experience gained solely on the basis of samples. A Q learning based reinforcement learning of a double inverted pendulum has been shown in this paper which reaches a limit cycle at the end of several learning cycles. The double inverted pendulum becomes stable, since the pole angle and pole angular velocity become zero. Stabilization of an equivalent double inverted pendulum representing a bipedal robot has been successfully implemented for balancing the pole angles in the required range using Q learning in Reinforcement Learning. Keywords—Q learning; Double inverted pendulum; Limit Cycle.