Motion Planning for a Snake Robot using Double Deep Q-Learning

Motion planning for a snake robot in an unknown complex environment is a long-standing research problem because of the complex control of the modular mechanism. We propose deep reinforcement learning-based novel framework for motion planning. In this model-free framework, we propose a double deep Q-learning-based technique to learn the optimal policy for reaching the goal point from a random start...