In the paper, a reinforcement learning technique is applied to produce a central pattern generation-based rhythmic motion control of a robotic salamander while moving toward a fixed target. Since its action spaces are continuous and there are various uncertainties in an environment that the robot moves, it is difficult for the robot to apply a conventional reinforcement learning algorithm. In order to overcome this issue, a deep deterministic policy gradient among the deep reinforcement learning algorithms is adopted. The robotic salamander and the environments where it moves are realized using the Gazebo dynamic simulator under the robot operating system environment. The algorithm is applied to the robotic simulation for the continuous motions in two different environments, i.e., from a firm ground to a mud. Through the simulation results, it is verified that the robotic salamander can smoothly move toward a desired target by adapting to the environmental change from the firm ground to the mud. The gradual improvement in the stability of learning algorithm is also confirmed through the simulations.

中文翻译：

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利用机器人sal的强化学习适应环境变化

在本文中，一种强化学习技术被应用于在向固定目标移动的同时，对机器人sal进行基于中央模式生成的节律运动控制。由于其动作空间是连续的，并且在机器人移动的环境中存在各种不确定性，因此机器人难以应用常规的强化学习算法。为了克服这个问题，在深度强化学习算法中采用了深度确定性策略梯度。机器人sal及其移动的环境是在机器人操作系统环境下使用Gazebo动态模拟器实现的。该算法应用于机器人仿真，以在两种不同的环境（即从坚硬的地面到泥泞的环境）中进行连续运动。通过仿真结果，可以证明机器人sal可以通过适应从坚硬的地面到泥泞的环境变化而平稳地向期望的目标移动。通过仿真也证实了学习算法稳定性的逐步提高。