Deep Reinforcement Learning for Mobile Robot Navigation

M. Gromniak, Jonas Stenzel
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引用次数: 7

Abstract

While navigation is arguable the most important aspect of mobile robotics, complex scenarios with dynamic environments or with teams of cooperative robots are still not satisfactory solved yet. Motivated by the recent successes in the reinforcement learning domain, the application of deep reinforcement learning to robot navigation was examined in this paper. In particular this required the development of a training procedure, a set of actions available to the robot, a suitable state representation and a reward function. The setup was evaluated using a simulated real-time environment. A reference setup, different goal-oriented exploration strategies and two different robot kinematics (holonomic, differential) were compared in the evaluation. In a challenging scenario with obstacles at changing locations in the environment the robot was able to reach the desired goal in 93% of the episodes.
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移动机器人导航的深度强化学习
虽然导航可以说是移动机器人最重要的方面,但动态环境或协作机器人团队的复杂场景仍未得到令人满意的解决。基于近年来在强化学习领域取得的成功,本文研究了深度强化学习在机器人导航中的应用。特别是,这需要开发一个训练程序、一组机器人可用的动作、一个合适的状态表示和一个奖励函数。使用模拟的实时环境对该设置进行了评估。在评价中比较了参考设置、不同目标导向的探索策略和两种不同的机器人运动学(完整的、微分的)。在具有挑战性的场景中,障碍物在环境中不断变化的位置,机器人能够在93%的情节中达到预期的目标。
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