论文信息 - Deep Reinforcement Learning for Mobile Robot Navigation

Deep Reinforcement Learning for Mobile Robot Navigation

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.

Jonas Stenzel | Martin Gromniak | Jonas Stenzel | M. Gromniak

[1] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[2] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.

[3] Andrew Y. Ng,et al. Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping , 1999, ICML.

[4] Gerald Tesauro,et al. Temporal difference learning and TD-Gammon , 1995, CACM.

[5] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.

[6] Mykel J. Kochenderfer,et al. Cooperative Multi-agent Control Using Deep Reinforcement Learning , 2017, AAMAS Workshops.

[7] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[8] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.

[9] Rama Chellappa,et al. Designing Deep Convolutional Neural Networks for Continuous Object Orientation Estimation , 2017, ArXiv.

[10] Richard S. Sutton,et al. Temporal credit assignment in reinforcement learning , 1984 .

[11] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).