Developing End-to-End Control Policies for Robotic Swarms Using Deep Q-learning
Yufei Wei*, Xiaotong Nie*, Motoaki Hiraga*, Kazuhiro Ohkura*, and Zlatan Car**
*Graduate School of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan
**Faculty of Engineering, University of Rijeka
58 Vukovarska, Rijeka 51000, Croatia
In this study, the use of a popular deep reinforcement learning algorithm – deep Q-learning – in developing end-to-end control policies for robotic swarms is explored. Robots only have limited local sensory capabilities; however, in a swarm, they can accomplish collective tasks beyond the capability of a single robot. Compared with most automatic design approaches proposed so far, which belong to the field of evolutionary robotics, deep reinforcement learning techniques provide two advantages: (i) they enable researchers to develop control policies in an end-to-end fashion; and (ii) they require fewer computation resources, especially when the control policy to be developed has a large parameter space. The proposed approach is evaluated in a round-trip task, where the robots are required to travel between two destinations as much as possible. Simulation results show that the proposed approach can learn control policies directly from high-dimensional raw camera pixel inputs for robotic swarms.
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