This paper presents a competitive deep reinforcement learning (DRL) structure for the robot control, the basic method of which is to make the two actors learn from each other with competition. And the improvement of training efficiency and learning ability of the competitive DRL model is verified by the experiments under the Gym environments. For the task of robot formation, we construct a cooperation mechanism exerting robot to take the optimal action due to the state and the next reward of other robots within a certain radius around based on the Kuhn-Munkres algorithm, to avoid collisions and blocking among robots. Lastly, we conduct the simulations with the above algorithms, and the results illustrate that the trained robots are capable of self-navigation, obstacle avoidance and formation without collisions.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.