A MultiRobot Cooperative Confrontation Game with Limited Range of Motion * With the development of robotics technology, it is possible for an autonomous robot to accomplish conflict tasks such as target reconnaissance in confrontation environment. Pursuit and evasion conflicts represent challenging problems with important applications in aerospace and robotics. In this paper, we address a novel multirobot cooperative confrontation game which has three players: a Target, an Attacker and a Defender. The Attacker aims to capture the Target, while avoiding being captured by the faster Defender which is restricted to move in a circular area. The Target cooperates with the Defender to avoid being captured by the Attacker. We characterize some general properties of the multirobot cooperative confrontation game. Based on the explicit policy method and geometric analysis, the optimal control strategies and the winning conditions for the Attacker are obtained.

Tactical Reward Shaping: Bypassing Reinforcement Learning with StrategyBased Goals Deep Reinforcement Learning (DRL) has shown its promising capabilities to learn optimal policies directly from trial and error. However, learning can be hindered if the goal of the learning, defined by the reward function, is not optimal. We demonstrate that by setting the goaltarget of competition in a counterintuitive but intelligent way, instead of heuristically trying solutions through many hours, the DRL simulation can quickly converge into a winning strategy. The ICRADJI RoboMaster AI Challenge is a game of cooperation and competition between robots in a partially observable environment, quite similar to the CounterStrike game. Unlike the traditional approach to games, where the reward is given at winning the match or hitting the enemy, our DRL algorithm rewards our robots when in a geometricstrategic advantage, which implicitly increases the winning chances. Furthermore, we use Deep Q Learning (DQL) to generate multiagent paths for moving, which improves the cooperation between two robots by avoiding collision. Finally, we implement a variant A* algorithm with the same implicit geometric goal as DQL and compare results. We conclude that a wellset goal can put in question the need for learning algorithms, with geometricbased searches outperforming DQL in many orders of magnitude.