Enhanced robotic pursuit-evasion strategies in constrained game environments through deep reinforcement learning and self-play training

Key Points

• The aim is to improve robotic pursuit-evasion strategies using advanced learning techniques.
• Implemented deep reinforcement learning algorithms for strategy development.
• Utilized self-play training to enhance agent performance.
• Tested strategies in various constrained game environments.
• Demonstrated superior performance in pursuit-evasion tasks compared to traditional methods.
• Achieved more efficient strategies through iterative self-play training.
• Notable improvements in agent adaptability and decision-making in constrained settings.

Abstract