QUANTITATIVE STUDIES OF DEEP REINFORCEMENT LEARNING IN GAMING, ROBOTICS AND REAL-WORLD CONTROL SYSTEMS

Abstract

Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm with profound implications for gaming, robotics, real-world control systems, and beyond. This quantitative analysis delves into the applications of DRL across these domains, assessing its capabilities, challenges, and potential. In the gaming realm, we showcase DRL's prowess through significant score improvements in benchmark games, with DQN and PPO leading the way. A3C underscores its adaptability through strong generalization within the gaming domain. While specific robotics and real-world control results are not presented here, their promise in enhancing task completion and precision is evident. Sample efficiency and safety strategies address critical concerns, demonstrating DRL's capacity to optimize resource utilization and ensure robustness. Generalization and transfer learning underscore DRL's adaptability to new scenarios. While these findings are not empirical but illustrative, they emphasize DRL's versatility and highlight the need for continued research to unlock its full potential in addressing complex real-world challenges.