Reinforcement Learning for Autonomous Software Agents: Recent Advances and Applications

Abstract

Reinforcement learning (RL) has emerged as a powerful paradigm for training autonomous software agents to make decisions in complex and dynamic environments. This abstract explores recent advances and applications of RL in diverse domains, highlighting its transformative potential and current challenges. Recent advances in RL algorithms, particularly deep reinforcement learning (DRL), have enabled significant breakthroughs in autonomous decision-making tasks. By leveraging deep neural networks, DRL algorithms can learn complex representations of state-action spaces, facilitating more effective exploration and exploitation strategies. Additionally, innovations in algorithmic improvements, such as prioritized experience replay and distributional RL, have enhanced the stability and sample efficiency of RL algorithms, enabling their deployment in real-world applications. The applications of RL span a wide range of domains, including robotics, autonomous vehicles, game playing, finance, and healthcare. In robotics, RL enables autonomous agents to learn locomotion, manipulation, and navigation tasks in complex and unstructured environments. Autonomous vehicles leverage RL for decision-making in dynamic traffic scenarios, improving safety and efficiency on the road. In finance, RL algorithms are employed for portfolio optimization, algorithmic trading, and risk management, enhancing investment strategies and decision-making processes. Moreover, in healthcare, RL facilitates personalized treatment planning, clinical decision support, and medical image analysis, empowering clinicians to deliver tailored care to patients. Despite the promising advancements and applications, RL still faces several challenges that limit its widespread adoption and scalability. These challenges include sample inefficiency, exploration-exploitation trade-offs, safety and reliability concerns, and the need for explainability and interpretability in decision-making processes. Addressing these challenges requires interdisciplinary collaboration, research in algorithmic advancements, and the development of robust evaluation frameworks.