Editorial Special Issue on Deep Reinforcement Learning and Adaptive Dynamic Programming.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Control Systems

Background:

• Deep reinforcement learning and adaptive dynamic programming (deep RL/ADP) combines deep learning's perception with reinforcement learning's decision-making.
• This integration enables AI systems to more closely mimic human cognitive processes.
• Deep RL/ADP has demonstrated significant successes across various domains, including gaming, robotics, and healthcare.

Purpose of the Study:

• To present the latest advancements in deep reinforcement learning and adaptive dynamic programming.
• To address the ongoing challenges in theoretical analysis, such as convergence and stability.
• To encourage further research into improving learning efficiency and practical applications of deep RL/ADP.

Main Methods:

• Focus on theoretical analysis of deep RL/ADP algorithms.
• Development of novel algorithms to enhance learning efficiency.
• Exploration of hybrid approaches combining deep RL/ADP with other methodologies.
• Presentation of practical demonstrations and case studies.

Main Results:

• Highlighting the successful integration of deep learning (DL) for perception and reinforcement learning (RL) or adaptive dynamic programming (ADP) for decision-making.
• Showcasing advancements in applying deep RL/ADP to complex problems like video games, Go, robotics, smart driving, and healthcare.
• Identifying persistent challenges in theoretical guarantees (convergence, stability, optimality) and learning efficiency.

Conclusions:

• Deep RL/ADP represents a significant step towards human-like artificial intelligence.
• Further theoretical investigation and algorithmic innovation are crucial for advancing the field.
• Increased practical demonstrations are needed to fully realize the potential of deep RL/ADP across diverse applications.