Policy Representation

Policy representation in artificial intelligence focuses on designing effective ways to encode and learn decision-making strategies, aiming to improve the efficiency, robustness, and generalizability of intelligent agents. Current research explores diverse approaches, including energy-based models, diffusion probability models, and behavior trees, often incorporating techniques like successor features and hierarchical structures to handle complex tasks and multi-modal environments. These advancements are crucial for improving reinforcement learning algorithms, enabling more adaptable and efficient robots, and facilitating the safe and reliable deployment of AI systems in real-world applications.