Policy Architecture

Policy architecture in artificial intelligence focuses on designing efficient and effective structures for representing and learning decision-making strategies, primarily within reinforcement learning and related fields. Current research emphasizes improving sample efficiency and generalization capabilities through novel architectures, such as those incorporating hierarchical structures, time-indexing, and attention mechanisms, alongside algorithms like counterfactual regret minimization and trust region optimization. These advancements aim to enable more robust and adaptable policies for complex tasks in robotics, game playing, and automated system management, ultimately leading to more capable and reliable intelligent systems.