Batch Policy

Batch policy optimization focuses on learning effective policies from a fixed dataset of past experiences, unlike online reinforcement learning which learns through continuous interaction. Current research emphasizes efficient algorithms that balance performance and computational cost, such as those employing hierarchical structures or batch-by-batch updates to address the challenges of multi-agent scenarios and hierarchical decision-making. A key challenge lies in robust model selection given the inherent limitations of offline data, with recent work exploring theoretical bounds and algorithmic strategies to mitigate the impact of dataset shift and approximation errors. These advancements are crucial for improving the applicability of reinforcement learning in situations where online interaction is impractical or expensive.