Reinforcement and Imitation Learning via Interactive No-Regret Learning (1406.5979v1)

Abstract: Recent work has demonstrated that problems-- particularly imitation learning and structured prediction-- where a learner's predictions influence the input-distribution it is tested on can be naturally addressed by an interactive approach and analyzed using no-regret online learning. These approaches to imitation learning, however, neither require nor benefit from information about the cost of actions. We extend existing results in two directions: first, we develop an interactive imitation learning approach that leverages cost information; second, we extend the technique to address reinforcement learning. The results provide theoretical support to the commonly observed successes of online approximate policy iteration. Our approach suggests a broad new family of algorithms and provides a unifying view of existing techniques for imitation and reinforcement learning.

• The paper demonstrates the AggreVaTe algorithm's ability to incorporate cost-to-go information into interactive imitation learning, reducing the cumulative cost compared to expert policies.
• It extends to reinforcement learning through the No-Regret Policy Iteration method, ensuring stable policy improvements via iterative online updates.
• The unified framework bridges imitation and reinforcement learning, offering strong theoretical guarantees that align the learner’s performance with expert-level decision-making.

Reinforcement and Imitation Learning via Interactive No-Regret Learning

The paper by Stephane Ross and J. Andrew Bagnell addresses key challenges in the domains of imitation learning and reinforcement learning through the lens of no-regret online learning methodologies. The research proposes significant advancements in tackling problems where the learner’s predictions affect the input distribution they will encounter, a nuance observed particularly in imitation learning and structured prediction domains. A core contribution of the paper is demonstrating how cost-to-go information can be leveraged in an interactive learning context, leading to the development of a broad family of algorithms that provide a unifying framework for both imitation and reinforcement learning.

Overview of Contributions

1. Interactive Imitation Learning with Cost Information: The paper introduces the AggreVaTe (Aggregate Values to Imitate) algorithm, which enhances existing imitation learning techniques by incorporating the cost-to-go information. Unlike traditional approaches that purely mimic expert actions without considering the long-term costs of errors, AggreVaTe allows for more nuanced decision-making by minimizing the expected cost-to-go. This method asserts stronger guarantees than immediate action imitation methods by leveraging statistical regret frameworks.
2. Extension to Reinforcement Learning: The methodology is expanded beyond imitation learning to address reinforcement learning tasks. By using approximate policy iteration (API) tactics and incorporating a no-regret learning approach, the paper presents the No-Regret Policy Iteration (NRPI) algorithm. NRPI achieves stable policy iteration with robust theoretical guarantees, providing an underlying explanation for the empirically observed success of online policy iteration techniques.
3. Theoretical Guarantees: The paper rigorously establishes theoretical bounds for both AggreVaTe and NRPI. In particular, the analysis reveals that as the number of iterations increases, the cumulative cost of the learned policy approaches that of the expert, with the added advantage of explicitly considering trade-offs between different errors based on their future impact (cost-to-go).
4. Bridging Imitation and Reinforcement Learning: By treating the learning procedure as a cost-sensitive classification task, the research unifies approaches to imitation and reinforcement learning under a common no-regret learning framework. This perspective facilitates the development of more general and powerful learning algorithms capable of handling diverse control and decision-making scenarios.

Implications and Future Work

The inclusion of cost-to-go information in imitation learning methodologies advocates for a refined approach to policy learning where long-term implications of actions are explicitly considered. This has practical implications in robotics and AI systems where safety and performance are interlinked with the cost-effectiveness of the decisions made by learned policies. Moreover, the extension to reinforcement learning may inspire more research into interactive and online learning environments where policies are continually updated as more interaction data becomes available.

For future research, exploring a wider array of no-regret learners within this framework could elucidate practical trade-offs between computational efficiency and accuracy in various application domains. The findings also call for deeper investigations into reducing computational burdens associated with exploring cost-to-go estimates in large state-action spaces, potentially through sampling techniques or approximations.

The paper delineates a powerful analytical technique for improving decision-making algorithms by unifying imitation learning with reinforcement learning theory, showcasing the robustness of no-regret learning as a foundational methodology in these domains.