Toward Information Theoretic Active Inverse Reinforcement Learning (2501.00381v1)

Abstract: As AI systems become increasingly autonomous, aligning their decision-making to human preferences is essential. In domains like autonomous driving or robotics, it is impossible to write down the reward function representing these preferences by hand. Inverse reinforcement learning (IRL) offers a promising approach to infer the unknown reward from demonstrations. However, obtaining human demonstrations can be costly. Active IRL addresses this challenge by strategically selecting the most informative scenarios for human demonstration, reducing the amount of required human effort. Where most prior work allowed querying the human for an action at one state at a time, we motivate and analyse scenarios where we collect longer trajectories. We provide an information-theoretic acquisition function, propose an efficient approximation scheme, and illustrate its performance through a set of gridworld experiments as groundwork for future work expanding to more general settings.

• The paper formulates active inverse reinforcement learning for querying full expert trajectories and adapts the expected information gain acquisition function for this setting.
• It introduces an efficient approximation scheme using Bayesian optimization to address the computational infeasibility of directly computing expected information gain.
• Empirical validation in gridworlds shows superior performance over baselines in reducing reward uncertainty and improving apprentice policy performance.

Toward Information Theoretic Active Inverse Reinforcement Learning

The paper "Toward Information Theoretic Active Inverse Reinforcement Learning" rigorously addresses the challenge of aligning autonomous AI systems with human preferences through the lens of inverse reinforcement learning (IRL). The authors identify that one of the critical obstacles in applying IRL is the substantial human effort required to provide demonstrations, as explicitly creating reward functions for domains such as autonomous driving or robotics is infeasible. They propose active IRL as a solution, which aims to maximize the information gain from human demonstrations while minimizing the necessary input by strategically selecting the most informative trajectories for demonstration.

Underpinned by an information-theoretic approach, the research delineates a framework for querying full expert trajectories rather than individual actions at isolated states, arguing that the former is more practical and informative for complex domains. Prior work predominantly focused on single-state queries, but the authors argue for employing full trajectories, presenting a robust methodology for high-frequency action domains like autonomous driving.

The research articulates several significant contributions:

1. Formulation of Active IRL for Full Trajectories: The paper formulates the problem of actively querying entire trajectories in the context of IRL. This approach is more aligned with how human experts naturally provide demonstrations and is anticipated to elicit richer information about the reward structure.
2. Adaptation of Expected Information Gain (EIG) Acquisition Function: A pivotal method proposed is the adaptation of the expected information gain acquisition function to the setting of trajectory-level queries. This adaptation facilitates the selection of trajectory queries that will most efficiently reduce uncertainty about the reward function.
3. Efficient EIG Approximation Scheme: Recognizing the computational infeasibility of directly computing EIG in high-dimensional spaces, the authors introduce an efficient approximation algorithm. The method leverages Bayesian optimization to enhance computational efficiency.
4. Empirical Validation on Gridworlds: The developed concepts are empirically validated through experiments in gridworld environments. These initial results demonstrate superior performance of the proposed method compared to baseline strategies, particularly in reducing posterior entropy over reward parameters.

The empirical results highlight that the EIG-based methods with full trajectories consistently surpass other baselines in terms of both posterior entropy reduction and apprentice policy performance. These findings indicate that the proposed approach utilizes demonstration efforts more efficiently, paving the way for practical applications in more intricate real-world domains.

The implications of this research are profound both practically and theoretically. Practically, the research provides a method for reducing the necessity of costly human demonstrations in IRL, potentially increasing the scalability and applicability of IRL in AI systems that must be tightly aligned with human preferences. Theoretically, it extends existing IRL frameworks to accommodate full trajectories, fostering new avenues of research in information-theoretic methods for IRL. The adaptation of traditional Bayesian metrics to the active learning scenario in this context illustrates the dynamic interplay between sampling efficiency and demonstration informativeness in reward learning.

Future research directions encouraged by this paper include extending the framework to continuous state spaces and implementing the algorithm in real-world scenarios where dynamic and complex human-AI interaction is prevalent. This paper sets a foundational stepping stone towards more efficient and informative agent learning in alignment-critical applications.