REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning (2404.03359v1)

Abstract: To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT). In contrast to the prevalent practice of validating RL models based on their optimal behavior learned during training, we posit that considering a range of edge-case trajectories provides a more comprehensive understanding of their inherent behavior. To induce such scenarios, we introduce a disturbance to the initial state, optimizing it through an evolutionary algorithm to generate a diverse population of demonstrations. To evaluate the fitness of trajectories, REACT incorporates a joint fitness function that encourages both local and global diversity in the encountered states and chosen actions. Through assessments with policies trained for varying durations in discrete and continuous environments, we demonstrate the descriptive power of REACT. Our results highlight its effectiveness in revealing nuanced aspects of RL models' behavior beyond optimal performance, thereby contributing to improved interpretability.

• The paper introduces the REACT framework that uses evolutionary optimization and a joint fitness metric to generate diverse RL trajectories for improved interpretability.
• It employs deliberate disturbances in initial states to uncover nuanced decision-making behaviors and differentiate between early convergence, optimal, and overfitting phases.
• The framework provides actionable insights into RL models, identifying vulnerabilities and guiding improvements to enhance transparency and robustness.

Revealing Evolutionary Action Consequence Trajectories (REACT) for Enhancing Interpretability in Reinforcement Learning

Introduction

The field of Reinforcement Learning (RL) has significantly benefited from advancements in artificial intelligence, particularly with the use of parameterized function approximation models for decision-making tasks. However, ensuring these models' behaviors are interpretable holds paramount importance, especially in applications where transparency and trust are crucial. Traditional RL validation methods, focusing majorly on optimal learned behavior, often overlook the interpretability aspect, hence limiting the understanding of the model's decision-making process, particularly in scenarios not encountered during training.

REACT Framework

To address these limitations, this paper introduces an interpretability framework, Revealing Evolutionary Action Consequence Trajectories (REACT), designed to enhance the understanding of RL models by evaluating their behavior across a spectrum of scenarios, including edge cases not specifically trained for. REACT proposes inducing disturbances in initial states and employing an evolutionary optimization approach to generate a diverse set of trajectories. This diversity reveals nuanced aspects of RL models beyond optimal performance, offering a richer interpretation of the model's behavior.

A key contribution of REACT is its joint fitness metric, integrating both local and global diversity alongside action certainty to evaluate the fitness of trajectories. This metric enables a more comprehensive analysis of the RL model’s behavior, accounting for variability in encountered states and chosen actions.

Evaluation and Results

REACT was assessed across various policies trained in discrete and continuous environments, with results demonstrating its capability in uncovering distinctive behaviors and providing a deeper understanding of the RL policies evaluated. Notably, the framework effectively revealed differences in policy behaviors across multiple training stages, illustrating its utility in distinguishing between early convergence, optimal, and potentially overfitting behaviors in RL models.

Theoretical and Practical Implications

From a theoretical standpoint, REACT introduces a novel approach to interpretability in RL through the lens of evolutionary optimization, which is both model-agnostic and applicable post-training, providing flexibility in its application across different RL architectures. Practically, the generated diverse set of demonstration trajectories offers valuable insights into the model's decision-making process, assisting in identifying potential improvements and understanding behavior under various scenarios.

Future Directions in AI

The implications of REACT extend into future developments in AI by proposing a shift in focus towards interpretability and understanding of RL models. Speculatively, this framework could pave the way for more sophisticated interpretability mechanisms that adapt evolutionary strategies for broader applications, including more complex and high-dimensional decision-making tasks.

Additionally, integrating REACT-generated demonstrations into the training process could further refine model behaviors, enhance robustness, and potentially lead to the development of more generalizable RL models. Investigating the extension of REACT to incorporate variations in environmental dynamics or task objectives could also broaden its applicability and effectiveness in interpreting RL models.

Conclusion

By enabling a deeper understanding of RL models through the generation and evaluation of diverse behavior demonstrations, REACT represents a significant step forward in addressing the challenges of interpretability in RL. Its capacity to reveal the underlying decision-making strategies and potential vulnerabilities of RL policies not only enhances transparency and trust in AI systems but also opens new avenues for research and development in the field of interpretable machine learning.