Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences (1807.08706v1)

Abstract: Machine Learning models become increasingly proficient in complex tasks. However, even for experts in the field, it can be difficult to understand what the model learned. This hampers trust and acceptance, and it obstructs the possibility to correct the model. There is therefore a need for transparency of machine learning models. The development of transparent classification models has received much attention, but there are few developments for achieving transparent Reinforcement Learning (RL) models. In this study we propose a method that enables a RL agent to explain its behavior in terms of the expected consequences of state transitions and outcomes. First, we define a translation of states and actions to a description that is easier to understand for human users. Second, we developed a procedure that enables the agent to obtain the consequences of a single action, as well as its entire policy. The method calculates contrasts between the consequences of a policy derived from a user query, and of the learned policy of the agent. Third, a format for generating explanations was constructed. A pilot survey study was conducted to explore preferences of users for different explanation properties. Results indicate that human users tend to favor explanations about policy rather than about single actions.

Insights into Contrastive Explanations for Reinforcement Learning

The paper by van der Waa et al. presents a method to generate explanations for Reinforcement Learning (RL) agents, focusing on the expected consequences of state transitions and outcomes. The motivation for this research emerges from the challenge of transparency in RL models, which inherently lack the ability to convey their decision-making processes to human users, undermining trust and usability, especially in high-stakes domains such as healthcare and defense.

Methodology Overview

The authors propose a novel approach where RL agents explain their actions and policies by simulating potential outcomes and contrasting them with alternative user-specified actions. Key steps in the proposed methodology include:

1. Translation of Actions and States: The method involves converting states and actions into user-friendly descriptions, facilitating a more intuitive understanding of the agent’s behavior.
2. Simulation of Expected Consequences: The authors leverage the transition model $T$ to forecast potential outcomes, constructing a Markov Chain reflective of the agent's expected state visits and derived consequences.
3. Contrastive Explanations: The paper adopts a contrastive question framework where explanations involve comparisons between the agent's learned policy and alternative policies proposed by users. The proposed method transforms user queries into policies using state-action value functions (Q-functions), establishing a comparative basis grounded in expected rewards.

Numerical Results and User Preferences

The authors conducted a pilot survey to evaluate user preferences regarding explanation attributes. Findings reveal a tendency among users to favor comprehensive policy-oriented explanations over those focusing on isolated actions. This suggests a preference for holistic insights into decision-making processes, which can enhance understanding and foster trust in RL agents.

Implications and Future Developments

Practically, the methodology paves the way towards integrating explainability into RL systems, enabling users to make informed assessments about agent behaviors. Theoretically, it extends eXplainable Artificial Intelligence (XAI) frameworks into the domain of RL by employing contrastive logic to elucidate decision pathways.

Future work will likely focus on scaling these methods to more complex RL settings, addressing computational challenges associated with simulating large state spaces. Furthermore, enhancing the translation functions for state and action interpretation could improve the granularity and utility of explanations. Further user studies could examine the impact of detailed explanations on user trust and decision-making support.

In conclusion, this paper contributes a substantive advancement towards rendering RL systems interpretable and fostering trust in automated decision-making, aligning with broader objectives in the expanding field of XAI.