Trajectory-Based Explainability for Reinforcement Learning
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Solution Overview
Problem
Existing reinforcement learning models face challenges in explaining their decision-making processes due to their black box nature, leading to inaccurate and inflexible interpretations of behavior, particularly in complex environments where access to model parameters or environment interactions is limited.
Innovation Solution
A trajectory-based explainability framework that encodes and clusters trajectories from offline training data to attribute policy decisions, allowing for the identification of influential trajectories by comparing the results of a reinforcement learning agent with a test agent trained on modified data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning models are used to make decisions based on learned policies, then optimization results are improved, but the black box nature makes it difficult to understand which factors influence the model
Solution Approach 1:
The patent introduces trajectory attributions as an intermediary mechanism that bridges the black box RL model and human interpreters. By computing and presenting trajectory attributions that explain which historical trajectories influenced the model's decision, the system makes the internal decision-making process transparent without changing the underlying RL optimization capability.
2Loss of information
If existing explainability methods are used, then some interpretability is provided, but the methods are inaccurate and inflexible, particularly in complex environments
Solution Approach 1:
The patent changes the parameters of existing explainability methods by operating in the trajectory embedding space rather than using traditional feature-level explanations. By computing attributions based on trajectory embeddings and comparing them with actual model behavior, the system achieves more accurate and flexible attribution that adapts to complex environments.
3Reliability
If access to model parameters or environment interactions is required for validation, then explainability can be verified, but access is limited in many applications
Solution Approach 1:
The system performs self-validation by using the RL model's own predictions and the historical trajectory data to compute and verify attributions. The trajectory attribution framework validates explainability by comparing attributed trajectories with actual model behavior without requiring external access to model parameters or environment interactions, making the system self-sufficient.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media that provide a trajectory-based explainability framework for reinforcement learning models. For example, the disclosed systems generate trajectory clusters from trajectories utilized to train a reinforcement learning agent. In some embodiments, the disclosed system generates a complementary target data set by removing a target trajectory cluster from the trajectory clusters. In some cases, the disclosed system trains a test reinforcement learning agent utilizing the complementary target data set and generates a cluster attribution by comparing the result of the test reinforcement learning agent with the result of the reinforcement learning agent.