MaxRL Meets Explainability: Interpreting Likelihood-Optimized Policies in Reasoning Tasks

TL;DR: It’s often a mystery why RL agents make certain decisions—let’s pair MaxRL with fuzzy rule-based explanation methods to demystify policy choices, especially in reasoning-heavy domains. Build a MaxRL+FuzRED agent that generates human-friendly explanations for its actions in navigation and LLM reasoning tasks.

Research Question: Can integrating explainable AI tools (like FuzRED) with MaxRL-trained agents provide interpretable, actionable insights into policy decisions, and does this help users trust or debug RL in complex reasoning domains?

Hypothesis: Combining MaxRL with fuzzy rule-based explanations will yield policies whose decisions can be interpreted and verified by humans, without sacrificing performance on reasoning or navigation tasks.

Experiment Plan: - Train MaxRL agents on navigation and reasoning tasks.

• Apply FuzRED to extract fuzzy association rules from action-state trajectories.
• Evaluate interpretability (human studies), explanation fidelity, and any impact on policy performance.

References:

• Tajwar, F., et al. (2026). Maximum Likelihood Reinforcement Learning.
• Buczak, A., Baugher, B. D., & Zaback, K. (2025). Fuzzy Rules for Explaining Deep Neural Network Decisions (FuzRED). Electronics.