This work introduces SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-based framework for interpreting DRL policies, and demonstrates how its tree-based representation can be used to inspect policy preferences, compare alternative future trajectories, and reveal downstream behaviors that are not visible through single-timestep feature-attribution methods.
Abstract
Deep reinforcement learning (DRL) agents achieve strong performance in complex environments, yet their decision-making processes remain difficult to interpret. We introduce SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-based framework for interpreting DRL policies. Given access to the policy and an environment simulator, SPOT constructs an interpretable finite-horizon tree by sampling actions and recursively simulating the resulting successor states. The tree provides an empirical representation of the policy's action preferences and their possible downstream evolution. We provide formal guarantees establishing SPOT's asymptotic recovery of the policy's unique most probable action and characterizing its disagreement behavior under high-entropy policies. We demonstrate SPOT in the SUMO-RL traffic-signal control domain. The case study illustrates how its tree-based representation can be used to inspect policy preferences, compare alternative future trajectories, and reveal downstream behaviors that are not visible through single-timestep feature-attribution methods.
This work introduces ORCAID, a novel method for extracting interpretable rule-based policies from RL agents operating in mixed continuous-discrete environments with continuous action spaces, with an efficient oblique decision tree training algorithm that partitions the state space by hyperplanes and fits local linear models.
Ignacio D. Lopez-Miguel, E. Bartocci, Thomas Eiter et al.· 0 citations
SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills and distills their behavioral effect back into the policy model, is proposed.
Jinyang Wu, Shuo Yang, Zhengxi Lu et al.· arXiv.org· 7 citations
Large language models (LLMs) possess extensive latent knowledge yet remain largely static at inference. Once prompted, their generation policy typically cannot evolve, and post-hoc ''self-reflection'' methods provide no explicit principled learning signals. To address this limitation, we formally model iterative research idea optimization as a finite-horizon Markov Decision Process and propose InfRL (Inference-time Reinforcement Learning), a framework designed for effective policy improvement at inference without updating model weights. InfRL coordinates three specialized LLM agents: (i) a State Transition Agent that proposes candidate ideas; (ii) a Policy Update Agent that learns feedback strategies based on idea trajectories; and (iii) a Reward Agent that assigns normalized, comparative rewards, enabling nuanced reinforcement signals. This empowers the Policy Update Agent to dynamically refine feedback strategies, progressively leveraging latent knowledge encoded within the LLM. We evaluate InfRL on five balanced datasets covering 500 recent research papers from health, genetics, environment, neuroscience, and engineering domains. Compared to a single-pass GPT-4o baseline and a strong self-reflection baseline, InfRL improves the novelty of generated ideas by 3.93%--32.7% and the feasibility of generated ideas by 70.1%--448.6% with GPT-4o, exhibiting consistent improvements across ten inference-time iterations. Ablation studies underscore the critical roles of our reward formulation and modular agent design, while human evaluations confirm alignment between reward trends and perceived idea quality. InfRL thus offers a practical and domain-agnostic approach to harness reinforcement learning during inference, bridging the gap between static prompting and computationally intensive parameter-level fine-tuning. The code and the dataset we use are provided at: https://github.com/amir-hassan25/InfRL
Sikun Guo, Amir Hassan Shariatmadari, Jiuqi Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Comparative control theory analysis reveals a fundamental trade-off: transitioning from continuous to discrete rule-based control induces high-frequency Bang-Bang actuation and a stable bimodal limit cycle.
This work proposes AgentOPSD, a critic-free, recursive method for turn-level credit assignment in agentic reinforcement learning that aggregates token-level teacher-student log-probability gaps into turn-level evidence and recursively updates a Bayesian belief state in log-odds space.
Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao et al.· 1 citation
Evidence Anchors are constructed, which are concise, step-level evidence snippets extracted from the web, as privileged information that captures key reasoning steps without revealing the entire answer path, and SSPO, which converts teacher-student disagreement into step-level advantage weights within GRPO, applied exclusively to incorrect trajectories.
Haoze Wu, Chuqiao Kuang, Tianyi Zhuang et al.· 0 citations
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