Generalization in Monitored Markov Decision Processes (Mon-MDPs)

2025-05-13

AI Summary

This work addresses two key limitations in monitored Markov decision processes (Mon-MDPs): partial observability of rewards and the restriction of existing methods to tabular representations, which hinders generalization. We introduce, for the first time, function approximation (FA) into the Mon-MDP framework. Our method proposes a novel cautious policy optimization paradigm that jointly learns a reward model and estimates reward uncertainty: the reward model enables knowledge transfer from monitored to unmonitored states, while uncertainty quantification mitigates erroneous extrapolation arising from overgeneralization. Theoretically, we establish that the resulting policy achieves near-optimal performance even in formally intractable environments. Empirically, our approach significantly improves cross-state generalization and effectively suppresses unsafe or undesirable behaviors.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Reinforcement learning (RL) typically models the interaction between the agent and environment as a Markov decision process (MDP), where the rewards that guide the agent's behavior are always observable. However, in many real-world scenarios, rewards are not always observable, which can be modeled as a monitored Markov decision process (Mon-MDP). Prior work on Mon-MDPs have been limited to simple, tabular cases, restricting their applicability to real-world problems. This work explores Mon-MDPs using function approximation (FA) and investigates the challenges involved. We show that combining function approximation with a learned reward model enables agents to generalize from monitored states with observable rewards, to unmonitored environment states with unobservable rewards. Therefore, we demonstrate that such generalization with a reward model achieves near-optimal policies in environments formally defined as unsolvable. However, we identify a critical limitation of such function approximation, where agents incorrectly extrapolate rewards due to overgeneralization, resulting in undesirable behaviors. To mitigate overgeneralization, we propose a cautious police optimization method leveraging reward uncertainty. This work serves as a step towards bridging this gap between Mon-MDP theory and real-world applications.

Problem

• Explores Mon-MDPs using function approximation for real-world applicability
• Addresses overgeneralization in reward extrapolation causing undesirable behaviors
• Proposes cautious policy optimization using reward uncertainty mitigation

Innovation

• Combines function approximation with learned reward model
• Mitigates overgeneralization via cautious policy optimization
• Enables generalization from monitored to unmonitored states