Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

This research introduces Robust Hallucinated Constrained Upper-Confidence Reinforcement Learning (RHC-UCRL), a novel model-based algorithm designed for safety-critical decision-making systems operating under adversarial conditions. Unlike traditional Constrained MDPs or robust RL methods that assume passive uncertainty, RHC-UCRL explicitly models exogenous factors as an adversarial policy $\bar{\pi}$ that co-determines state transitions, aiming to degrade agent performance and violate safety constraints. The algorithm maintains optimism over both agent and adversary policies, separating epistemic from aleatoric uncertainty, and employs a "rectified penalty" approach to handle the misalignment between reward and constraint adversaries, a challenge where standard primal-dual methods fail. RHC-UCRL is the first provably robust constrained RL algorithm to achieve sub-linear regret and constraint violation guarantees, demonstrating superior performance in maintaining safety constraints while maximizing reward on benchmark environments like CartPole-v1 and Pendulum-v1 compared to its unconstrained counterpart, RH-UCRL.

Key takeaway

For research scientists developing safety-critical autonomous systems, RHC-UCRL offers a robust framework to ensure constraint satisfaction against strategic adversaries. You should consider implementing its rectified penalty approach and optimistic/pessimistic policy evaluation to achieve provable sub-linear regret and violation guarantees, particularly in environments where external actors can actively undermine safety. This method addresses a critical gap in existing robust RL by explicitly modeling policy-dependent adversarial dynamics.

Key insights

RHC-UCRL enables safe, optimal reinforcement learning against explicit adversarial policies with theoretical guarantees.

Principles

• Explicitly model adversarial policies for robust safety.
• Separate epistemic and aleatoric uncertainty.
• Rectified penalties handle conflicting adversarial objectives.

Method

RHC-UCRL uses an ensemble of neural networks for model learning and an actor-critic framework with optimistic and pessimistic critics for policy learning, optimizing via min-max procedures.

In practice

• Apply RHC-UCRL in autonomous systems facing strategic adversaries.
• Use rectified penalties for robust constrained optimization.
• Evaluate performance on CartPole-v1 and Pendulum-v1.

Topics

• Robust Reinforcement Learning
• Constrained Markov Decision Processes
• Adversarial Dynamics
• RHC-UCRL Algorithm
• Sub-linear Regret

Code references

• Sourav1429/RHC_UCRL
• Sourav1429/RHC_UCRL

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.