Post-Hoc Robustness for Model-Based Reinforcement Learning

📰 ArXiv cs.AI

Learn to improve model-based reinforcement learning with post-hoc robustness against adversarial environment perturbations

advanced Published 3 Jun 2026

Action Steps

Implement a model-based reinforcement learning algorithm
Introduce an adversary to perturb the environment
Train the protagonist agent to optimize a policy under environmental perturbations
Evaluate the robustness of the learned policy using metrics such as expected reward and failure rate
Apply post-hoc robustness techniques to improve the model's resilience to adversarial attacks

Who Needs to Know This

Researchers and engineers working on reinforcement learning and robustness can benefit from this knowledge to develop more reliable and resilient models

Key Insight

💡 Post-hoc robustness can significantly improve the reliability and resilience of model-based reinforcement learning models in the presence of adversarial environment perturbations

Key Takeaways

Learn to improve model-based reinforcement learning with post-hoc robustness against adversarial environment perturbations

Full Article

Title: Post-Hoc Robustness for Model-Based Reinforcement Learning

Abstract:
arXiv:2606.03521v1 Announce Type: cross Abstract: To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversarial environment perturbations. In this setting, a protagonist agent optimizes a policy under environmental perturbations from an adversary, resulting in a zero-sum Markov game. When adversarially robust RL is combined with model-based RL, the adversary can target a learned transition model instead

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