Tzu-Han Hsu — accepted AI-conference papers

2026

HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation

Arshia Rafieioskouei, Tzu-Han Hsu, Matthew Lucas, Borzoo Bonakdarpour

ICML 2026poster

Formal specification is a powerful tool to guide the learning process and provides significant advantages over ad-hoc reward shaping: (1) mathematical rigor; (2) expressiveness to specify objectives and constraints, and (3) the ability to define strategies to achieve objectives. However, these benef…

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2025

HYPRL: Reinforcement Learning of Control Policies for Hyperproperties

Tzu-Han Hsu, Arshia Rafieioskouei, Borzoo Bonakdarpour

NeurIPS 2025poster

Reward shaping in multi-agent reinforcement learning (MARL) for complex tasks remains a significant challenge. Existing approaches often fail to find optimal solutions or cannot efficiently handle such tasks. We propose HYPRL, a specification-guided reinforcement learning framework that learns contr…

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