Reward Machines for Signal Temporal Logic
arXiv:2605.01222v1 Announce Type: new Abstract: Signal Temporal Logic (STL) offers verifiable task specifications and is crucial for safety-critical control. Yet STL planning
arXiv:2605.08354v1 Announce Type: new Abstract: Aligning multimodal generative models with human preferences demands reward signals that respect the compositional,
arXiv:2605.12581v1 Announce Type: cross Abstract: Synthesising autonomous agents that can navigate uncertain environments while adhering to complex temporal constraints remains a
arXiv:2603.29868v1 Announce Type: new Abstract: The reliability of autonomous systems depends on their robustness, i.e., their ability to meet their objectives under uncertainty.
arXiv:2603.18859v1 Announce Type: new Abstract: Reinforcement learning (RL) holds significant promise for enhancing the agentic reasoning capabilities of large language models
arXiv:2604.05535v1 Announce Type: new Abstract: Traffic signal control TSC requires strategies that are both effective and interpretable for deployment, yet reinforcement learning
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