arXiv:2606.00840v1 Announce Type: new Abstract: This work presents a logic-driven framework to evaluate the performance of reinforcement learning (RL) algorithms in their ability to generalize to unseen tasks. Our framework defines a family of inductive reach-avoid tasks, characterized by structural similarities in task dynamics, enabling evaluation of generalization capabilities. We introduce a neural certificate function that validates trajectories generated by RL algorithms by enforcing key conditions, thereby serving as a litmus test for RL generalization. We empirically demonstrate our me
The rapid advancement and deployment of RL systems necessitate more robust methods for ensuring their reliability and generalization capabilities, pushing research towards formal verification.
Improving the generalization and trustworthiness of reinforcement learning is critical for its adoption in real-world, high-stakes applications, fostering greater confidence in AI systems.
This framework offers a new, logic-driven approach to evaluate and potentially enhance the reliability and generalization of RL algorithms beyond traditional empirical testing.
- · AI safety researchers
- · Developers of robust RL systems
- · Industries deploying autonomous AI
- · RL applications with unverified generalization claims
- · Developers relying solely on empirical validation
Increased trust and faster adoption of reinforcement learning in critical applications.
Development of standardized benchmarks and certification processes for RL system generalization.
Shift in AI development methodologies towards incorporating formal verification and certificate functions by default.
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Read at arXiv cs.AI