Deep Reinforcement Learning Policy Safety Verification
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Solution Overview
Problem
Deep reinforcement learning (DRL) agents face challenges in ensuring safety due to non-deterministic policies and unknown state-transition functions, limiting their applicability in fields like cybersecurity, self-driving cars, and finance, as existing safety guarantees are difficult to implement in real-world scenarios with unpredictable environments.
Innovation Solution
The method decomposes non-deterministic policies into deterministic policies and approximates unknown state-transition functions using deep neural networks transformed into polynomial forms, enabling verification with constraint solvers to ensure safety through runtime shielding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If non-deterministic policies are used in deep reinforcement learning to encourage exploration, then the agent's ability to discover optimal actions improves, but safety verification becomes difficult due to unpredictable agent behavior
Solution Approach 1:
The patent decomposes the non-deterministic policy into a set of deterministic policies, each representing a possible action the agent might take. This segmentation allows safety verification to be performed on each deterministic policy individually while still capturing the exploration behavior of the original non-deterministic policy. The constraint solver can then verify safety across all segmented policies without being overwhelmed by the complexity of the full non-deterministic policy.
2Adaptability or versatility
If the state-transition function is approximated using deep neural networks to handle unknown environments, then the system's adaptability to real-world scenarios improves, but the complexity of verification increases
Solution Approach 1:
The patent transforms the deep neural network approximation of the state-transition function into a polynomial representation. This parameter change from neural network weights to polynomial coefficients enables the constraint solver to work with the transition function in a form that is both accurate enough for real-world scenarios and tractable for formal verification. The polynomial representation maintains the approximation capability while providing a structured format that constraint solvers can process efficiently.
3Reliability
If safety verification is performed on deep reinforcement learning policies, then safety guarantees are provided, but the computational resources and time required for verification increase
Solution Approach 1:
The patent performs preliminary decomposition of the non-deterministic policy into deterministic policies and preliminary approximation of the state-transition function before actual safety verification. This preparation step organizes the verification problem into a form that can be solved more efficiently by the constraint solver. By breaking down the verification task into smaller, manageable components beforehand, the actual verification process requires less computational time and resources.
Data Source
AI summary
Safety verification for reinforcement learning can include receiving a policy generated by deep reinforced learning, where the policy is used in acting in an environment having a set of states. Responsive to determining that the policy is a non-deterministic policy, the non-deterministic policy can be decomposed into a set of deterministic policies. Responsive to determining that a state-transition function associated with the set of states is unknown, the state-transition function can be approximated at least by training a deep neural network and transforming the deep neural network into a polynomial. Using a constraint solver the policy with the state-transition function can be verified. Runtime shielding can be performed.