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Safe Value Functions
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OA
AI
DOI：10.1109/TAC.2022.3200948.png)
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Abstract
En 中文
Safety constraints and optimality are important but sometimes conflicting criteria for controllers. Although these criteria are often solved separately with different tools to maintain formal guarantees, it is also common practice in reinforcement learning (RL) to simply modify reward functions by penalizing failures, with the penalty treated as a mere heuristic. We rigorously examine the relationship of both safety and optimality to penalties, and formalize sufficient conditions for safe value functions (SVFs): value functions that are both optimal for a given task, and enforce safety constraints. We reveal this structure by examining when rewards preserve viability under optimal control, and show that there always exists a finite penalty that induces an SVF. This penalty is not unique, but upper-unbounded: larger penalties do not harm optimality. Although it is often not possible to compute the minimum required penalty, we reveal clear structure of how the penalty, rewards, discount factor, and dynamics interact. This insight suggests practical, theory-guided heuristics to design reward functions for control problems where safety is important.
Keywords:
Safety
Task analysis
Trajectory
Dynamical systems
Reinforcement learning
Optimal control
Kernel
reinforcement learning (RL)
safety
strong duality
value functions
viability
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
Organization
Cited Papers
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