Completion vs Optimality: Policy Gradient in Long-Horizon Cumulative-Damage Problems
Long-horizon decision problems with cumulative damage couple locally attractive actions to globally adverse outcomes. We identify two orthogonal failure modes for policy-gradient methods on this class and propose a decomposition that separates them: \emph{completion} (reaching the terminal horizon rather than exiting via an implicit terminal constraint) and \emph{optimality} (matching the dynamic-programming reference given completion). Under PPO with a linear soft penalty, granting horizon access alone reduces the completion rate: the penalty's equilibrium drives the dominant-activity share to zero, while action-space restriction combined with horizon access achieves completion but leave...
| Authors | Wolfgang Maass, Sabine Janzen |
|---|---|
| Primary category | cs.AI |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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