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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00962 |
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| _version_ | 1866916897545519104 |
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| author | Borrell, Enric Ribera Richter, Lorenz Schütte, Christof |
| author_facet | Borrell, Enric Ribera Richter, Lorenz Schütte, Christof |
| contents | We extend the standard reinforcement learning framework to random time horizons. While the classical setting typically assumes finite and deterministic or infinite runtimes of trajectories, we argue that multiple real-world applications naturally exhibit random (potentially trajectory-dependent) stopping times. Since those stopping times typically depend on the policy, their randomness has an effect on policy gradient formulas, which we (mostly for the first time) derive rigorously in this work both for stochastic and deterministic policies. We present two complementary perspectives, trajectory or state-space based, and establish connections to optimal control theory. Our numerical experiments demonstrate that using the proposed formulas can significantly improve optimization convergence compared to traditional approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00962 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning with Random Time Horizons Borrell, Enric Ribera Richter, Lorenz Schütte, Christof Machine Learning Optimization and Control Probability We extend the standard reinforcement learning framework to random time horizons. While the classical setting typically assumes finite and deterministic or infinite runtimes of trajectories, we argue that multiple real-world applications naturally exhibit random (potentially trajectory-dependent) stopping times. Since those stopping times typically depend on the policy, their randomness has an effect on policy gradient formulas, which we (mostly for the first time) derive rigorously in this work both for stochastic and deterministic policies. We present two complementary perspectives, trajectory or state-space based, and establish connections to optimal control theory. Our numerical experiments demonstrate that using the proposed formulas can significantly improve optimization convergence compared to traditional approaches. |
| title | Reinforcement Learning with Random Time Horizons |
| topic | Machine Learning Optimization and Control Probability |
| url | https://arxiv.org/abs/2506.00962 |