Stochastic Decision Horizons for Constrained Reinforcement Learning

• URL: http://arxiv.org/abs/2602.04599v1
• Date: Wed, 04 Feb 2026 14:27:16 GMT
• Title: Stochastic Decision Horizons for Constrained Reinforcement Learning
• Authors: Nikola Milosevic, Leonard Franz, Daniel Haeufle, Georg Martius, Nico Scherf, Pavel Kolev,
• Abstract summary: Constrained Markov decision processes (CMDPs) provide a principled model for handling constraints, such as safety and other auxiliary objectives, in reinforcement learning.<n>We propose Control as Inference formulation based on state-action-dependent decision horizons, where constraint violations attenuate reward contributions and shorten the effective planning horizon via state-action-dependent continuation.<n>We propose two violation semantics, absorbing and virtual termination, that share the same survival-weighted return but result in distinct structures that lead to SAC/MPO-style policy improvement.
• Score: 22.755234154139174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Constrained Markov decision processes (CMDPs) provide a principled model for handling constraints, such as safety and other auxiliary objectives, in reinforcement learning. The common approach of using additive-cost constraints and dual variables often hinders off-policy scalability. We propose a Control as Inference formulation based on stochastic decision horizons, where constraint violations attenuate reward contributions and shorten the effective planning horizon via state-action-dependent continuation. This yields survival-weighted objectives that remain replay-compatible for off-policy actor-critic learning. We propose two violation semantics, absorbing and virtual termination, that share the same survival-weighted return but result in distinct optimization structures that lead to SAC/MPO-style policy improvement. Experiments demonstrate improved sample efficiency and favorable return-violation trade-offs on standard benchmarks. Moreover, MPO with virtual termination (VT-MPO) scales effectively to our high-dimensional musculoskeletal Hyfydy setup.

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