Dynamic Context Scheduling: Learning Beyond the Static Universe

• URL: http://arxiv.org/abs/2608.20799v1
• Date: Fri, 21 Aug 2026 07:17:19 GMT
• Title: Dynamic Context Scheduling: Learning Beyond the Static Universe
• Abstract summary: We study dynamic context scheduling as a training instrument for contextual re- inforcement learning.<n>We introduce DYNAMICCARLENV, a framework that wraps contextual environments with pluggable schedule families.<n>Preliminary findings indicate that automatic search for multi-stage curricula can successfully discover schedules that improve generalization.
• Score: 3.5178680418107464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study dynamic context scheduling as a training instrument for contextual re- inforcement learning. Rather than treating intra-episode context variation as a deployment reality, we treat it as a controlled shaping mechanism. Thereby, context evolves within each training episode according to a predetermined schedule, expos- ing the policy to a richer and more temporally structured region of the environment parameter space. We introduce DYNAMICCARLENV, a framework that wraps contextual environments with pluggable schedule families, such as sinusoidal off- sets or cosine annealing. Across CartPole, BipedalWalker and VehicleRacing with CARL contextualization, we show that dynamic schedules match or outperform static context baselines in the out-of-distribution (OOD) regimes. Interestingly, for the more complex BipedalWalker and VehicleRacing environments we also achieve higher in-distribution (ID) evaluation performance. Preliminary findings indicate that automatic search for multi-stage curricula can successfully discover schedules that improve generalization, performing comparably to extensive grid search over single-stage schedulers.

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