Author

André Biedenkapp

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Preprint Aug 2026

Dynamic Context Scheduling: Learning Beyond the Static Universe

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,...

M. Mráz, André Biedenkapp · 0 citations

#machine learning Preprint Sep 2026

Reinforcement Learning with Complex (valued) Memories

Partially observable environments pose a fundamental challenge in deep reinforcement learning, requiring agents to compress temporal information from observations and maintain a memory to make effective decisions. While there exist many approaches ranging from gated recurrence to attention mechanisms and model-based RL...

Sathya Kamesh Bhethanabhotla, Efstratios Gavves, André Biedenkapp · 0 citations