3 papers
cs.LG2026
Reinforcement Learning with Complex (valued) Memories
Sathya Kamesh Bhethanabhotla, Efstratios Gavves, André Biedenkapp
Partially observable environments pose a fundamental challenge in deep reinforcement learning, requiring agents to compress temporal information from observations and maintain a me…
cs.AI2026
Dynamic Context Scheduling: Learning Beyond the Static Universe
Martin Mráz, André Biedenkapp
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,…
cs.LG2026
Contextual Intelligence The Next Leap for Reinforcement Learning
André Biedenkapp
Reinforcement learning (RL) has produced spectacular results in games, robotics, and continuous control. Yet, despite these successes, learned policies often fail to generalize bey…