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cs.LG2024
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
Philipp Becker, Niklas Freymuth, Gerhard Neumann
Probabilistic State Space Models (SSMs) are essential for Reinforcement Learning (RL) from high-dimensional, partial information as they provide concise representations for control…
cs.LG2023
Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization
Philipp Dahlinger, Philipp Becker, Maximilian Hüttenrauch +1
Stochastic gradient-based optimization is crucial to optimize neural networks. While popular approaches heuristically adapt the step size and direction by rescaling gradients, a mo…