Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management

no code implementations • 18 Mar 2023 • Julien Siems, Maximilian Schambach, Sebastian Schulze, Johannes S. Otterbach

In this work, we focus on developing dynamic inventory ordering policies for a multi-echelon, i. e. multi-stage, supply chain.

Additive models Management +2

Marginalising over Stationary Kernels with Bayesian Quadrature

no code implementations • 14 Jun 2021 • Saad Hamid, Sebastian Schulze, Michael A. Osborne, Stephen J. Roberts

Marginalising over families of Gaussian Process kernels produces flexible model classes with well-calibrated uncertainty estimates.

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

3 code implementations • ICLR 2020 • Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson

Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning.

Meta-Learning

Bayesian Optimization for Iterative Learning

1 code implementation • NeurIPS 2020 • Vu Nguyen, Sebastian Schulze, Michael A. Osborne

We demonstrate the efficiency of our algorithm by tuning hyperparameters for the training of deep reinforcement learning agents and convolutional neural networks.

Bayesian Optimization reinforcement-learning +1

Active Reinforcement Learning with Monte-Carlo Tree Search

no code implementations • 13 Mar 2018 • Sebastian Schulze, Owain Evans

Active Reinforcement Learning (ARL) is a twist on RL where the agent observes reward information only if it pays a cost.

reinforcement-learning Reinforcement Learning (RL) +1