65 citations · 76 across the 5 of their papers we have counts for
4 papers · 1 filter
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management
Julien Siems, Maximilian Schambach, Sebastian Schulze +1
The COVID-19 pandemic has highlighted the importance of supply chains and the role of digital management to react to dynamic changes in the environment. In this work, we focus on d…
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl +4
Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions…
Bayesian Optimization for Iterative Learning
Vu Nguyen, Sebastian Schulze, Michael A Osborne
The performance of deep (reinforcement) learning systems crucially depends on the choice of hyperparameters. Their tuning is notoriously expensive, typically requiring an iterative…
Active Reinforcement Learning with Monte-Carlo Tree Search
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. This subtle change makes exploration substantially more cha…