3 citations · 10 across the 9 of their papers we have counts for
5 papers · 1 filter
Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem
Hélène Plisnier, Denis Steckelmacher, Jeroen Willems +2
Many instances of similar or almost-identical industrial machines or tools are often deployed at once, or in quick succession. For instance, a particular model of air compressor ma…
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning
Youri Coppens, Denis Steckelmacher, Catholijn M. Jonker +1
Today's advanced Reinforcement Learning algorithms produce black-box policies, that are often difficult to interpret and trust for a person. We introduce a policy distilling algori…
Transfer Learning Across Simulated Robots With Different Sensors
Hélène Plisnier, Denis Steckelmacher, Diederik Roijers +1
For a robot to learn a good policy, it often requires expensive equipment (such as sophisticated sensors) and a prepared training environment conducive to learning. However, it is…
The Actor-Advisor: Policy Gradient With Off-Policy Advice
Hélène Plisnier, Denis Steckelmacher, Diederik M. Roijers +1
Actor-critic algorithms learn an explicit policy (actor), and an accompanying value function (critic). The actor performs actions in the environment, while the critic evaluates the…
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan +3
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability. While hierarchy and partial observability are us…