31 citations · 151 across the 20 of their papers we have counts for
4 papers · 1 filter
Improving generalization in reinforcement learning through forked agents
Olivier Moulin, Vincent Francois-Lavet, Mark Hoogendoorn
An eco-system of agents each having their own policy with some, but limited, generalizability has proven to be a reliable approach to increase generalization across procedurally ge…
Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents
Olivier Moulin, Vincent Francois-Lavet, Paul Elbers +1
Adapting a Reinforcement Learning (RL) agent to an unseen environment is a difficult task due to typical over-fitting on the training environment. RL agents are often capable of so…
pH-RL: A personalization architecture to bring reinforcement learning to health practice
Ali el Hassouni, Mark Hoogendoorn, Marketa Ciharova +5
While reinforcement learning (RL) has proven to be the approach of choice for tackling many complex problems, it remains challenging to develop and deploy RL agents in real-life sc…
A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions
Ali el Hassouni, Mark Hoogendoorn, Martijn van Otterlo +3
Personalization is very powerful in improving the effectiveness of health interventions. Reinforcement learning (RL) algorithms are suitable for learning these tailored interventio…