90 citations · 339 across the 37 of their papers we have counts for
Showing 2019 · cs.LGShow all
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cs.LG2019★ 7 cited
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action
Mathieu Seurin, Philippe Preux, Olivier Pietquin
The use of Reinforcement Learning (RL) is still restricted to simulation or to enhance human-operated systems through recommendations. Real-world environments (e.g. industrial robo…
cs.LG2019★ 7 cited
MERL: Multi-Head Reinforcement Learning
Yannis Flet-Berliac, Philippe Preux
A common challenge in reinforcement learning is how to convert the agent's interactions with an environment into fast and robust learning. For instance, earlier work makes use of d…
cs.LG2019★ 5 cited
Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL
Yannis Flet-Berliac, Philippe Preux
In reinforcement learning, policy gradient algorithms optimize the policy directly and rely on sampling efficiently an environment. Nevertheless, while most sampling procedures are…