222 citations · 242 across the 6 of their papers we have counts for
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cs.LG2022
Risk-Sensitive Policy with Distributional Reinforcement Learning
Thibaut Théate, Damien Ernst
Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheles…
cs.LG2021★ 6 cited
Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks
Thibaut Théate, Antoine Wehenkel, Adrien Bolland +2
The distributional reinforcement learning (RL) approach advocates for representing the complete probability distribution of the random return instead of only modelling its expectat…