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Challenging Common Assumptions in Convex Reinforcement Learning. (arXiv:2202.01511v2 [cs.LG] UPDATED)
Nov. 17, 2022, 2:12 a.m. | Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, Marcello Restelli
cs.LG updates on arXiv.org arxiv.org
The classic Reinforcement Learning (RL) formulation concerns the maximization
of a scalar reward function. More recently, convex RL has been introduced to
extend the RL formulation to all the objectives that are convex functions of
the state distribution induced by a policy. Notably, convex RL covers several
relevant applications that do not fall into the scalar formulation, including
imitation learning, risk-averse RL, and pure exploration. In classic RL, it is
common to optimize an infinite trials objective, which accounts for …
More from arxiv.org / cs.LG updates on arXiv.org
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