Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

16 Oct 2019Rémy PortelasCédric ColasKatja HofmannPierre-Yves Oudeyer

We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments. To do so, we study how a teacher algorithm can learn to generate a learning curriculum, whereby it sequentially samples parameters controlling a stochastic procedural generation of environments... (read more)

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