Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
Keywords: Reinforcement Learning, Diverse Skill Learning, Skill Discovery, Curriculum Reinforcement Learning, Mixture of Experts
TL;DR: We propose a novel Curriculum Reinforcement Learning method to acquire Diverse skills.
Abstract: Reinforcement learning (RL) is a powerful approach for acquiring a good-performing policy. However, learning diverse skills is challenging in RL due to the commonly used Gaussian policy parameterization. We propose \textbf{Di}verse \textbf{Skil}l \textbf{L}earning (Di-SkilL), an RL method for learning diverse skills using Mixture of Experts, where each expert formalizes a skill as a contextual motion primitive. Di-SkilL optimizes each expert and its associate context distribution to a maximum entropy objective that incentivizes learning diverse skills in similar contexts. The per-expert context distribution enables automatic curricula learning, allowing each expert to focus on its best-performing sub-region of the context space. To overcome hard discontinuities and multi-modalities without any prior knowledge of the environment's unknown context probability space, we leverage energy-based models to represent the per-expert context distributions and demonstrate how we can efficiently train them using the standard policy gradient objective. Di-SkilL can learn diverse and performant skills on challenging robot simulation tasks.
Submission Number: 42
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