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Found 6 papers, 4 papers with code
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Experts
Reinforcement learning (RL) is a powerful approach for acquiring a good-performing policy.
MP3: Movement Primitive-Based (Re-)Planning Policy
We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3).
Curriculum-Based Imitation of Versatile Skills
Learning skills by imitation is a promising concept for the intuitive teaching of robots.
Information Maximizing Curriculum: A Curriculum-Based Approach for Imitating Diverse Skills
Imitation learning uses data for training policies to solve complex tasks.
Deep Black-Box Reinforcement Learning with Movement Primitives
In this paper, we present a new algorithm for deep ERL.
Specializing Versatile Skill Libraries using Local Mixture of Experts
This local and incremental learning results in a modular MoE model of high accuracy and versatility, where both properties can be scaled by adding more components on the fly.
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