Keywords: Hierarchical Reinforcement Learning, Reinforcement Learning, Skill Discovery, Deep Learning, Deep Reinforcement Learning
TL;DR: We present a new hierarchical reinforcement learning algorithm which can solve high-dimensional goal-oriented tasks more reliably than non-hierarchical agents and other state-of-the-art skill discovery techniques.
Abstract: Autonomously discovering temporally extended actions, or skills, is a longstanding goal of hierarchical reinforcement learning. We propose a new algorithm that combines skill chaining with deep neural networks to autonomously discover skills in high-dimensional, continuous domains. The resulting algorithm, deep skill chaining, constructs skills with the property that executing one enables the agent to execute another. We demonstrate that deep skill chaining significantly outperforms both non-hierarchical agents and other state-of-the-art skill discovery techniques in challenging continuous control tasks.
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