mlsanityData to 7 Oct 2026

Boosting Reinforcement Learning and Planning with Demonstrations: A Survey

arXiv 2303.13489 ↗First posted 23 Mar 2023cs.LG cs.AI cs.ROcited by 1

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Abstract (arXiv)

Although reinforcement learning has seen tremendous success recently, this kind of trial-and-error learning can be impractical or inefficient in complex environments. The use of demonstrations, on the other hand, enables agents to benefit from expert knowledge rather than having to discover the best action to take through exploration. In this survey, we discuss the advantages of using demonstrations in sequential decision making, various ways to apply demonstrations in learning-based decision making paradigms (for example, reinforcement learning and planning in the learned models), and how to collect the demonstrations in various scenarios. Additionally, we exemplify a practical pipeline for generating and utilizing demonstrations in the recently proposed ManiSkill robot learning benchmark.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 112 of its 151 references that are in the map. First posted on arXiv 23 Mar 2023.

Builds on

112 of 151 references.

References · 112

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  1. 2023Learning Multimodal Latent Dynamics for Human-Robot Interactioncited by 1