Cited by 712Reinforcement Learning in RoboticsRobot Manipulation and LearningDomain Adaptation and Few-Shot Learning

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Overcoming Exploration in Reinforcement Learning with Demonstrations

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Nair, Ashvin, McGrew, Bob, Andrychowicz, Marcin, Zaremba, Wojciech, Abbeel, Pieter·2018·2018 IEEE International Conference on Robotics and Automation (ICRA)

DOI 10.1109/icra.2018.8463162

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Abstract

Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL). Many tasks are natural to specify with a sparse reward, and manually shaping a reward function can result in suboptimal performance. However, finding a non-zero reward is exponentially more difficult with increasing task horizon or action dimensionality. This puts many real-world tasks out of practical reach of RL methods. In this work, we use demonstrations to overcome the exploration problem and successfully learn to perform long-horizon, multi-step robotics tasks with continuous control such as stacking blocks with a robot arm. Our method, which builds on top of Deep Deterministic Policy Gradients and Hindsight Experience Replay, provides an order of magnitude of speedup over RL on simulated robotics tasks. It is simple to implement and makes only the additional assumption that we can collect a small set of demonstrations. Furthermore, our method is able to solve tasks not solvable by either RL or behavior cloning alone, and often ends up outperforming the demonstrator policy.

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Authors: Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., & Abbeel, P.
Affiliations: OpenAI (United States) (US)
ORCID: Abbeel, Pieter
Venue: 2018 IEEE International Conference on Robotics and Automation (ICRA)
Publisher: IEEE
Published: 2018-05
Pages: 6292-6299
ISSN: 1050-4729 (ISSN-L)
DOI: 10.1109/icra.2018.8463162
OpenAlex: W2756826236
Cited by: 712
References: 44
Citations by year: 2026: 24 · 2025: 72 · 2024: 86 · 2023: 111 · 2022: 93 · 2021: 117 · 2020: 105 · 2019: 67 · 2018: 35
FWCI: 33.08
Type: conference paper
Language: en
Open access: green
arxiv.org
Locations: Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: exploration in reinforcement learning, sparse rewards, demonstrations, deep deterministic policy gradient, hindsight experience replay, long-horizon tasks, continuous control, robotic manipulation, block stacking, robot manipulators, behavior cloning

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Overcoming Exploration in Reinforcement Learning with Demonstrations

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