Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

Shihmin WANG; Binqi ZHAO; Zhengfeng ZHANG; Junping ZHANG; Jian PU

doi:10.1631/FITEE.2300084

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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

Regular Papers | Updated：2023-06-26

- Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning
- 基于专家示教聚类经验池的高效深度强化学习
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 11, Pages: 1541-1556(2023)
- Affiliations：
  
  1.Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai 200433, China
  2.Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China
- Author bio：
  
  †E-mail: wangshimin20@fudan.edu.cn
  bqzhao20@fudan.edu.cn
  jpzhang@fudan.edu.cn
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2300084
  CLC： TP181
- Published：0 November 2023，
  
  Received：12 February 2023，
  
  Accepted：2023-05-19
- Accepted：
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SHIHMIN WANG, BINQI ZHAO, ZHENGFENG ZHANG, et al. Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning. [J]. Frontiers of information technology & electronic engineering, 2023, 24(11): 1541-1556.
DOI：

SHIHMIN WANG, BINQI ZHAO, ZHENGFENG ZHANG, et al. Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning. [J]. Frontiers of information technology & electronic engineering, 2023, 24(11): 1541-1556. DOI： 10.1631/FITEE.2300084.

摘要

作为强化学习领域最基本的主题之一，样本效率对于深度强化学习算法的部署至关重要。与现有大多数从不同类型的后验分布中对动作进行采样的探索方法不同，我们专注于策略的采样过程，提出一种有效的选择性采样方法，通过对环境的内部层次结构建模来提高样本效率。具体来说，首先在策略采样过程中使用聚类方法生成动作候选集，随后引入一个用于对内部层次结构建模的聚类缓冲区，它由同轨数据、异轨数据以及专家数据组成，用于评估探索阶段动作候选集中不同类别动作的价值。通过这种方式，我们的方法能够更多地利用专家示教数据中的监督信息。在6种不同的连续运动环境中进行了实验，结果表明选择性采样方法具有卓越的强化学习性能和更快的收敛速度。特别地，在LGSVL任务中，该方法可以减少46.7%的收敛步数和28.5%的收敛时间。代码已开源，见https://github.com/Shihwin/SelectiveSampling。

Abstract

As one of the most fundamental topics in reinforcement learning (RL)

sample efficiency is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions

we focus on the policy sampling process and propose an efficient selective sampling approach to improve sample efficiency by modeling the internal hierarchy of the environment. Specifically

we first employ clustering methods in the policy sampling process to generate an action candidate set. Then we introduce a clustering buffer for modeling the internal hierarchy

which consists of on-policy data

off-policy data

and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage. In this way

our approach is able to take advantage of the supervision information in the expert demonstration data. Experiments on six different continuous locomotion environments demonstrate superior reinforcement learning performance and faster convergence of selective sampling. In particular

on the LGSVL task

our method can reduce the number of convergence steps by 46.7% and the convergence time by 28.5%. Furthermore

our code is open-source for reproducibility. The code is available at https://github.com/Shihwin/SelectiveSampling.

关键词

强化学习采样效率采样过程聚类方法自动驾驶

Keywords

Reinforcement learningSample efficiencySampling processClustering methodsAutonomous driving

references

Andrychowicz M, Wolski F, Ray A, et al., 2017. Hindsight experience replay. Proc 31st Int Conf on Neural Information Processing Systems, p.5055-5065. https://doi.org/10.5555/3295222.3295258https://doi.org/10.5555/3295222.3295258

Bellemare MG, Srinivasan S, Ostrovski G, et al., 2016. Unifying count-based exploration and intrinsic motivation. Proc 30th Int Conf on Neural Information Processing Systems, p.1479-1487. https://doi.org/10.5555/3157096.3157262https://doi.org/10.5555/3157096.3157262

Brockman G, Cheung V, Pettersson L, et al., 2016. OpenAI Gym. https://arxiv.org/abs/1606.01540https://arxiv.org/abs/1606.01540

Cheung WC, Simchi-Levi D, Zhu RH, 2020. Reinforcement learning for non-stationary Markov decision processes: the blessing of (more) optimism. Proc 37th Int Conf on Machine Learning, Article 172. https://doi.org/10.5555/3524938.3525110https://doi.org/10.5555/3524938.3525110

Dai XY, Zhao C, Wang X, et al., 2022. Image-based traffic signal control via world models. Front Inform Technol Electron Eng, 23(12):1795-1813. https://doi.org/10.1631/FITEE.2200323https://doi.org/10.1631/FITEE.2200323

Fu J, Luo K, Levine S, 2017. Learning robust rewards with adversarial inverse reinforcement learning. https://arxiv.org/abs/1710.11248https://arxiv.org/abs/1710.11248

Goodfellow I, Pouget-Abadie J, Mirza M, et al., 2020. Generative adversarial networks. Commun ACM, 63(11):139-144. https://doi.org/10.1145/3422622https://doi.org/10.1145/3422622

Haarnoja T, Zhou A, Abbeel P, et al., 2018. Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. Proc 35th Int Conf on Machine Learning, p.1861-1870.

Hester T, Vecerik M, Pietquin O, et al., 2018. Deep Q-learning from demonstrations. Proc AAAI Conf on Artificial Intelligence, p.394. https://doi.org/10.1609/aaai.v32i1.11757https://doi.org/10.1609/aaai.v32i1.11757

Ho J, Ermon S, 2016. Generative adversarial imitation learning. Proc 30th Int Conf on Neural Information Processing Systems, p.4572-4580. https://doi.org/10.5555/3157382.3157608https://doi.org/10.5555/3157382.3157608

Houthooft R, Chen X, Duan Y, et al., 2016. VIME: variational information maximizing exploration. Proc 30th Int Conf on Neural Information Processing Systems, p.1117-1125. https://doi.org/10.5555/3157096.3157221https://doi.org/10.5555/3157096.3157221

Huang ZZ, Chen J, Zhang JP, et al., 2023. Learning representation for clustering via prototype scattering and positive sampling. IEEE Trans Patt Anal Mach Intell, 45(6):7509-7524. https://doi.org/10.1109/TPAMI.2022.3216454https://doi.org/10.1109/TPAMI.2022.3216454

Kingma DP, Welling M, 2014. Auto-encoding variational Bayes. Proc 2nd Int Conf on Learning Representations.

Li HQ, Huang J, Cao Z, et al., 2023. Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning. Front Inform Technol Electron Eng, 24(1):131-140. https://doi.org/10.1631/FITEE.2200128https://doi.org/10.1631/FITEE.2200128

Liu SP, Tian GH, Cui YC, et al., 2022. A deep Q-learning network based active object detection model with a novel training algorithm for service robots. Front Inform Technol Electron Eng, 23(11):1673-1683. https://doi.org/10.1631/FITEE.2200109https://doi.org/10.1631/FITEE.2200109

Moerland TM, Broekens J, Plaat A, et al., 2023. Model-based reinforcement learning: a survey. Found Trends Mach Learn, 16(1):1-118. https://doi.org/10.1561/2200000086https://doi.org/10.1561/2200000086

Murtagh F, Legendre P, 2014. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion. J Classif, 31(3):274-295. https://doi.org/10.1007/s00357-014-9161-zhttps://doi.org/10.1007/s00357-014-9161-z

Nair A, McGrew B, Andrychowicz M, et al., 2018. Overcoming exploration in reinforcement learning with demonstrations. Proc IEEE Int Conf on Robotics and Automation, p.6292-6299. https://doi.org/10.1109/ICRA.2018.8463162https://doi.org/10.1109/ICRA.2018.8463162

Niu C, Zhang J, Wang G, et al., 2020. GATCluster: self-supervised Gaussian-attention network for image clustering. Proc 16th European Conf on Computer Vision, p.735-751. https://doi.org/10.1007/978-3-030-58595-2_44https://doi.org/10.1007/978-3-030-58595-2_44

Niu C, Shan HM, Wang G, 2022. SPICE: semantic pseudo-labeling for image clustering. IEEE Trans Image Process, 31:7264-7278. https://doi.org/10.1109/TIP.2022.3221290https://doi.org/10.1109/TIP.2022.3221290

Ravichandar H, Polydoros AS, Chernova S, et al., 2020. Recent advances in robot learning from demonstration. Ann Rev Contr Robot Auton Syst, 3:297-330. https://doi.org/10.1146/annurev-control-100819-063206https://doi.org/10.1146/annurev-control-100819-063206

Rong GD, Shin BH, Tabatabaee H, et al., 2020. LGSVL Simulator: a high fidelity simulator for autonomous driving. Proc 23rd Int Conf on Intelligent Transportation Systems, p.1-6. https://doi.org/10.1109/ITSC45102.2020.9294422https://doi.org/10.1109/ITSC45102.2020.9294422

Schaul T, Quan J, Antonoglou I, et al., 2016. Prioritized experience replay. Proc 4th Int Conf on Learning Representations.

Schulman J, Levine S, Moritz P, et al., 2015. Trust region policy optimization. Proc 32nd Int Conf on Machine Learning, p.1889-1897. https://doi.org/10.5555/3045118.3045319https://doi.org/10.5555/3045118.3045319

Schulman J, Moritz P, Levine S, et al., 2016. High-dimensional continuous control using generalized advantage estimation. Proc 4th Int Conf on Learning Representations.

Schulman J, Wolski F, Dhariwal P, et al., 2017. Proximal policy optimization algorithms. https://arxiv.org/abs/1707.06347https://arxiv.org/abs/1707.06347

Silver D, Huang A, Maddison CJ, et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587):484-489. https://doi.org/10.1038/nature16961https://doi.org/10.1038/nature16961

Sun PQ, Zhou WG, Li HQ, 2020. Attentive experience replay. Proc AAAI Conf on Artificial Intelligence, p.5900-5907. https://doi.org/10.1609/aaai.v34i04.6049https://doi.org/10.1609/aaai.v34i04.6049

Sutton RS, Barto AG, 1998. Reinforcement Learning: an Introduction. MIT Press, Cambridge, UK.

Vecerik M, Hester T, Scholz J, et al., 2017. Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards. https://arxiv.org/abs/1707.08817https://arxiv.org/abs/1707.08817

Vinyals O, Babuschkin I, Czarnecki WM, et al., 2019. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782):350-354. https://doi.org/10.1038/s41586-019-1724-zhttps://doi.org/10.1038/s41586-019-1724-z

Wang SC, Li B, 2020. Implicit posterior sampling reinforcement learning for continuous control. Proc 27th Int Conf on Neural Information Processing, p.452-460. https://doi.org/10.1007/978-3-030-63833-7_38https://doi.org/10.1007/978-3-030-63833-7_38

Xie JY, Girshick R, Farhadi A, 2016. Unsupervised deep embedding for clustering analysis. Proc 33rd Int Conf on Machine Learning, p.478-487. https://doi.org/10.5555/3045390.3045442https://doi.org/10.5555/3045390.3045442

Xue JR, Hu B, Li LX, et al., 2022. Human-machine augmented intelligence: research and applications. Front Inform Technol Electron Eng, 23(8):1139-1141. https://doi.org/10.1631/FITEE.2250000https://doi.org/10.1631/FITEE.2250000

Ye DH, Chen GB, Zhang W, et al., 2020. Towards playing full MOBA games with deep reinforcement learning. Proc 34th Int Conf on Neural Information Processing Systems, Article 53.

Zhang JP, Pu J, Xue JR, et al., 2023a. HiVeGPT: human-machine-augmented intelligent vehicles with generative pre-trained transformer. IEEE Trans Intell Veh, 8(3):2027-2033. https://doi.org/10.1109/TIV.2023.3256982https://doi.org/10.1109/TIV.2023.3256982

Zhang JP, Pu J, Chen J, et al., 2023b. DSiV: data science for intelligent vehicles. IEEE Trans Intell Veh, 8(4):2628-2634. https://doi.org/10.1109/TIV.2023.3264601https://doi.org/10.1109/TIV.2023.3264601

Zhou J, Ke P, Qiu XP, et al., 2023. ChatGPT: potential, prospects, and limitations. Front Inform Technol Electron Eng, early access. https://doi.org/10.1631/FITEE.2300089https://doi.org/10.1631/FITEE.2300089

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