基于Transformer的状态−动作−奖赏预测表征学习

刘民颂; 朱圆恒; 赵冬斌

doi:10.16383/j.aas.c240230

基于Transformer的状态−动作−奖赏预测表征学习

doi: 10.16383/j.aas.c240230 cstr: 32138.14.j.aas.c240230

刘民颂^{1, 2,},
朱圆恒^{1, 2,},
赵冬斌^{1, 2,}

1.
中国科学院自动化研究所多模态人工智能系统全国重点实验室北京 100190
2.
中国科学院大学人工智能学院北京 100049

基金项目: 中国科学院战略性先导研究(XDA27030400), 国家自然科学基金(62136008, 62293541), 北京市自然科学基金(4232056)资助

详细信息

作者简介:
刘民颂：中国科学院自动化研究所博士研究生. 2018年获得北京科技大学学士学位. 主要研究方向为深度强化学习和对比学习. E-mail: liuminsong2018@ia.ac.cn

朱圆恒：中国科学院自动化研究所副研究员. 2010年获得南京大学自动化专业学士学位. 2015年获得中国科学院自动化研究所控制理论和控制工程专业博士学位. 主要研究方向为深度强化学习, 博弈理论, 博弈智能和多智能体学习. 本文通信作者. E-mail: yuanheng.zhu@ia.ac.cn

赵冬斌：中国科学院自动化研究所研究员, 中国科学院大学教授. 分别于1994年、1996年和2000年获得哈尔滨工业大学学士学位、硕士学位和博士学位. 主要研究方向为深度强化学习, 计算智能, 自动驾驶, 游戏人工智能, 机器人. E-mail: dongbin.zhao@ia.ac.cn

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出版历程
- 收稿日期: 2024-04-30
- 录用日期: 2024-09-25
- 网络出版日期: 2024-12-12

State-Action-Reward Prediction Representation Learning Based on Transformer

LIU Min-Song^{1, 2
,},
ZHU Yuan-Heng^{1, 2
,},
ZHAO Dong-Bin^{1, 2
,}

1.
State Key Laboratory of Multi-modal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
2.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049

Funds: Supported by Strategic Priority Research Program of Chinese Academy of Sciences (XDA27030400), National Natural Science Foundation of China (62136008, 62293541), and Beijing Natural Science Foundation (4232056)

More Information

Author Bio:
LIU Min-Song　Ph.D. candidate of the Institute of Automation, Chinese Academy of Science. He received the bachelor degree from University of Science and Technology Beijing, in 2018. His research interest covers deep reinforcement learning and contrastive learning

ZHU Yuan-Heng　Associate professor at the Institute of Automation, Chinese Academy of Sciences. He received his bachelor degree in automation from Nanjing University in 2010, and his Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences in 2015. His research interest covers deep reinforcement learning, game theory, game intelligence, and multiagent learning. Corresponding author of this paper

ZHAO Dong-Bin　Professor at the Institute of Automation, Chinese Academy of Sciences and the University of Chinese Academy of Sciences. He received his bachelor, master, and Ph.D. degrees from Harbin Institute of Technology in 1994, 1996, and 2000, respectively. His research interest covers deep reinforcement learning, computational intelligence, autonomous driving, game artificial intelligence, and robotics

摘要

摘要: 为了提升具有高维动作空间的复杂连续控制任务的性能和样本效率, 提出一种基于Transformer的状态−动作−奖赏预测表征学习框架(Transformer-based state-action-reward prediction representation learning framework, TSAR). 具体来说, TSAR提出一种基于Transformer的融合状态−动作−奖赏信息的序列预测任务. 该预测任务采用随机掩码技术对序列数据进行预处理, 通过最大化掩码序列的预测状态特征与实际目标状态特征间的互信息, 同时学习状态与动作表征. 为进一步强化状态和动作表征与强化学习(Reinforcement learning, RL)策略的相关性, TSAR引入动作预测学习和奖赏预测学习作为附加的学习约束以指导状态和动作表征学习. TSAR同时将状态表征和动作表征显式地纳入到强化学习策略的优化中, 显著提高了表征对策略学习的促进作用. 实验结果表明, 在DMControl的9个具有挑战性的困难环境中, TSAR的性能和样本效率超越了现有最先进的方法.
- 深度强化学习 /
- 表征学习 /
- 自监督对比学习 /
- Transformer
Abstract: To enhance the performance and sample efficiency of complex continuous control tasks with high-dimensional action spaces, this paper introduces a transformer-based state-action-reward prediction representation learning framework (TSAR). Specifically, TSAR proposes a sequence prediction task integrating state-action-reward information using the Transformer architecture. This prediction task employs random masking techniques for preprocessing sequence data and seeks to maximize the mutual information between predicted features of masked sequences and actual target state features, thus concurrently learning state representation and action representation. To further strengthen the relevance of state representation and action representation to reinforcement learning strategies, TSAR incorporates an inverse dynamics model and a reward prediction model as additional learning constraints to guide the learning of state and action representations. TSAR explicitly incorporates state representation and action representation into the optimization of reinforcement learning strategies, significantly enhancing the facilitative role of representations in policy learning. Experimental results demonstrate that, across nine challenging and difficult environments in DMControl, the performance and sample efficiency of TSAR exceed those of existing state-of-the-art methods.
- Deep reinforcement learning (DRL) /
- representation learning /
- self-supervised contrastive learning /
- transformer

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图 1 传统状态预测模型和TSAR的状态预测模型

Fig. 1 Traditional stale prediction model and TSAR stale prediction model

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图 2 TSAR学习框架

Fig. 2 The learning framework of TSAR

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图 3 状态预测学习框架

Fig. 3 The framework of state prediction learning

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图 4 动作预测学习和奖赏预测学习框架

Fig. 4 The framework of action prediction learning and reward prediction learning

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图 5 TSAR和对比算法的性能

Fig. 5 Performance of TSAR and comparison algorithms

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图 6 动作表征的可视化展示

Fig. 6 The visualization of action representation

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图 7 3种预测任务的消融实验

Fig. 7 Ablation study on three prediction tasks

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图 8 RL策略学习中动作表征的消融实验

Fig. 8 Ablation study of action representation in RL policy learning

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图 9 状态预测模型输入词符的消融实验

Fig. 9 Ablation study on the input tokens of the prediction model

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图 10 掩码比例的消融实验

Fig. 10 Ablation study on mask ratio

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图 11 序列长度的消融实验

Fig. 11 Ablation study on sequence length

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表 1 9个困难环境的基本信息

Table 1 The fundamental information of nine challenging environments

环境	动作空间维度	难易程度
Quadruped Walk	12	困难
Quadruped Run	12	困难
Reach Duplo	9	困难
Walker Run	6	困难
Cheetah Run	6	困难
Hopper Hop	4	困难
Finger Turn Hard	2	困难
Reacher Hard	2	困难
Acrobot Swingup	1	困难

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表 2 TSAR额外的超参数

Table 2 Additional hyperparameters for TSAR

超参数	含义	值
$ \lambda_1 $	状态预测损失权重	1
$ \lambda_2 $	动作预测损失权重	1
$ \lambda_3 $	奖赏预测损失权重	1
batch_size	训练批次大小	256
mask_ratio	掩码比例	50%
$ K $	序列长度	16
$ L $	注意力层数	2
$ n $	奖赏预测步长	2: Hopper Hop
		Reacher Hard
		1: 其他
$ \tau $	EMA衰减率	0.95

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表 3 TSAR和对比算法在100万步长时的得分

Table 3 Scores achieved by TSAR and comparison algorithms at 1 M time steps

环境	TSAR (本文)	TACO^[16]	DrQ-v2^[22]	CURL^[8]	Dreamer-v3^[44]	TDMPC^[27]
Quadruped Run	657±25	541±38	407±21	181±14	331±42	397±37
Hopper Hop	293±41	261±52	189±35	152±34	369±21	195±18
Walker Run	699±22	637±11	517±43	387±24	765±32	600±28
Quadruped Walk	837±23	793±8	680±52	123±11	353±27	435±16
Cheetah Run	835±32	821±48	691±42	657±35	728±32	565±61
Finger Turn Hard	636±24	632±75	220±21	215±17	810±58	400±113
Acrobot Swingup	318±19	241 ±21	128±8	5±1	210±12	224±20
Reacher Hard	937±18	883±63	572±51	400±29	499±51	485 ±31
Reach Duplo	247±11	234±21	206±32	8±1	119±30	117±12
平均性能	606.6	560.3	226.4	236.4	464.9	379.8
中位性能	657	632	179	181	369	400
注: 加粗字体表示在不同环境下各算法的最优结果。

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表 4 与不同表征学习目标的对比

Table 4 Comparison with other representation learning objectives

环境	TSAR (本文)	TACO^[16]	M-CURL^[11]	SPR^[10]	ATC^[39]	DrQ-v2^[22]
Quadruped Run	657±25	541±38	536±45	448 ±79	432±54	407±21
Hopper Hop	293±41	261±52	248±61	154±10	112±98	192±41
Walker Run	699±22	637±21	623±39	560±71	502±171	517±43
Quadruped Walk	837±23	793±8	767±29	701±25	718±27	680±52
Cheetah Run	835±32	821±48	794±61	725±49	710±51	691±42
Finger Turn Hard	636±24	632±75	624±102	573±88	526±95	220±21
Acrobot Swingup	318±19	241±21	234±22	198±21	206±61	210±12
Reacher Hard	937±18	883±63	865±72	711±92	863±12	572±51
Reach Duplo	247±11	234±21	229±34	217±25	219±27	206±32
平均性能	606.6	560.3	546.7	476.3	475.4	226.4
中位性能	657	632	623	560	502	179

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表 5 状态预测准确性对比

Table 5 Comparison of state prediction accuracy

环境	TSAR (本文)		TACO^[16]		M-CURL^[11]
环境	误差	性能	误差	性能	误差	性能
Quadruped Run	0.097	657±25	0.157	541±38	0.124	536±45
Walker Run	0.081	699±22	0.145	637±21	0.111	623±39
Hopper Hop	0.206	293±41	0.267	261±52	0.245	248±61
Reacher Hard	0.052	937±18	0.142	883±63	0.107	865±72
Acrobot Swingup	0.063	318±19	0.101	241±21	0.082	234±22

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