Research Progress of Opponent Modeling Based on Deep Reinforcement Learning

doi:10.16182/j.issn1004731x.joss.22-0555

Abstract

Abstract:

Deep reinforcement learning is an agent modeling method with both deep learning feature extraction ability and reinforcement learning sequence decision-making ability, which can make up for the depleted non-stationary adaptation, complex feature selection and insufficient state-space representation ability of traditional opponent modeling. The deep reinforcement learning-based opponent modeling methods are divided into two categories, explicit modeling and implicit modeling, and the corresponding theories, models, algorithms and applicable scenarios are sorted out according to the categories. The applications of deep reinforcement learning-based opponent modeling techniques on different fields are introduced. The key problems and future development are summarized to provide a comprehensive research review for the deep reinforcement learning-based opponent modeling methods.

Key words: deep reinforcement learning, opponent modeling, game theory, theory of mind, representation learning, meta learning

CLC Number:

TP391.9

Haotian Xu, Long Qin, Junjie Zeng, Yue Hu, Qi Zhang. Research Progress of Opponent Modeling Based on Deep Reinforcement Learning[J]. Journal of System Simulation, 2023, 35(4): 671-694.

Figures/Tables 9

Fig. 1

Table 1

Fig. 2

Table 2

Research motivation, solved problem and effects of game equilibrium strategy methods

分类	方法	研究动机	对手模型	模型效果
虚拟自博弈	FSP^[38]	将FP推广至扩展式博弈	对手的历史平均最佳响应	强化学习实现最优响应，监督学习实现平均策略，收敛至纳什均衡
	NFSP^[39]	使用神经网络近似最优策略和平均策略	多层神经网络近似的对手历史平均最佳响应	基于DQN实现端到端学习，并收敛至纳什均衡
	PSRO^[42]	求解子博弈元策略，合并成完整策略	将博弈对手的历史策略记录在元策略集	使用DO算法^[58]训练新策略，收敛性受到对手策略采样方式的影响
	$α$ -PSRO^[44]	训练改进群体的每种策略，而非单纯训练纳什均衡策略	马尔科夫-康尼链评价对手种群的质量	策略收敛于 $α$ -rank解^[43]，改进了群体博弈的均衡收敛性
反事实遗憾值最小化	MCCFR^[48]	采用蒙特卡罗抽样代替树节点遍历计算各个状态的遗憾值	包含对手所有可能行动的信息集	蒙特卡罗抽样是对遗憾值无偏估计，且在不完美信息扩展式博弈中快速收敛
反事实遗憾值最小化	CFR+^[51]	采用保证动作的遗憾值为正数，累计值不减少的遗憾值匹配方法	包含对手所有可能行动的信息集	改进遗憾值匹配机制，使CFR算法加速收敛近似纳什均衡
MiniMax 均衡	Level-0^[55]	有限理性的对手行动源自0级策略的递归推理，0级策略采用人工筛选策略	以MiniMax策略为0级的定量认知层次策略	0级策略改进认知层次模型的效果，数据集实验结果有效预测人类行为
MiniMax 均衡	M3DDPG^[56]	采用保底策略鲁棒应对变化对手的多智能体DRL算法	导致自身收益最小的对手策略	采用对抗学习方法求解连续动态环境的MiniMax均衡策略

Table 2

Fig. 3

Fig. 4

Table 3

Table 4

Summary of research motivation, innovation points and limitations of implicit based opponent modeling methods

类别	算法	研究动机	模型特点	创新点	局限性
辅助任务	DRON^[26]	设计挖掘不同对手策略隐藏特征的神经网络	使用MLP处理对手行动，将表征信息用于强化学习任务	提取对手特征用于DRL算法决策	手工提取输入专家网络的特征，可采用RNN改进
	DIPQN^[71]	从观测直接提取对手策略特征，训练对手建模的辅助任务	策略特征网络学习从观测提取表征，并通过行为克隆的准确性修正网络	设计了调节最大奖励与对手建模的自适应损失函数	采用经验回放池离线训练，学习的对手策略具有较大样本方差
	AMS-A3C^[72]	在强化学习过程中，制订估计其他智能体策略的辅助任务	决策网络与模仿决策的对手模型共享结构、参数，降低模型学习成本	提出参数共享、策略表征2套方案，将对手建模融合进A3C算法	对手模型参数敏感，难以应对复杂场景、具有学习能力的对手
学习表征	PPO-Emb^[74]	从交互样本中无监督地学习对手表征	提取同时具有策略提升效果和对手区分度的表征信息	无需领域知识，通用性强，适用大多数DRL算法	无法独立推断，用于辅助其他DRL算法决策
学习表征	RFM^[73]	采用图网络学习智能体的社会关系表征	通过边缘属性、节点等图结构信息预测对手行动、评估对手社会关系强度	量化智能体交互的社会属性，网络结构具有较好拓展性	存在复杂交互关系的图网络计算困难
概率推理	P-BIT^[76]	多智能体DRL的最优策略形式化为推理私有信息的概率下界	使用信念模块根据友方行为推理其私有信息	提出不完美信息条件下通过行动与队友传递私有信息的方法	适用于简单的二人合作场景
概率推理	ROMMEO^[78]	多智能体DRL形式化为基于对手模型的最优策略变分推理	预测对手行动，用于实现学习最优策略的推理任务	提出最大熵目标的正则化的对手建模方法	在线优化参数，训练时间长。默认对手目标已知，无法适应未知智能体
自我- 他人交互	SOM^[79]	基于自身策略推理对手可能的目标，用于支撑决策	建立拟合对手策略的神经网络，通过优化对手策略反向推断对手的目标	无需额外模型和参数显式建模，仿照自身模型推理任意数量规模对手	智能体与对手共享目标，并且奖励结构取决于目标
自我- 他人交互	LOLA^[81]	考虑具有学习能力的对手，解释对手学习参数的更新对自身策略影响	建模对手的价值函数，求其二阶导优化策略梯度	策略更新中增加了对手参数更新项，通过泰勒展开构造成高阶梯度项	默认对手使用可梯度优化的方法，并且无法察觉LOLA对其模型进行利用

Table 4

Table 5

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分类	算法	优点	不足
值函数近似	DQN^[6]	经验复用、离轨策略机制	无法用于高维、连续空间
值函数近似	DRQN^[33]	采用LSTM代替全连接层	完全可观测下表现不如DQN
策略梯度	DDPG^[34]	确定性策略、Actor-Critic框架	无法处理离散问题、难以确定更新步长
	A3C^[35]	多线程学习、异步更新参数	更新策略方差较大
	PPO^[37]	有裁剪的自适应超参数KL散度	对差异性较大样本敏感

类别	算法	研究动机	模型用途	创新点	局限性
心智理论	ToMnet^[60]	从心智理论提出符合人类认知的元学习对手模型	预测的对手行为、目标、信念	建立元学习的先验模型，用于预测表征和心智状态	适用的实验场景简单，环境完全可观
认知层次结构	PR2^[63]	智能体具有推断对手策略的信念递归推理能力	推理对手下一步意图	提出多智能体概率递归推理的分布式框架，利用变分贝叶斯推理对手策略	二人博弈场景收敛，复杂合作场景中表现不足
认知层次结构	GR2^[64]	借助不同层次结构的递归推理建模对手的有限理性	以K层深度推理对手的下一步意图	设计了基于概率图模型的层次结构，并证明存在完美贝叶斯均衡	具有递归推理层级选择问题，带来更高计算要求
贝叶斯策略复用	DPN-BPR+^[68]	针对非平稳的对手策略，提出策略检测和复用机制	根据收益更新对当前对手策略的信念	深度神经网络作为BPR+的值函数近似，使用网络蒸馏存储最优响应策略	假定对手在固定策略之间切换，无法识别连续演化的对手策略
贝叶斯策略复用	Deep Bayes ToMop^[69]	将BPR预测能力和心智理论的递归推理能力结合互补	在BPR信念基础上多层递归推理	具有学习对手演化和应对未知对手策略的能力	在线学习新策略的耗时长，无法应对多个对手

实验环境	博弈模型	文献	可观测信息	合作关系	行动顺序	状态动作
粒子世界	POMDP	[56-57,112-114]	部分可观	混合	同步	连续
德州扑克	EG	[38,40-42]	全局可观	竞争	序贯	离散
囚徒/硬币博弈	MG	[80]	全局可观	竞争	同步	离散
多智能体Mujoco	POMDP	[115]	部分可观	混合	同步	连续
网格世界	MG	[66-69]	全局可观	混合	同步	连续
迭代矩阵游戏	Team MG	[64,78]	全局可观	竞争	同步	离散
智力竞赛碗	EG	[26,71]	全局可观	竞争	序贯	离散
炸弹人	MG	[72]	全局可观	竞争	同步	离散
合作导航	Dec-POMDP	[63-64,116]	部分可观	合作	同步	离散
FightingICE	MG	[84,107]	全局可观	竞争	同步	连续
谷歌足球环境	POMDP	[87]	部分可观	混合	同步	连续

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