论文发表在AAAI2022,通过将Stackelberg博弈的结构引入Actor-Critic框架,解决梯度循环问题,提升收敛速度。在若干经典的OpenAI gym环境中表现较好。 背景 Stackelberg博弈 Stackelberg博弈,又称为主从博弈。在双人一般和博弈场景下,存在一个Leader(L)与Follower(F),L先于F做决策,F会根据L的决策最大化自身收益,同样的L会预...
多智能体强化学习(Multiagnet reinforcement Learning, MARL)的目标在于,多个智能体共享一个环境,要求在学习过程中不仅从环境中获取observation,也要与其他智能体进行交互。一个简单的思路是独立学习(Independent reinforcement learning, InRL),即将其他智能体看做环境(non-stationary environment)的一部分,按照单智能体的方...
Deep learning is an advanced part of machine learning algorithms based on artificial neural networks and various learning methods, i.e., supervised, unsupervised, and reinforcement. There are well-known deep learning architecture and techniques. Deep learning models use multiple layers in an artificial...
在2022年的AAAI会议上，一篇论文提出了Stackelberg Actor-Critic（SAC）算法，它将Stackelberg博弈的原理应用于强化学习，以解决Actor-Critic框架中的梯度循环问题，从而加快收敛速度。在OpenAI gym的多个经典环境中，SAC展现出良好的性能。Stackelberg博弈描述了一种双人博弈，其中一方（Leader，L）先行动，另一方...
他写道:“We have introduced NFSP, the first end-to-end deep reinforcement learning approach to learning approximate Nash Equilibria of imperfect-information games from self-play. Unlike previous game theoretic methods, NFSP is scalable without prior domain knowledge. Furthermore, NFSP is the first...
We then present a taxonomy to classify state-of-the-art solutions into three main categories: modified game models, modified architectures, and modified learning methods. The classification is based on modifications made to the basic GAN model by proposed game-theoretic approaches in the literature....
deep learning using game-theoretic concepts thus, giving a clear insight, chal-lenges, and future directions. The current study also details various real-time applicationsof existing literature, valuable datasets in the field, and the popularity of this research areain recent years of publications ...
Our research aims at providing a more practical solution for the complex real-world green security problems by empowering security games with deep reinforcement learning. Specifically, we propose a novel game model which incorporates the vital element of online information and provide a discussion of ...
This paper is to discuss the development of Deep Reinforcement Learning and the future of it from the perspective of Game Theory. The relationship and potential interaction between these two areas are also introduced, especially the optimization method. This paper discusses about the situations both ...
利用meta-solver求解经验博弈中的Nash均衡，其中Projected Replicator Dynamics是一个重要步骤。并行训练与过拟合衡量针对训练复杂性，Deep Cognitive Hierarchies提出并行训练策略，以降低时间成本。同时，通过joint-policy correlation（JPC）指标，量化了agent间的策略过拟合问题，有效地评估了agent间的适应程度。