论文标题
扩展马尔可夫游戏，以学习多代理强化学习中的多个任务
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement Learning
论文作者
论文摘要
正式方法与增强学习（RL）的结合最近引起了单个Agent RL学习多任务规格的一种兴趣。在本文中，我们将这种融合扩展到多代理设置，并正式将扩展的马尔可夫游戏定义为一种一般数学模型，允许多个RL代理同时学习各种非马克维亚规格。为了介绍这个新模型，我们提供了正式的定义和证明以及在此框架上运行的RL算法的经验测试。具体来说，我们使用模型来训练两种基于逻辑的多代理RL算法来解决非马克维亚共同安全LTL规范的各种设置。
The combination of Formal Methods with Reinforcement Learning (RL) has recently attracted interest as a way for single-agent RL to learn multiple-task specifications. In this paper we extend this convergence to multi-agent settings and formally define Extended Markov Games as a general mathematical model that allows multiple RL agents to concurrently learn various non-Markovian specifications. To introduce this new model we provide formal definitions and proofs as well as empirical tests of RL algorithms running on this framework. Specifically, we use our model to train two different logic-based multi-agent RL algorithms to solve diverse settings of non-Markovian co-safe LTL specifications.
