Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement Learning

This paper focus on formally extending Markov Learning (RL) has recently attracted interest as a way for singleagent Games (MGs), the mathematical model that is traditionally used in RL to learn multiple-task specifications. In this paper we extend MARL, to build a new general model, i.e, not focused solely in one this convergence to multi-agent settings and formally define Extended kind of multi-agent game, that allows multiple learning agents to Markov Games as a general mathematical model that allows concurrently fulfill various non-Markovian specifications in multiagent multiple RL agents to concurrently learn various non-Markovian settings. To support our model with empirical evidence, we specifications. To introduce this new model we provide formal definitions also extended two logic-based RL algorithms to multi-agents systems and proofs as well as empirical tests of RL algorithms running in order to show how various learning agents can fulfill different on this framework. Specifically, we use our model to train two different types of non-Markovian specifications expressed in co-safe- Lineartime logic-based multi-agent RL algorithms to solve diverse settings Temporal Logic (LT L). Our results are promising and point to of non-Markovian co-safe LT L specifications.