LoRa is one of the most popular technologies for low-power wide area networks. It offers long-range communication with a low energy consumption, which makes it ideal for many applications in the Internet of things. The performance of LoRa networks depends on the communication parameters used by individual nodes. Several works have proposed different solutions, typically running on a central network server, to select these parameters. However, existing approaches have not addressed the need to (re-)assign parameters when channel conditions suddenly vary due to additional traffic, changes in the weather or the presence of obstacles. Moreover, allocation strategies that require a central entity to decide communication parameters do not scale due to the large number of configuration packets that must be sent to the nodes. To address these issues, this work proposes NoReL, a distributed game-theoretic approach that allows nodes to autonomously update their parameters and maximize their packet delivery ratio. NoReL is based on a stochastic variant of no-regret learning, which is proven to reach an ϵ-coarse correlated equilibrium in LoRa networks. Extensive simulations show that NoReL achieves a higher delivery ratio than the state of the art in both static and dynamic environments, with an improvement up to 12%.