Generating Reinforcement Learning Environments for Industrial Communication Protocols

An important part of any reinforcement learning application is interfacing the agent to its environment. To enable an easier use of reinforcement learning agents in manufacturing and automation-related real-world environments, we propose an environment generator which acts as an adapter between the interface of the agent and existing industrial communication protocols. This paper describes the functionality and architecture of such an environment generator.