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Since the enormous breakthroughs in machine learning over the last decade,functional neural network models are of growing interest for many researchersin the field of computational neuroscience. One major branch of research isconcerned with biologically plausible implementations of reinforcementlearning, with a variety of different models developed over the recent years.However, most studies in this area are conducted with custom simulation scriptsand manually implemented tasks. This makes it hard for other researchers toreproduce and build upon previous work and nearly impossible to compare theperformance of different learning architectures. In this work, we present anovel approach to solve this problem, connecting benchmark tools from the fieldof machine learning and state-of-the-art neural network simulators fromcomputational neuroscience. This toolchain enables researchers in both fieldsto make use of well-tested high-performance simulation software supportingbiologically plausible neuron, synapse and network models and allows them toevaluate and compare their approach on the basis of standardized environmentsof varying complexity. We demonstrate the functionality of the toolchain byimplementing a neuronal actor-critic architecture for reinforcement learning inthe NEST simulator and successfully training it on two different environmentsfrom the OpenAI Gym.

Closing the loop between neural network simulators and the OpenAI Gym