The Cart-Pole Application as a Benchmark for Neuromorphic Computing

The cart-pole application is a well-known control application that is often used to illustrate reinforcement learning algorithms with conventional neural networks. An implementation of the application from OpenAI Gym is ubiquitous and popular. Spiking neural networks are the basis of brain-based, or neuromorphic computing. They are attractive, especially as agents for control applications, because of their very low size, weight and power requirements. We are motivated to help researchers in neuromorphic computing to be able to compare their work with common benchmarks, and in this paper we explore using the cart-pole application as a benchmark for spiking neural networks. We propose four parameter settings that scale the application in difficulty, in particular beyond the default parameter settings which do not pose a difficult test for AI agents. We propose achievement levels for AI agents that are trained with these settings. Next, we perform an experiment that employs the benchmark and its difficulty levels to evaluate the effectiveness of eight neuroprocessor settings on success with the application. Finally, we perform a detailed examination of eight example networks from this experiment, that achieve our goals on the difficulty levels, and comment on features that enable them to be successful. Our goal is to help researchers in neuromorphic computing to utilize the cart-pole application as an effective benchmark.