Our work aims to design an intelligent agent that chooses its actions based on compression as a reward signal. The design falls into the category of spiking neural network. In the scenarios tested, the goal of the agent is to compress an input stream of bytes. Neurons are organized by layers and connected to other neurons at adjacent layers. A neuron receives an increase in voltage based on how well its associated action compresses the input stream. The agent performs actions, based on the neuron, according to a probability that’s calculated based on how successful the associated action has been at compressing the input stream and the strength of the neuron’s connections to neighboring neurons that recently fired. The agent has been successful at learning to perform an action out of a subset of actions that leads to the most compression, and at learning sequences of actions that lead to significant or optimal compression. In some specific scenarios, the agent performs close to optimal actions that lead to significant compression rather than the optimal sequence of actions. There are limitations on the length of sequences of actions that can be learned, and some specific types of sequences could not be learned given the current structure, e.g., sequences that change with each iteration could not be learned. This agent compresses data in a novel and effective way and shows promise for simultaneously displaying intelligent behavior and compressing data intelligently.