Topic: action representations

Numerous machine learning algorithms incorporate memory models that were inspired by episodic memory. However, almost all of them employ pre-defined and rigid mechanisms for the encoding and retrieval of memories. Usually, a pre-determined type of information is stored at each timestep regardless of whether anything interesting has happened. Here, we investigate a model based on a Memory-Augmented Neural Network, which learns autonomously what and when to store into, and retrieve from, an external memory buffer, while solving a navigation task in a simulated maze. The agent learns to navigate