Graph Convolutional Memory using Topological Priors

Understand

Solving partially-observable Markov decision processes (POMDPs) is critical when applying reinforcement learning to real-world problems, where agents have an incomplete view of the world.

• We present graph convolutional memory (GCM), the first hybrid memory model for solving POMDPs using reinforcement learning.
• GCM uses either human-defined or data-driven topological priors to form graph neighborhoods, combining them into a larger network topology using dynamic programming.
• We query the graph using graph convolution, coalescing relevant memories into a context-dependent belief.