  Potential-based reward shaping provides an approach for designing good reward
functions, with the purpose of speeding up learning. However, automatically
finding potential functions for complex environments is a difficult problem (in
fact, of the same difficulty as learning a value function from scratch). We
propose a new framework for learning potential functions by leveraging ideas
from graph representation learning. Our approach relies on Graph Convolutional
Networks which we use as a key ingredient in combination with the probabilistic
inference view of reinforcement learning. More precisely, we leverage Graph
Convolutional Networks to perform message passing from rewarding states. The
propagated messages can then be used as potential functions for reward shaping
to accelerate learning. We verify empirically that our approach can achieve
considerable improvements in both small and high-dimensional control problems.

Machine Learning;Artificial Intelligence