Towards Sample Efficient Agents through Algorithmic Alignment

Deep reinforcement-learning agents have demonstrated great success on various tasks. However, current methods typically suffer from sample complexity problems when learning in high dimensional observation spaces, which limits the application of deep reinforcement-learning agents to complex, uncertain real-world tasks. In this work, we propose and explore Deep Graph Value Network as a promising method to work around this drawback using a message-passing mechanism. The main idea is that the RL agent should be guided by structured non-neural-network algorithms like dynamic programming. According to recent advances in algorithmic alignment, neural networks with structured computation procedures can be trained efficiently. We demonstrate the potential of graph neural network in supporting sample efficient learning by showing that Deep Graph Value Network can outperform unstructured baselines by a large margin with low sample complexity.