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In this work, we propose and explore Deep Graph Value Network (DeepGV) as apromising method to work around sample complexity in deepreinforcement-learning agents using a message-passing mechanism. The main ideais that the agent should be guided by structured non-neural-network algorithmslike dynamic programming. According to recent advances in algorithmicalignment, neural networks with structured computation procedures can betrained efficiently. We demonstrate the potential of graph neural network insupporting sample efficient learning by showing that Deep Graph Value Networkcan outperform unstructured baselines by a large margin in solving the MarkovDecision Process (MDP). We believe this would open up a new avenue forstructured agent design. Seehttps://github.com/drmeerkat/Deep-Graph-Value-Network for the code.

Towards Sample Efficient Agents through Algorithmic Alignment