Deep reinforcement learning has seen great success across a breadth of tasks such as in game playing and robotic manipulation. However, the modern practice of attempting to learn tabula rasa disregards the logical structure of many domains and the wealth of readily-available human domain experts' knowledge that could help warm start'' the learning process. Further, learning from demonstration techniques are not yet sufficient to infer this knowledge through sampling-based mechanisms in large state and action spaces, or require immense amounts of data. We present a new reinforcement learning architecture that can encode expert knowledge, in the form of propositional logic, directly into a neural, tree-like structure of fuzzy propositions that are amenable to gradient descent. We show that our novel architecture is able to outperform reinforcement and imitation learning techniques across an array of canonical challenge problems for artificial intelligence.
In this paper, a novel, automated process for constructing and initializing deep feedforward neural networks based on decision trees is presented. The proposed algorithm maps a collection of decision trees trained on the data into a collection of initialized neural networks with the structures of the networks determined by the structures of the trees. The tree-informed initialization acts as a warm-start to the neural network training process, resulting in efficiently trained, accurate networks. These models, referred to as “deep jointly informed neural networks” (DJINN), demonstrate high predictive performance for a variety of regression and classification data sets and display comparable performance to Bayesian hyperparameter optimization at a lower computational cost. By combining the user-friendly features of decision tree models with the flexibility and scalability of deep neural networks, DJINN is an attractive algorithm for training predictive models on a wide range of complex data sets.