This work presents some approaches to overcome current Reinforcement Learning limits. We implement a simple virtual environment and some state-of-the-art Reinforcement Learning algorithms for testing and producing a baseline for comparison. Then we implement a Relational Reinforcement Learning algorithm that shows superior performance to the baseline but requires introducing human knowledge. We also propose that Model-based Reinforcement Learning can help us overcome some of the barriers. For better World models, we explore Inductive Logic Programming methods, such as First-Order Inductive Learner, and develop an improved version of it, more adequate to Reinforcement Learning environments. Finally we develop a novel Neural Network architecture, the Inductive Logic Neural Network, to fill the gaps of the previous implementations, that shows great promise.
CITATION STYLE
Rocha, F. M., Costa, V. S., & Reis, L. P. (2020). Overcoming reinforcement learning limits with inductive logic programming. In Advances in Intelligent Systems and Computing (Vol. 1160 AISC, pp. 414–423). Springer. https://doi.org/10.1007/978-3-030-45691-7_38
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