Preliminary Results on a State-Driven Method for Rule Construction in Neural-Symbolic Reinforcement Learning

Deep reinforcement learning has obtained impressive results in the last few years. However, the limitations of deep reinforcement learning with respect to interpretability and generalization have been clearly identified and discussed. In order to overcome these limitations, neural-symbolic methods for reinforcement learning have been recently proposed. This paper presents preliminary results on a new neural-symbolic method for reinforcement learning called State-Driven Neural Logic Reinforcement Learning. The discussed method generates sets of candidate logic rules directly from the states of the environment. Then, it uses a differentiable architecture to select a good subset of the generated rules to successfully complete the training task. The experimental results presented in this paper provide empirical evidence that the discussed method can achieve good performance without requiring the user to specify the structure of the generated rules. Besides being preliminary, the experimental results also suggest that the presented method has sufficient generalization capabilities to allow using learned rules in environments that are sufficiently similar to the training environment. However, this is a preliminary work, and the experimental results show that the proposed method is not yet sufficiently effective.