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- W2962702317 abstract "Deep reinforcement learning techniques have demonstrated superior performance in a wide variety of environments. As improvements in training algorithms continue at a brisk pace, theoretical or empirical studies on understanding what these networks seem to learn, are far behind. In this paper we propose an interpretable neural network architecture for Q-learning which provides a global explanation of the model’s behavior using key-value memories, attention and reconstructible embeddings. With a directed exploration strategy, our model can reach training rewards comparable to the state-of-the-art deep Q-learning models. However, results suggest that the features extracted by the neural network are extremely shallow and subsequent testing using out-of-sample examples shows that the agent can easily overfit to trajectories seen during training." @default.
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- W2962702317 date "2019-07-17" @default.
- W2962702317 modified "2023-10-10" @default.
- W2962702317 title "Towards Better Interpretability in Deep Q-Networks" @default.
- W2962702317 doi "https://doi.org/10.1609/aaai.v33i01.33014561" @default.
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