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• W2105486945 abstract "A key component of any reinforcement learning algorithm is the underlying representation used by the agent. While reinforcement learning (RL) agents have typically relied on hand-coded state representations, there has been a growing interest in learning this representation. While inputs to an agent are typically fixed (i.e., state variables represent sensors on a robot), it is desirable to automatically determine the optimal relative scaling of such inputs, as well as to diminish the impact of irrelevant features. This work introduces Holler, a novel distance metric learning algorithm, and combines it with an existing instance-based RL algorithm to achieve precisely these goals. The algorithms' success is highlighted via empirical measurements on a set of six tasks within the mountain car domain." @default.
• W2105486945 created "2016-06-24" @default.
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• W2105486945 date "2011-05-02" @default.
• W2105486945 modified "2023-09-29" @default.
• W2105486945 title "Metric learning for reinforcement learning agents" @default.
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• W2105486945 doi "https://doi.org/10.5555/2031678.2031728" @default.
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