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Performance Metrics for State-Based Imitation Learning
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We propose five new domain-independent metrics for evaluating and comparing performance at imitating a state-based expert. We use two agents in the RoboCup environment to compare the performance metrics: an agent based on a Multi-Layer Perceptron (MLP) and an agent based on a Long Short-Term Memory (LSTM) neural network.
University of Florida George A Smathers Libraries
Title: Performance Metrics for State-Based Imitation Learning
Description:
We propose five new domain-independent metrics for evaluating and comparing performance at imitating a state-based expert.
We use two agents in the RoboCup environment to compare the performance metrics: an agent based on a Multi-Layer Perceptron (MLP) and an agent based on a Long Short-Term Memory (LSTM) neural network.
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