Performance Metrics for State-Based Imitation Learning
DOI:
https://doi.org/10.32473/flairs.v34i1.128479Palavras-chave:
Imitation Learning, Metrics, Machine Learning, Agent-Based SystemsResumo
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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Publicado
2021-04-18
Como Citar
Zalat, M., & Esfandiari, B. (2021). Performance Metrics for State-Based Imitation Learning. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128479
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Seção
Main Track Proceedings
