| Título: | A Comparative Analysis of Reinforcement Learning Methods |
| Autores: | Mataric, Maja |
| Fecha: |
2004-10-04 2004-10-04 1991-10-01 |
| Publicador: | MIT |
| Fuente: |
 |
| Tipo: | |
| Tema: |
reinforcement learning situated agents inputsgeneralization complexity built-in knowledge |
| Descripción: | This paper analyzes the suitability of reinforcement learning (RL) for both programming and adapting situated agents. We discuss two RL algorithms: Q-learning and the Bucket Brigade. We introduce a special case of the Bucket Brigade, and analyze and compare its performance to Q in a number of experiments. Next we discuss the key problems of RL: time and space complexity, input generalization, sensitivity to parameter values, and selection of the reinforcement function. We address the tradeoffs between the built-in and learned knowledge and the number of training examples required by a learning algorithm. Finally, we suggest directions for future research. |
| Idioma: | Inglés |
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