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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5978| Title: | A Comparative Analysis of Reinforcement Learning Methods |
| Keywords: | reinforcement learning situated agents inputsgeneralization complexity built-in knowledge |
| Issue Date: | 9-Oct-2013 |
| Description: | 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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-1322 http://hdl.handle.net/1721.1/5978 |
| Appears in Collections: | MIT Items |
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