Automatically Configuring Deep Q-Learning agents for the Berkeley Pacman project
Recently, there have been several advances on integrating Deep Neural Networks (DNNs) and Reinforcement Learning (RL) algorithms. These efforts led to the development of Deep Q-Learning (DQL) algorithms which have been applied successfully to develop competitive approaches for multiagent games. Both...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10459.1/64813 |
| Acceso en línea: | http://hdl.handle.net/10459.1/64813 |
| Access Level: | acceso abierto |
| Palabra clave: | Automatic configuration Reinforcement learning Deep Q-learning Aprenentatge automàtic |
| Sumario: | Recently, there have been several advances on integrating Deep Neural Networks (DNNs) and Reinforcement Learning (RL) algorithms. These efforts led to the development of Deep Q-Learning (DQL) algorithms which have been applied successfully to develop competitive approaches for multiagent games. Both DNNs and RL algorithms are highly parameterized and di erent settings can have a dramatic impact on their e ciency. Thus, DQL algorithms can also greatly bene t from a good setting of their parameters. In this project, we show how to apply Automatic Con guration (AC) tools in order to explore efficiently the parameter search space. We have conducted an extensive experimental investigation in the Berkeley Pacman environment which con rms that AC tools can provide up to an additional 20% boost in performance to DQL agents. |
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