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Vector quantization applied to reinforcement learning (1999).
| Content Provider | CiteSeerX |
|---|---|
| Author | Fern, O. Borrajo, Daniel |
| Abstract | Reinforcement learning has proven to be a set of successful techniques for nding optimal policies on uncertain and/or dynamic domains, such as the RoboCup. One of the problems on using such techniques appears with large state and action spaces, as it is the case of input information coming from the Robosoccer simulator. In this paper, we describe a new mechanism for solving the states generalization problem in reinforcement learning algorithms. |
| File Format | |
| Publisher Date | 1999-01-01 |
| Access Restriction | Open |
| Subject Keyword | Reinforcement Learning Vector Quantization Large State Successful Technique State Generalization Problem Optimal Policy Input Information New Mechanism Dynamic Domain Action Space Robosoccer Simulator Reinforcement Learning Algorithm |
| Content Type | Text |
