Reinforcement learning in high-diameter, continuous environments
Many important real-world robotic tasks have high diameter, that is, their solution requires a large number of primitive actions by the robot. For example, they may require navigating to distant locations using primitive motor control commands. In addition, modern robots are endowed with rich, high-...
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| Format: | Others |
| Language: | English |
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2008
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| Online Access: | http://hdl.handle.net/2152/3263 |