Publication Date
2006
Abstract
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that estimates intermediate rewards, resulting in accelerated learning in later tasks that are related but distinct. Such agents can be trained on a sequence of relatively easy tasks in order to develop a more informative measure of reward that can be transferred to improve performance on more difficult tasks without requiring a hand coded shaping function. We use a rod positioning task to show that this significantly improves performance even after a very brief training period.
Recommended Citation
Konidaris, George, "Autonomous Shaping: Knowledge Transfer in Reinforcement Learning" (2006). Computer Science Department Faculty Publication Series. 99.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/99
Comments
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