Explanation-Based Neural Network Learning pp 131-176 | Cite as
Empirical Results
Chapter
Abstract
Armed with an algorithm for learning from delayed reward, we are now ready to apply EBNN in the context of lifelong control learning. This chapter deals with the application of Q-Learning and EBNN in the context of robot control and chess. The key questions underlying this research are:
- 1.
Can EBNN improve learning control when an accurate domain theory is available?
- 2.
How does EBNN perform if the domain theory is poor? Will the analytical component of EBNN hurt the performance if slopes are misleading? How effective is LOB*?
- 3.
How applicable are EBNN and reinforcement learning in the context of control learning problems that involve non-linear target functions and high-dimensional and noisy feature spaces?
Keywords
Action Model Hide Unit Domain Theory Evaluation Network Sonar Sensor
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© Kluwer Academic Publishers 1996