Reward Function and Initial Values: Better Choices for Accelerated Goal-Directed Reinforcement Learning

Author:

Matignon Laëtitia,Laurent Guillaume J.,Le Fort-Piat Nadine

Publisher

Springer Berlin Heidelberg

Reference10 articles.

1. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, Cambridge (1998)

2. Watkins, C.: Learning from Delayed Rewards. PhD thesis, Cambridge University, Cambridge, England (1989)

3. Mataric, M.J.: Reward functions for accelerated learning. In: Proc. of the 11th ICML, pp. 181–189 (1994)

4. Ng, A.Y., Harada, D., Russell, S.: Policy invariance under reward transformations: theory and application to reward shaping. In: Proc. of the 16th ICML, pp. 278–287 (1999)

5. Wiewiora, E.: Potential-based shaping and Q-value initialization are equivalent. Journal of Artificial Intelligence Research 19, 205–208 (2003)

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