Continuous Q-learning application

Continuous Q-learning application

Paper in proceedings (conference paper)

Standard algorithm of Q-Learning is limited by discrete states and actions and Q-function is usually represented as discrete table. To avoid this obstacle and extend the use of Q-learning for continuous states and actions the algorithm must be modified and such modification is presented in the paper. Straightforward way is to replace discrete table with suitable approximator. A number of approximators can be used, with respect to memory and computational requirements the local approximator is particularly favorable. We have used Locally Weighted Regression (LWR) algorithm. The paper discusses advantages and disadvantages of modified algorithm demonstrated on simple control task.

Standard algorithm of Q-Learning is limited by discrete states and actions and Q-function is usually represented as discrete table. To avoid this obstacle and extend the use of Q-learning for continuous states and actions the algorithm must be modified and such modification is presented in the paper. Straightforward way is to replace discrete table with suitable approximator. A number of approximators can be used, with respect to memory and computational requirements the local approximator is particularly favorable. We have used Locally Weighted Regression (LWR) algorithm. The paper discusses advantages and disadvantages of modified algorithm demonstrated on simple control task.

Q-learning, Machine learning, Locall approximators

Q-learning, Machine learning, Locall approximators

VĚCHET, S., KREJSA, J.

10.05.2004

Institute of Thermonechanics Academy of Sciences of the Czech Republic, Prague 2004

Prague

80-85918-88-9

Engineering Mechanics 2004

307

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