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Model learning actor-critic algorithms: Performance evaluation in a motion control task
2012 IEEE 51st IEEE Conference on Decision and Control (CDC)
Reinforcement learning (RL) control provides a means to deal with uncertainty and nonlinearity associated with control tasks in an optimal way. The class of actorcritic RL algorithms proved useful for control systems with continuous state and input variables. In the literature, modelbased actor-critic algorithms have recently been introduced to considerably speed up the the learning by constructing online a model through local linear regression (LLR). It has not been analyzed yet whether thedoi:10.1109/cdc.2012.6426427 dblp:conf/cdc/GrondmanBB12 fatcat:iihtxvfeg5bdfg6c4bsvksf4jm