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This paper focuses on the application of reinforcement learning to obstacle avoidance in dynamic environments. Behavior-based control architecture is more robust and better in real-time performance than conventional model based architecture in the control of mobile robot. An intelligent controller is proposed by integrating reinforcement learning with the(More)
This paper focuses on the learning action selection in behavior-based autonomous mobile robot. Autonomous mobile robot needs a large space to store the state-action pair in the application of tabular Q-learning. Neural network has a good ability of generalization, so in this paper Q-learning based on neural network is developed which has a good ability to(More)
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