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This Proposed Reinforcement learning supports for optimal path selection for Mobile Robot Navigation in an indoor grid (10×10) environment. Without Prior knowledge in the environment, mobile robot calculates Q-values using current and future discounted reward in each time step. Based on the reinforcement, mobile robot learns the environment, selects the navigation path to reach the goal. Markov Decision...
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