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Robust Uncertainty Estimation Framework In Deep Reinforcement Learning For Active Slam, Bryan Joseph Pedraza
Robust Uncertainty Estimation Framework In Deep Reinforcement Learning For Active Slam, Bryan Joseph Pedraza
Theses and Dissertations
Autonomous mobile robots are essential in various domains such as industry, manufacturing and healthcare. Navigating autonomously and avoiding obstacles are crucial tasks that involve localizing the robot to explore and map unknown environments without prior knowledge. Simultaneous localization and mapping (SLAM) present significant challenges. In this study, we introduce a new approach to address robust navigation and mapping of robot actions using Bayesian Actor-Critic (A2C) reinforcement learning. The A2C framework combines policy-based and value-based learning by dividing the model into two components: (1) the policy model (Actor) determines the actions based on the state, and (2) the value model (Critic) …