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Learning navigation attractors for mobile robots with reinforcement learning and reservoir computing

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Academic year: 2021

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Figure

Figure 1: Approximate Policy Iteration: Policy improvement + Policy evaluation. The iterative policy learning consists of: gen- gen-eration of samples by interacting with the environment using a ǫ-greedy policy and the trained architecture (policy improvem
Figure 2: Reservoir Computing network as a function approximator for reinforcement learning tasks with partially observable environments
Figure 4: Rectangular environment with an obstacle between the robot and the goal location
Figure 6: (a) A sequence of robot trajectories as learning evolves, using the ESN. Each plot shows robot trajectories in the environment for several episodes during the learning process

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