🗊Презентация Reinforcement learning of fuzzy logic controllers

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Reinforcement learning of fuzzy logic controllers, слайд №1Reinforcement learning of fuzzy logic controllers, слайд №2Reinforcement learning of fuzzy logic controllers, слайд №3Reinforcement learning of fuzzy logic controllers, слайд №4Reinforcement learning of fuzzy logic controllers, слайд №5Reinforcement learning of fuzzy logic controllers, слайд №6Reinforcement learning of fuzzy logic controllers, слайд №7Reinforcement learning of fuzzy logic controllers, слайд №8Reinforcement learning of fuzzy logic controllers, слайд №9Reinforcement learning of fuzzy logic controllers, слайд №10Reinforcement learning of fuzzy logic controllers, слайд №11Reinforcement learning of fuzzy logic controllers, слайд №12Reinforcement learning of fuzzy logic controllers, слайд №13Reinforcement learning of fuzzy logic controllers, слайд №14Reinforcement learning of fuzzy logic controllers, слайд №15Reinforcement learning of fuzzy logic controllers, слайд №16
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Reinforcement learning of fuzzy logic controllers Nursadyk D.
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Reinforcement learning of fuzzy logic controllers Nursadyk D.

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What is fuzzy logic?
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What is fuzzy logic?

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Fuzzy Logic Simple example of the logic for temperature regulator that uses a fan might look like this:
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Fuzzy Logic Simple example of the logic for temperature regulator that uses a fan might look like this:

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Reinforcement learning of fuzzy logic controllers, слайд №4
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Reinforcement learning of fuzzy logic controllers, слайд №5
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Reinforcement learning of fuzzy logic controllers, слайд №6
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Reinforcement learning of fuzzy logic controllers, слайд №7
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Reinforcement learning of fuzzy logic controllers, слайд №8
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Reinforcement learning of fuzzy logic controllers, слайд №9
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There are three types of scheme: FLC – Fuzzy Logic Controllers NN – Neural Networks RL – Reinforcement Learning
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There are three types of scheme: FLC – Fuzzy Logic Controllers NN – Neural Networks RL – Reinforcement Learning

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FLC For Sony legged robots, the output action is the discrete command set, each of which can make the robot move single steps in different directions.
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FLC For Sony legged robots, the output action is the discrete command set, each of which can make the robot move single steps in different directions.

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Reinforcement learning of fuzzy logic controllers, слайд №12
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The input state vector is S = [s1, s2]T = [θ, d]T. This behavior is to control the robot to approach the ball by taking action such as MOVE FORWARD, LFFT FORWARD, RIGHT FORWARD, LEFT TURN, or RIGHT TURN, which are provided by low-level walking software. The input state vector is S = [s1, s2]T = [θ, d]T. This behavior is to control the robot to approach the ball by taking action such as MOVE FORWARD, LFFT FORWARD, RIGHT FORWARD, LEFT TURN, or RIGHT TURN, which are provided by low-level walking software.
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The input state vector is S = [s1, s2]T = [θ, d]T. This behavior is to control the robot to approach the ball by taking action such as MOVE FORWARD, LFFT FORWARD, RIGHT FORWARD, LEFT TURN, or RIGHT TURN, which are provided by low-level walking software. The input state vector is S = [s1, s2]T = [θ, d]T. This behavior is to control the robot to approach the ball by taking action such as MOVE FORWARD, LFFT FORWARD, RIGHT FORWARD, LEFT TURN, or RIGHT TURN, which are provided by low-level walking software.

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Reinforcement learning of fuzzy logic controllers, слайд №14
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Experimental results The experimental results show the FLC can be learned by the proposed reinforcement learning scheme.
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Experimental results The experimental results show the FLC can be learned by the proposed reinforcement learning scheme.

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Thank you for attention! Thank you for attention!
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Thank you for attention! Thank you for attention!

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