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Advanced Practical Reinforcement Learning

Track :

Programming

Lessons no : 4

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What will you learn in this course?
  • Design and optimize reinforcement learning policies for complex real-world applications using advanced techniques and algorithms
  • Implement reward functions and value functions to improve agent performance in dynamic environments
  • Develop models of environments to enhance reinforcement learning efficiency and decision-making accuracy
  • Apply practical reinforcement learning strategies to solve real-world problems in robotics, gaming, and autonomous systems

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Lessons | 4


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Beyond the agent and the environment, one can identify four main subelements of a reinforcement learning system: a policy, a reward function, a value function, and, optionally, a model of the environment. A policy defines the learning agent's way of behaving at a given time.