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Practical Reinforcement Agents

Track :

Programming

Lessons no : 5

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What will you learn in this course?
  • Design and implement reinforcement learning agents for decision-making in real-world scenarios using reward and punishment signals
  • Apply key reinforcement learning algorithms such as Q-learning and Deep Q-Networks to optimize agent performance
  • Analyze environment states and rewards to improve reinforcement learning strategies and outcomes
  • Evaluate the effectiveness of reinforcement agents in practical applications like gaming, robotics, and autonomous systems

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Lessons | 5


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In Reinforcement learning, the agent is one who takes decisions based on the rewards and punishments. Consider an example of a batsman in cricket. He tries to hit the ball if he misses he gets a negative point. If he hits the ball then he gets a reward .