Practical Reinforcement Learning Agents and Environments Markov Decision Process| packtpub com
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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 .
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