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The "Q-Learning Explained - A Reinforcement Learning Technique" video introduces the concept of Q-learning, a reinforcement learning technique used to determine the optimal policy in a Markov decision process. It explains how Q-learning algorithm updates the Q values for each state-action pair until they converge to the optimal Q function. The video also highlights the exploration versus exploitation concept in helping an agent make decisions and the importance of a balance between the two. Finally, the video discusses how reinforcement learning is used in turn-based games like Go.
In this section, the video introduces the concept of Q-learning, which is a reinforcement learning technique used to learn the optimal policy in a Markov decision process. The goal of Q-learning is to find the optimal policy by learning the optimal Q values for each state-action pair using the Bellman equation. The video explains how the Q-learning algorithm iteratively updates the Q values for each state-action pair until they converge to the optimal Q function, with the help of a Q table to store the Q values. The video then sets up an example game, called the Lizard Game, to illustrate how Q-learning works.
In this section, the concept of exploration versus exploitation is introduced, which is important for helping us understand how an agent chooses its actions. While it might seem like the agent should always use exploitation to maximize its expected return, a balance of both exploration and exploitation is necessary. An epsilon greedy strategy is used to implement this balance, which we will learn more about in the next video. The article also discusses how reinforcement learning is used in a turn-based game like Go, where a value function is used to help the agent make decisions by assessing the strengths and weaknesses of moves.
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