🤖Andrew Tate Explains Q-Learning
🤖Q-learning is a reinforcement learning algorithm designed to enable an agent to learn optimal behavior within an environment through trial and error. It’s the technology behind self driving cars, AI chess players, and more!
💡Q-learning approximates the optimal action-value function, known as Q-values. By iteratively updating these values based on rewards obtained from actions, the agent gradually hones its decision-making abilities, ultimately aiming to maximize cumulative rewards over time.
🔥Lazy Programmer is the BEST place for you to learn Q-learning and other Machine Learning subjects! 💯 Click HERE to get up to 90% OFF RL and other courses: https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python?utm_source=youtube&utm_medium=reel&utm_campaign=reel
#datascience #data #datanalytics #mathematics #deeplearning #machinelearning #ai #artificialintelligence #reinforcementlearning
🤖Q-learning is a reinforcement learning algorithm designed to enable an agent to learn optimal behavior within an environment through trial and error. It’s the technology behind self driving cars, AI chess players, and more!
💡Q-learning approximates the optimal action-value function, known as Q-values. By iteratively updating these values based on rewards obtained from actions, the agent gradually hones its decision-making abilities, ultimately aiming to maximize cumulative rewards over time.
🔥Lazy Programmer is the BEST place for you to learn Q-learning and other Machine Learning subjects! 💯 Click HERE to get up to 90% OFF RL and other courses: https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python?utm_source=youtube&utm_medium=reel&utm_campaign=reel
#datascience #data #datanalytics #mathematics #deeplearning #machinelearning #ai #artificialintelligence #reinforcementlearning
