🧠Q-learning is based on the concept of Markov Decision Processes (MDP).
🔑The Q function evaluates the total reward of taking a specific action in a given state.
💡A policy determines the action to take in a given state based on the Q function.
🔄Q-learning iteratively updates the Q function by comparing the predicted reward with the actual reward.
⚖️The optimal policy is determined by the Q values, which represent the expected total reward for each action in each state.
What is Q-learning?
—Q-learning is a reinforcement learning algorithm used to find optimal actions by evaluating the expected total reward for each action in a given state.
What is a policy in Q-learning?
—A policy is a function that determines the action to take in a given state based on the Q values.
How is the Q function updated in Q-learning?
—The Q function is updated by comparing the predicted reward with the actual reward obtained by taking an action in a given state.
What is the role of Markov Decision Processes in Q-learning?
—Q-learning is based on the concept of Markov Decision Processes, which define the states, actions, and rewards in a sequential decision-making process.
How are Q values used to determine the optimal policy?
—The optimal policy is determined by the Q values, which represent the expected total reward for each action in each state. The action with the highest Q value is chosen in each state.
00:00Q-learning is a reinforcement learning algorithm used to find optimal actions based on rewards.
02:11Q-learning is based on the concept of Markov Decision Processes (MDP).
05:21The Q function evaluates the total reward of taking a specific action in a given state.
07:59A policy determines the action to take in a given state based on the Q function.
09:07Q-learning iteratively updates the Q function by comparing the predicted reward with the actual reward.
11:32The optimal policy is determined by the Q values, which represent the expected total reward for each action in each state.
