Contents
What are Q values in RL?
Q Value (Q Function): Usually denoted as Q(s,a) (sometimes with a π subscript, and sometimes as Q(s,a; θ) in Deep RL), Q Value is a measure of the overall expected reward assuming the Agent is in state s and performs action a, and then continues playing until the end of the episode following some policy π.
Is Q-Learning greedy?
Q-learning is an off-policy algorithm. It estimates the reward for state-action pairs based on the optimal (greedy) policy, independent of the agent’s actions. However, due to greedy action selection, the algorithm (usually) selects the next action with the best reward.
Which is the best description of Q-learning?
Q-learning is a model-free reinforcement learning algorithm. Q-learning is a values-based learning algorithm. Value based algorithms updates the value function based on an equation (particularly Bellman equation).
How is the Q value replaced during learning?
After making a move during learning, the Q value for a given state and action is replaced the new value. The new value is a sum of two parts. The first part is (1-learning rate)*old value. This is how much of the old value we retain.
What’s the difference between Double Q and Double Q-learning?
Double Q-learning. A variant called Double Q-learning was proposed to correct this. Double Q-learning is an off-policy reinforcement learning algorithm, where a different policy is used for value evaluation than what is used to select the next action.
What is the update rule for Q-learning?
Here is the basic update rule for q-learning: In the update above there are a couple variables that we haven’t mentioned yet. Whats happening here is we adjust our q-values based on the difference between the discounted new values and the old values. We discount the new values using gamma and we adjust our step size using learning rate (lr).