Contents
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 are temporal differences used in Q-learning?
Q-learning uses Temporal Differences (TD) to estimate the value of Q* (s,a). Temporal difference is an agent learning from an environment through episodes with no prior knowledge of the environment. The agent maintains a table of Q [S, A], where S is the set of states and A is the set of actions.
What’s the difference between model-based and Q-learning algorithms?
Whereas, a model-based algorithm is an algorithm that uses the transition function (and the reward function) in order to estimate the optimal policy. Moving in to Q-Learning Q-learning is a model-free reinforcement learning algorithm. Q-learning is a values-based learning algorithm.
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 is the update rule for Q-learning?
The off-policy Q-learning algorithm has the update rule defined by where rt + 1 is the reward observed after performing at in st, and where αt ( s, a ), with all α ∈ [0, 1], is the learning rate which may be the same for all pairs. Q-learning algorithm has problems with big numbers of continuous states and discrete actions.
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.