Q-learning is a model-free reinforcement learning algorithm to learn a policy telling an agent what action to take under what circumstances. Correspondingly, is Q-learning model-free or model-based?
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).
Subsequently, question is, is sarsa model-free? Algorithms that purely sample from experience such as Monte Carlo Control, SARSA, Q-learning, Actor-Critic are "model free" RL algorithms.
Likewise, people ask, why Q-learning is model-free?
Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
Is a form of model-free reinforcement learning?
Many modern reinforcement learning algorithms are model-free, so they are applicable in different environments and can readily react to new and unseen states. In their seminal work on reinforcement learning, authors Barto and Sutton demonstrated model-free RL using a rat in a maze.
Related Question Answers
Why We Use Q learning?
Q-Learning is a value-based reinforcement learning algorithm which is used to find the optimal action-selection policy using a Q function. Our goal is to maximize the value function Q. The Q table helps us to find the best action for each state. Is Deep Q learning model based?
Q-Learning, Deep Q-Networks, and Policy Gradient methods are model-free algorithms because they don't create a model of the environment's transition function. Why Q-learning is off policy?
Q-learning is called off-policy because the updated policy is different from the behavior policy, so Q-Learning is off-policy. In other words, it estimates the reward for future actions and appends a value to the new state without actually following any greedy policy. Who invented Q-learning?
Finally, the temporal-difference and optimal control threads were fully brought together in 1989 with Chris Watkins's development of Q-learning. This work extended and integrated prior work in all three threads of reinforcement learning research. What is the meaning of model-free?
A model-free algorithm is an algorithm that estimates the optimal policy without using or estimating the dynamics (transition and reward functions) of the environment. How does Q learning work?
Q-learning is an off policy reinforcement learning algorithm that seeks to find the best action to take given the current state. It's considered off-policy because the q-learning function learns from actions that are outside the current policy, like taking random actions, and therefore a policy isn't needed. Is Q-learning online?
Reinforcement learning algorithms such as Q-learning can be classified as online learning algorithms, as the reward for each action is determined by what is sensed at that moment. What are the major issues with Q-learning?
A major limitation of Q-learning is that it is only works in environments with discrete and finite state and action spaces. Is sarsa better than Q-learning?
If your goal is to train an optimal agent in simulation, or in a low-cost and fast-iterating environment, then Q-learning is a good choice, due to the first point (learning optimal policy directly). If your agent learns online, and you care about rewards gained whilst learning, then SARSA may be a better choice. What is model-based learning?
Definition. Model-based learning is the formation and subsequent development of mental models by a learner. Most often used in the context of dynamic phenomena, mental models organize information about how the components of systems interact to produce the dynamic phenomena. What is DDPG?
Deep Deterministic Policy Gradient (DDPG) is a model-free off-policy algorithm for learning continous actions. It combines ideas from DPG (Deterministic Policy Gradient) and DQN (Deep Q-Network). What is model-based RL?
Model-based Reinforcement Learning refers to learning optimal behavior indirectly by learning a model of the environment by taking actions and observing the outcomes that include the next state and the immediate reward. 
