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## Will TD 0 always converge?

In general, batch TD(0) converges to the certainty-equivalence estimate. MC because it is moving toward a better estimate, even though it is not getting all the way there. At the current time nothing more definite can be said about the relative efficiency of on-line TD and Monte Carlo methods.

## Does TD Lambda converge?

TD(λ) is known to converge when used with linear function approximators, if states are sampled according to the policy being evaluated – a scenario called on- policy learning (Tsitsiklis & Van Roy, 1997).

**What policy does Q-learning converge to?**

In addition, Q-learning is exploration insensitive: that is, that the Q values will converge to the optimal values, independent of how the agent behaves while the data is being collected (as long as all state-action pairs are tried often enough).

**Is Q-Learning model based?**

Q-learning is a model-free reinforcement learning algorithm. Q-learning is a values-based learning algorithm. Means it learns the value of the optimal policy independently of the agent’s actions.

### Is Q-Learning 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.

### What is TD error?

TD algorithms adjust the prediction function with the goal of making its values always satisfy this condition. The TD error indicates how far the current prediction function deviates from this condition for the current input, and the algorithm acts to reduce this error.

**Why is Q-learning 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.

**Why Q-learning is off policy?**