Does approximate Q-learning converge?

Does approximate Q-learning converge?

Value-based methods such as TD-learning [3], Q-learning [4] or SARSA [5] have been exhaustively covered in the literature and, under mild assumptions, have been proven to converge to the desired solution [6]–[8]. In this paper, we describe Q-learning with linear function approximation.

What is meant by premature convergence?

In genetic algorithms, the term of premature convergence means that a population for an optimization problem converged too early, resulting in being suboptimal. An allele is considered lost if, in a population, a gene is present, where all individuals are sharing the same value for that particular gene.

Is Approximate Q-learning optimal?

If Q-value estimates are correct a greedy policy is optimal. Instead of updating based on the best action from the next state, update based on the action your current policy actually takes from the next state.

Is there proof that Q-learning converges when using function?

A complete proof that shows that Q -learning finds the optimal Q function can be found in the paper Convergence of Q-learning: A Simple Proof (by Francisco S. Melo).

Why is convergence of reinforcement learning algorithms important?

The convergence of these methods yields a measure proportional to how reinforcement learning algorithms will converge because reinforcement learning algorithms are sampling-based versions of Value and Policy Iteration, with a few more moving parts.

How is Q-learning similar to Q-value iteration?

Recall: Q-learning is the same update rule as Q-value Iteration, but the transition function is replaced by the action of sampling and the reward function is replaced with the actual sample, r, received from the environment.

What to look for in a convergence proof?

Any convergence proof will be looking for a relationship between the error bound, ε, and the number of steps, N , (iterations). This relationship will give us the chance to bound the performance with an analytical equation. We want the bound of our Utility error at step N — b (N) — to be less than epsilon.