Why is Q-learning slow?

The main reason for the slow convergence of Q-learning is the combination of the sample-based stochastic approximation (that makes use of a decaying learning rate) and the fact that the Bellman operator propagates information throughout the whole space (specially when γ is close to 1).

What is the major issues with Q-learning?

Q-learning algorithm has problems with big numbers of continuous states and discrete actions. Usually, it needs function approximations, e.g., neural networks, to associate triplets like state, action, and Q-value.

What is the weakness of Q-learning?

A major limitation of Q-learning is that it is only works in environments with discrete and finite state and action spaces.

What is the problem of Q-learning algorithm?

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 Q-learning slower than SARSA?

Generally speaking, the Sarsa algorithm has faster convergence characteristics, while the Q-learning algorithm has a better final performance. However, Sarsa algorithm is easily stuck in the local minimum and Q-learning needs longer time to learn. Most literatures investigated the action selection policy.

Q Learning Explained (tutorial)

Why is SARSA faster than Q-learning?

Sarsa learns the safe path, along the top row of the grid because it takes the action selection method into account when learning. Because Sarsa learns the safe path, it actually receives a higher average reward per trial than Q-Learning even though it does not walk the optimal path.

Why SARSA is better than Q-learning?

SARSA vs Q-learning

The difference between these two algorithms is that SARSA chooses an action following the current policy and updates its Q-values, whereas Q-learning chooses the greedy action. A greedy action is one that gives the maximum Q-value for the state, that is, it follows an optimal policy.

Does Q-learning have regret?

This paper presents the first non-asymptotic result showing that a model-free algorithm can achieve a logarithmic cumulative regret for episodic tabular reinforcement learning if there exists a strictly positive sub-optimality gap in the optimal Q-function.

Is Q-learning a greedy algorithm?

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. An off-policy algorithm approximates the optimal action-value function, independent of the policy.

Is Q-learning biased?

However, as shown by prior work, double Q-learning is not fully unbiased and suffers from underestimation bias. In this paper, we show that such underestimation bias may lead to multiple non-optimal fixed points under an approximate Bellman operator.

What is better than Q-learning?

SARSA is a value-based method similar to Q-learning. Hence, it uses a Q-table to store values for each state-action pair. With value-based strategies, we train the agent indirectly by teaching it to identify which states (or state-action pairs) are more valuable.

Is Q-learning a deep learning?

The deep Q-learning algorithm employs a deep neural network to approximate values. It generally works by feeding the initial state into the neural network which calculates all possible actions based on the Q-value.

What does Q-learning optimize?

Q-Learning is a Reinforcement learning policy that will find the next best action, given a current state. It chooses this action at random and aims to maximize the reward.

When should I stop Q-learning?

Goal: Train until convergence, but no longer

The easiest way is probably the "old-fashioned" way of plotting your episode returns during training (if it's an episodic task), inspecting the plot yourself, and interrupting the training process when it seems to have stabilized / converged.

What is the difference between Q-learning and deep learning?

A core difference between Deep Q-Learning and Vanilla Q-Learning is the implementation of the Q-table. Critically, Deep Q-Learning replaces the regular Q-table with a neural network. Rather than mapping a state-action pair to a q-value, a neural network maps input states to (action, Q-value) pairs.

Is Q-learning Markov decision?

Q-Learning is the learning of Q-values in an environment, which often resembles a Markov Decision Process. It is suitable in cases where the specific probabilities, rewards, and penalties are not completely known, as the agent traverses the environment repeatedly to learn the best strategy by itself.

What type of algorithm is 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).

Is Q-learning a value based method?

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.

Which learning is less accurate?

Unsupervised learning is intrinsically more difficult than supervised learning as it does not have corresponding output. The result of the unsupervised learning algorithm might be less accurate as input data is not labeled, and algorithms do not know the exact output in advance.

What is regret optimization?

Minimizing (or, alternatively, optimizing for) "regret" is simply reducing the number of actions taken which, in hindsight, it is apparent that there was a better choice.

What is the regret of an algorithm?

It incorporates a regret term in the utility function which depends negatively on the realized outcome and positively on the best alternative outcome given the uncertainty resolution. This regret term is usually an increasing, continuous and non-negative function subtracted to the traditional utility index.

How is Q-learning different from the other TD methods?

Temporal Difference Learning in machine learning is a method to learn how to predict a quantity that depends on future values of a given signal. It can also be used to learn both the V-function and the Q-function, whereas Q-learning is a specific TD algorithm that is used to learn the Q-function.

Why is double Q-learning better?

The paper shows that Double Q-learning might underestimates the action values at times, but avoids the flaw of the overestimation bias that Q-learning does. It also shows that is this type of problems Double Q-learning reaches good performance levels much more quickly.

Why is Q-learning superior to TD learning of values?

Why is temporal difference (TD) learning of Q-values (Q-learning) superior to TD learning of values? Because if you use temporal difference learning on the values, it is hard to extract a policy from the learned values. Specifically, you would need to know the transition model T.