The ratio of reward for different games are very different. For instance I saw the ratio of reward of Linear Q-learning is between 3.8 to 100 times more than DQN? What is the reason of this big difference? How we can clarify this? Is it related to complexity or sensitivity of game? I am not very familiar with complexity and properties of these games.
In addition, in Extended Data Table 2 of this paper it has written it is compared with best linear learner?
Would you please explained this sentence:"Best Linear Learner is the best result obtained by a linear function approximator on different types of hand designed feature"
What is the meaning that by best? The best results during test of linear? Because I only know one linear regression
Also, do you have seen the comparison with Logistic Regression Q-learning? (What I mean by logistic regression Q-learning is to repalce the deep neural network section of DQN with a Sigmoid function between input and output without any hidden layer) What is the ratio of result of this approach in comparison with DQN?