The discount factor does not represent the likelihood to reach the state $s′$from the state $s$. That would be $p(s'|s,a)$, which is not used in Q-Learning, since it is model-free (only model-based ...

In general, yes, they are the same thing, which means to learn from demonstration (LfD). But, usually, apprenticeship learning is mentioned in the context of "Apprenticeship learning via inverse ...

I just found out the answer and it's actually pretty simple: while there is a good linear policy for the mountain car task, the value function itself is non-linear. The state space of this task is ...

The disadvantage would be that your fitness landscape would look less smooth (a small change in your weights may result in a big change in the output or no change at all). This may affect the ...

Reinforcement learning works by bootstrapping, which means that Q values are updated based on previous estimates. Also, MDP's don't generate Q values, just rewards (Q values are long-term estimates of ...

In fact, you're talking about Memetic Algorithms. In the case of a neural network, supposing you have training samples, the individual learning may be done through an iteration of backpropagation. If ...

Your "stuff" will get executed $N(N - 1)/2 = 0.5N^2 - 0.5N$ times. When analyzing the asymptotic complexity, only the highest order term is kept, and multiplicative constants are removed, leaving you ...

At t=1, you just took action a = d at state s = S and ended in s' = S again with 0 reward. So: $Q(S,d) = (1 - 1/2)(1) + 1/2[0 + (1)(1)] = 1$ ($maxQ(s',a')$ is the maximum value you can get from state ...

Input: it is not common to use activation functions in the input layer. Just rescale your data to the [-1;1] interval; Hidden: the $tanh$ activation used to be the most popular. Now, this role has ...