If $t^i - o^i$ is negative, doesn't the power of 2 eliminate any negative result? In the loss function, yes that is correct, and is what you want - a measurement that gets higher due to any ...

To obtain guarantees of convergence for Q table values, you need to decay the learning rate, $\alpha$, at a suitable rate. Too fast and convergence will be to inaccurate values. Too slow and ...

The basis of Q-learning is recursive (similar to dynamic programming), where only the absolute value of the terminal state is known. This may be true in some environments. Many environments do not ...

Conceptually, in general, how is the context being handled in CB, compared to states in RL? In terms of its place in the description of Contextual Bandits and Reinforcement Learning, context in CB is ...

Generally speaking, is it better for rewards to be a scalar, or is using matrices okay? Rewards need to be scalar, real values to match to standard theory of Markov decision processes (MDPs) and ...

A Q table allows you to look up any state/action pair in it and find the associated action value. It is not itself a policy. However, in order to calculate the action values, you will have assumed ...

What does the target Q-values represent? In a DQN, which uses off-policy learning, they represent a refined estimate for the expected future reward from taking an action $a$ in state $s$, and from ...

Is there a term for the humans who do [machine] learning? Typically you will see "AI researchers" for people studying machine intelligence in general, or "data scientists" for ...

However, from the blogs and texts I read, the equations are expressed in terms of V and NOT Q. Why is that? MC and TD are methods for associating value estimates to time step based on experienced ...

For single-step Q learning, the behaviour policy can be any stochastic policy without any further adjustment to the update rules. You don't have to use $\epsilon$-greedy based on current Q function ...

Your main problem is that you need to separate out what is driving the behaviour policy from the Q-table. Q Learning is an off-policy algorithm. The Q-table that it eventually learns is for an optimal ...

In reinforcement learning, is the value of terminal/goal state always zero? Yes, always for episodic problems, the value of a terminal state is always zero, from the definition. The value of a state $... View answer Accepted answer 3 votes I am wondering which definition is correct. The asterisk * in both the definitions stands for "optimal" in the sense of "value when following the optimal policy" So this one is ... View answer Accepted answer 3 votes A tabular system for agent decisions is a direct and simple map of percept to control choice. For each percept received, the agent looks up the percept and cross-references it to the action it should ... View answer Accepted answer 3 votes How would you implement this "Number of Steps" cost? What the paper is referring to is the reward discounting process which is a standard way of formulating RL problems, either continuous ... View answer Accepted answer 3 votes This answer assumes that you only have a problem with this notation from the article:$r : \mathcal{S} \times \mathcal{A} \rightarrow \mathbb{R}$This is a standard notation, used in many ... View answer 3 votes There is a difference between accurate value function estimates, and optimal value functions. An optimal value function is more specifically the value function of an optimal policy. Value functions ... View answer Accepted answer 3 votes A simple feed-forward neural network with at least one hidden layer would suffice in your problem, and can deal with arbitrary non-linear relationships between input and output. If you expect ... View answer Accepted answer 3 votes You appear to comparing the value table update steps in policy iteration and value iteration, which are both derived from Bellman equations. Policy iteration In policy iteration, a policy lookup table ... View answer Accepted answer 3 votes If the episode does not terminate naturally, then if you are breaking it up into pseudo-episodes for training purposes, the one thing you should not do is use the TD target$G_{T-1} = R_T$used for an ... View answer Accepted answer 3 votes The game of TIC-TAC-TOE can be modelled as a non-deterministic Markov decision process (MDP) if, and only if: The opponent is considered part of the environment. This is a reasonable approach when ... View answer Accepted answer 3 votes In the book, the phrase "generate the data" refers to the data from observations about states, actions, next states and rewards, that then get used to make value estimate updates. In both ... View answer Accepted answer 3 votes Reward in reinforcement learning (RL) is entirely different from a supervised learning (SL) label, but can be related to it indirectly. In a RL control setting, you can imagine that you had a data ... View answer Accepted answer 3 votes Why can’t we during the first 1000 episodes allow our agent perform only exploration You can do this. It is fine to do so either to learn the value function of a simple random policy, or when ... View answer Accepted answer 3 votes From comments, you say there is no "outer" goal for picking an adversary other than scoring highly in an individual episode. You could potentially model the initial adversary choice as a ... View answer Accepted answer 3 votes Your calculations are correct, but you have misinterpreted the equations and the diagram. The index$k$in$v_k$for the diagram refers to the policy evaluation update iteration only, and is not ... View answer Accepted answer 3 votes Can someone provide the reasoning behind why$G_{t+1}$is equal to$v_*(S_{t+1})$? The two things are not usually exactly equal, because$G_{t+1}\$ is a probability distribution over all possible ...

Is this a sign that the algorithm diverged? It is a common sign of a problem with learning process. That includes divergence due to poor hyper-parameters, even just bad luck. But it can also point to ...