Linked Questions

7 votes
2 answers

What is a time-step in a Markov Decision Process?

The "discounted sum of future rewards" (or return) using discount factor $\gamma$ is $$\gamma^1 r_1 +\gamma^2 r_2 + \gamma^3 r_2 + \dots \tag{1}\label{1}$$ where $r_i$ is the reward received ...
Abhishek Bhatia's user avatar
10 votes
1 answer

Can supervised learning be recast as reinforcement learning problem?

Let's assume that there is a sequence of pairs $(x_i, y_i), (x_{i+1}, y_{i+1}), \dots$ of observations and corresponding labels. Let's also assume that the $x$ is considered as independent variable ...
TomR's user avatar
  • 853
3 votes
1 answer

What is the difference between the $\epsilon$-greedy and softmax policies?

Could someone explain to me which is the key difference between the $\epsilon$-greedy policy and the softmax policy? In particular, in the contest of SARSA and Q-Learning algorithms. I understood the ...
FraMan's user avatar
  • 199
1 vote
2 answers

What is the difference between "ground truth" and "ground-truth labels"?

I'm aware that the ground-truth of the example at the top left-hand corner of the image below is "zero" However, I am confused about the meaning of the terms ground truth and ground-truth ...
JJJohn's user avatar
  • 221
6 votes
1 answer

What are the conditions of convergence of temporal-difference learning?

In reinforcement learning, temporal difference seem to update the value function in each new iteration of experience absorbed from the environment. What would be the conditions for temporal-...
MJeremy's user avatar
  • 163
2 votes
1 answer

What is an agent in Artificial Intelligence?

While studying artificial intelligence, I have often encountered the term "agent" (often autonomous, intelligent). For instance, in fields such as Reinforcement Learning, Multi-Agent Systems, Game ...
olinarr's user avatar
  • 755
3 votes
1 answer

Why does Deep Q Network outputs multiple Q values?

I am learning Deep RL following this tutorial: I understand everything but one detail: This image shows ...
NMO's user avatar
  • 161
3 votes
2 answers

Apart from the state and state-action value functions, what are other examples of value functions used in RL?

In reinforcement learning, we often define two functions, the state-value function $$V^\pi(s) = \mathbb{E}_{\pi} \left[\sum_{k=0}^{\infty} \gamma^{k}R_{t+k+1} \Bigg| S_t=s \right]$$ and the state-...
nbro's user avatar
  • 40.8k
6 votes
1 answer

What is the relation between a policy which is the solution to a MDP and a policy like $\epsilon$-greedy?

In the context of reinforcement learning, a policy, $\pi$, is often defined as a function from the space of states, $\mathcal{S}$, to the space of actions, $\mathcal{A}$, that is, $\pi : \mathcal{S} \...
nbro's user avatar
  • 40.8k
0 votes
1 answer

Why are we using all hyperparameters in RL? [closed]

I am new in RL and I am trying to understand why do we need all these hyperparameters. Can somebody explain me why we use them and what are the best values to use for them? total_episodes = 50000 ...
justStarting's user avatar