Linked Questions
10 questions linked to/from Why are Q values updated according to the greedy policy?
2
votes
1
answer
3k
views
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 ...
- 37k
1
vote
2
answers
3k
views
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 ...
- 37k
6
votes
1
answer
454
views
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} \...
- 37k
3
votes
1
answer
1k
views
Why does Deep Q Network outputs multiple Q values?
I am learning Deep RL following this tutorial: https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8
I understand everything but one detail:
This image shows ...
- 37k
7
votes
2
answers
3k
views
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 ...
- 37k
2
votes
1
answer
6k
views
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 ...
- 37k
3
votes
2
answers
721
views
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-...
- 37k
7
votes
1
answer
557
views
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 ...
- 37k
6
votes
1
answer
1k
views
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-...
- 37k
0
votes
1
answer
344
views
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 ...
- 37k