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1500 questions
6
votes
2 answers
How is parallelism implemented in RL algorithms like PPO?
There are multiple ways to implement parallelism in reinforcement learning. One is to use parallel workers running in their own environments to collect data in parallel, instead of using replay memory buffers (this is how A3C works, for…
alex vdk
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6
votes
1 answer
How to detect LEGO bricks by using a deep learning approach?
In my thesis I dealt with the question how a computer can recognize LEGO bricks. With multiple object detection, I chose a deep learning approach. I also looked at an existing training set of LEGO brick images and tried to optimize it.
My…
melawiki
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6
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1 answer
Can this tic tac toe program be considered AI?
I coded a tic tac toe program, but I don't know if I can call it artificial intelligence.
Here's what I did.
There is a random player, which always makes random valid moves.
And then there is the AI player, which will receive input before every…
Pablo Carrasco Hernández
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6
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1 answer
When should we use algorithms like Adam as opposed to SGD?
As far as I know, Stochastic Gradient Descent is an optimization algorithm which belongs to the the category of algorithms where hyper-parameters have to be defined beforehand. They are useful in many cases, but there are some cases that the…
Utku
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6
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1 answer
Why Q2 is a more or less independant estimate in Twin Delayed DDPG (TD3)?
Twin Delayed Deep Deterministic (TD3) policy gradient is inspired by both double Q-learning and double DQN. In double Q-learning, I understand that Q1 and Q2 are independent because they are trained on different samples. In double DQN, I understand…
Luke Guye
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6
votes
1 answer
Reinforcement Learning with more actions than states
I have read a lot about RL recently. As far as I understood, most RL applications have much more states than there are actions to choose from.
I am thinking about using RL for a problem where I have got a lot of actions to choose from, but only very…
Jan
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Which machine learning algorithm is used in self-driving cars?
Which deep neural network is used in Google's driverless cars to analyze the surroundings? Is this information open to the public?
kenorb
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6
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1 answer
Why is a constant plane of ones added into the input features of AlphaGo?
In the paper Mastering the game of Go with deep neural networks and tree search, the input features of the networks of AlphaGo contains a plane of constant ones and a plane of constant zeros, as following.
Feature #of planes Description
Stone…
Yangcy
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6
votes
1 answer
Why would someone use NEAT over other machine learning algorithms?
Why would someone use a neuroevolution algorithm, such as NEAT, over other machine learning algorithms? What situation would only apply to an algorithm such as NEAT, but no other machine learning algorithm?
Sebastian Dixon
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2 answers
What is "planning" in the context of reinforcement learning, and how is it different from RL and SL?
This is an excerpt taken from Sutton and Barto (pg. 3):
Another key feature of reinforcement learning is that it explicitly considers the whole problem of a goal-directed agent interacting with an uncertain environment. This is in contrast with…
user9947
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, $\pi : \mathcal{S} \rightarrow \mathcal{A}$. This function is the…
nbro
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6
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1 answer
Can TD($\lambda$) be used with deep reinforcement learning?
TD lambda is a way to interpolate between TD(0) - bootstrapping over a single step, and, TD(max), bootstrapping over the entire episode length, or, Monte Carlo.
Reading the link above, I see that an eligibility trace is kept for each state in order…
Gulzar
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6
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2 answers
Rollout algorithm like Monte Carlo search suggest model based reinforcement learning?
From what I understand, Monte Carlo Tree Search Algorithm is a solution algorithm for model free reinforcement learning (RL).
Model free RL means agent doesnt know the transition and reward model. Thus for it to know which next state it will observe…
user21872
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6
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2 answers
What does learning mean?
Can someone explain what is the process of learning? What does it mean to learn something?
Jay Critch
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6
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1 answer
What is a "logit probability"?
DeepMind's paper "Mastering the game of Go without human knowledge" states in its "Methods" section on its "Neural network architecture" that the output layer of AlphaGo Zero's policy head is "A fully connected linear layer that outputs a vector of…
sadakatsu
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