The reinforcement-learning tag has no wiki summary.
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What is Recurrent Reinforcement Learning
I recently came across the word of "Recurrent Reinforcement Learning". I understand what "Recurrent Neural Network" is and what "Reinforcement Learning" is, but couldn't find much information about ...
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1answer
102 views
Framework for reinforcement learning for games [closed]
[I hope this is the correct place to post - if not, please feel free to migrate the question to another site].
I would like to build a computer program that will play games effectively, and I want to ...
4
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1answer
170 views
Can a model of P(Y|X) be trained via stochastic gradient descent from non-i.i.d. samples of P(X) and i.i.d. samples of P(Y|X)?
When training a parameterized model (e.g. to maximize likelihood) via stochastic gradient descent on some data set, it is commonly assumed that the training samples are drawn i.i.d. from the training ...
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The total collected rewards in the q learning?
In the q learning assignment, I am asked to get the total collected rewards in one episode,
For example, $Q(s,t) = Q(s,t) + a(R + rQ(s',t') - Q(s,t))$.
Which one is it in the following choice?
...
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0answers
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Ultimate Jedi Challenge - Multiarmed Bandit / Reinforcment Learning / advanced AI with a lightsaber twist [closed]
1. Ultimate Jedi Challange - the core
Background story
You are a Jedi master who wants to prepare a training program (online-algorithm) for his apprentice - Luke. Luke needs to practice several ...
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0answers
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Q learning in a stochastic environment
Most examples I have seen about Q learning, are performed in a deterministic world. For example, in the traditional grid world, the agent can finally do the path searching by exploring and exploiting ...
1
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1answer
185 views
Reinforcement Learning Value Iteration Explained
I am having a hard time to understand the value iteration derived from the Bellman equation:
$$V_{k=0}(s) = 0$$
$$\forall s : V_{k+1}(s) = \max_a \Bigr[ R(s,a) + \gamma \sum_{s'} P(s'|s, \pi(s)) ...
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2answers
441 views
Best bandit algorithm?
The most well-known bandit algorithm is upper confidence bound (UCB) which popularized this class of algorithms. Since then I presume there are now better algorithms. What is the current best ...
4
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1answer
111 views
Difference between dynamic programming and temporal difference learning in reinforcement learning
In reinforcement learning, what is the difference between dynamic programming and temporal difference learning?
16
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4answers
265 views
How would you design a machine learning system to play Angry Birds?
After playing way too much Angry Birds, I started to observe my own strategies. It turns out that I developed a very specific approach to getting 3 stars on each level.
That made me wonder about the ...
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0answers
44 views
Reinforcement learning of a policy for multiple actors in large state spaces
I have a real-time domain where I need to assign an action to N actors involving moving one of O objects to one of L locations. At each time step, I'm given a reward R, indicating the overall success ...
1
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1answer
68 views
Getting critics to recognize that two similar input patterns refer to the same output-performance relationship
The actor-critic model is used within temporal difference learning, which is a method within reinforcement learning, to optimize a process on a state-by-state basis by using the difference between ...
8
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1answer
416 views
Optimal algorithm for solving n-armed bandit problems?
I've read about a number of algorithms for solving n-armed bandit problems like $\epsilon$-greedy, softmax, and UCB1, but I'm having some trouble sorting through what approach is best for minimizing ...
4
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2answers
458 views
What statistical technique would be appropriate for optimising the weights?
Background:
I have the following data (an example):
...
