Questions tagged [thompson-sampling]
For questions about Thompson sampling, which is a technique for choosing actions (that addresses the exploration-exploitation dilemma) in the multi-armed bandit and reinforcement learning problems.
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UCB, Thompson sampling etc seems myopic/greedy for bandits?
When considering multi-armed bandits in different formats, UCB, $\epsilon$-greedy, thompson sampling etc seems so greedy/myopic in the sense that it solely considers reward for the current timestep.
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Intuition behind why Posterior Sampling Lemma holds?
Posterior Sampling Lemma was introduced in the (More) Efficient RL via Posterior Sampling and looks like this.
$M^*$ here is the true MDP while $M_k$ is the MDP sampled from the posterior in episode $...
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How to use UCB or TS in linear programming?
Consider a sequential decision-making problem over $T$ periods where the parameters of the problem should be learned and also optimize an objective function. One possibility is to model the problem as ...
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Minimum sampling for maximising the prediction accuracy
Suppose that I'm training a machine learning model to predict people's age by a picture of their faces. Lets say that I have a dataset of people from 1 year olds to 100 year olds. But I want to choose ...
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Data Imbalance in Contextual Bandit with Thompson Sampling
I'm working with the Online Logistic Regression Algorithm (Algorithm 3) of Chapelle and Li in their paper, "An Empirical Evaluation of Thompson Sampling" (https://papers.nips.cc/paper/2011/...
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Is there a variant of Thompson Sampling that works with variable bandits?
Does there exist a variant of TS, such that, while computing the returns of multi-armed bandits, we have the possibility of introducing an extra bandit?
For instance, while we are applying TS to 3 ...
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What is the Thompson Sampling in simple terms?
I am looking at the different existing methods of action selection in reinforcement learning.
I found several methods like epsilon-greedy, softmax, upper confidence bound and Thompson sampling.
I ...
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Why is Thompson Sampling considered a part of Reinforcement Learning?
I often see Thompson Sampling in RL literature, however, I am not able to relate it to any of the current RL techniques. How exactly does it fit with RL?
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Thompson sampling with Bernoulli prior and non-binary reward update
I am solving a problem for which I have to select the best possible servers (level 1) to hit for a given data. These servers (level 1) in turn hit some other servers (level 2) to complete the request. ...
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Why aren't exploration techniques, such as UCB or Thompson sampling, used in full RL problems?
Why aren't exploration techniques, such as UCB or Thompson sampling, typically used in bandit problems, used in full RL problems?
Monte Carlo Tree Search may use the above-mentioned methods in its ...
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Multi-armed bandits: reducing stochastic multi-armed bandits to bernoulli bandits
Agrawal and Goyal (http://proceedings.mlr.press/v23/agrawal12/agrawal12.pdf page 3) discussed how we can extend Thompson sampling for bernoulli bandits to Thompson sampling for stochastic bandits in ...
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Why am I getting better performance with Thompson sampling than with UCB or $\epsilon$-greedy in a multi-armed bandit problem? [closed]
I ran a test using 3 strategies for multi-armed bandit: UCB, $\epsilon$-greedy, and Thompson sampling.
The results for the rewards I got are as follows:
Thompson sampling had the highest average ...
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Should I use exploration strategy in Policy Gradient algorithms?
In policy gradient algorithms the output is a stochastic policy - a probability for each action.
I believe that if I follow the policy (sample an action from the policy) I make use of exploration ...
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Normalizing Normal Distributions in Thompson Sampling for online Reinforcement Learning
In my implementation of Thompson Sampling (TS) for online Reinforcement Learning, my distribution for selecting $a$ is $\mathcal{N}(Q(s, a), \frac{1}{C(s,a)+1})$, where $C(s,a)$ is the number of times ...
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How to compute the action probabilities with Thompson sampling in deep Q-learning?
In some implementations of off-policy Q-learning, we need to know the action probabilities given by the behavior policy $\mu(a)$ (e.g., if we want to use importance sampling).
In my case, I am using ...
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