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.

Filter by
Sorted by
Tagged with
0
votes
0answers
36 views

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. ...
2
votes
3answers
80 views

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 ...
1
vote
0answers
29 views

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 ...
0
votes
1answer
260 views

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 ...
1
vote
2answers
104 views

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 ...
3
votes
1answer
264 views

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 ...