Questions tagged [contextual-bandits]
For questions about the contextual bandit (CB) problem and algorithms that solve it. The CB problem is a generalization of the (context-free) multi-armed bandit problem, where there is more than one situation (or state) and the optimal action to take in one state may be different than the optimal action to take in another state, but where the actions do not affect states (as e.g. in the reinforcement learning problem), but only the rewards.
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Does the off-policy/on-policy regime plays role in Contextual Bandits?
I would like to bring up a point regarding the application of on-policy algorithms, such as REINFORCE, to contextual bandit problems using data collected from other policies.
Here are my thoughts:
In ...
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Python libraries for mulit-armed bandit problems [closed]
I am working on a problem that can be casted as a contextual bandit problem with continuous action space. I would like to tackle it by using something like the contextual zooming algorithm from the ...
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The impact of allowing the reward to be negative in a contextual bandit problem
I posted this question on stats.stackexchange.com 3 days ago. But there has been no answer. I would like to try my luck here.
It seems the contextual bandits problems, as shown in these two papers ...
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How to model the probability of click-through in bandit problem?
Consider a bandit problem in which we want to maximize the probability of click-through based on bid values ($b$ is the value of bid and $\Pr(b)$ shows the probability that a customer clicks on a link ...
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Policy gradient (or more general, RL algorithms) for the problems where actions does not determine next state (next state is independent to action)
I am pretty new in RL. Could anyone suggest results/paper about whether or not policy gradient (or more general RL algorithms) can be applied to the problems where actions does not determine next ...
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How to handled delayed rewards in contextual bandits [closed]
All the examples I see in the tf_Agents for contextual bandits, involves a reward function we generated the reward instantly after an observation has been generated.
But, in my real world usecase (say ...
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Does the policy search work if there is no state to state dependency through actions?
There is a game in which the state comes one after the other without depending on the agent's action. The agent gets a reward for its actions at the end of the game. The goal of the agent is to reach ...
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Multi-armed Bandit in optimization on graph edges selection
I have the problem, which I described below. I wonder if there exists a class of multi-armed bandit approaches that is related to it.
I am working on computer networking optimization.
In the simplest ...
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Is it better to model a Contextual Multi-Armed Bandit problem as an MDP with a non-zero discount factor than treating it as it is?
I'd like to ask if it is, generally, better to model a problem that naturally appears as a Contextual Multi-Armed Bandit like Recommender Systems as a Markov Decision Process with a non-zero discount ...
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Why do I get bad results no matter my neural network function approximator for parametrized Q-learning implementation for Contextual Bandits?
I'd like to ask you why, no matter my neural network function approximator for parametrized Q-learning implementation for a Contextual Bandits environment, I'm getting bad results. I don't know if it'...
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(explore-exploit + supervised learning ) vs contextual bandits
Lets take an ad recommendation problem for 1 slot. Feedback is click/no click. I can solve this by contextual bandits. But I can also introduce exploration in supervised learning, I learn my model ...
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What are the state-of-the-art learning algorithms for contextual bandits with stochastic rewards
I am building a solution for an environment with stochastic rewards in an online setting. I am wondering what the state of the art is in this setting.
Is it $\epsilon$-greedy (with logistic regression)...
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What is the relation between the context in contextual bandits and the state in reinforcement learning?
Conceptually, in general, how is the context being handled in contextual bandits (CB), compared to states in reinforcement learning (RL)?
Specifically, in RL, we can use a function approximator (e.g. ...
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How do I learn the value function for a POMDP with a single-step horizon (bandit)?
Consider a POMDP with a finite number of environment states, $|\mathcal{S}| = N$, but the number of belief states is uncountably infinite. The belief state space is the convex hull of an $N$ simplex. ...
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Is there a UCB type algorithm for linear stochastic bandit with lasso regression?
Why is there no upper confidence bound algorithm for linear stochastic bandits that uses lasso regression in the case that the regression parameters are sparse in the features?
In particular, I don't ...
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Can you convert a MDP problem to a Contextual Multi-Arm Bandits problem?
I'm trying to get a better understanding of Multi-Arm Bandits, Contextual Multi-Arm Bandits and Markov Decision Process.
Basically, Multi-Arm Bandits is a special case of Contextual Multi-Arm Bandits ...
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Can I apply DQN or policy gradient algorithms in the contextual bandit setting?
I have a problem which I believe can be described as a contextual bandit.
More specifically, in each round, I observe a context from the environment consisting of five continuous features, and, ...
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Are bandits considered an RL approach?
If a research paper uses multi-armed bandits (either in their standard or contextual form) to solve a particular task, can we say that they solved this task using a reinforcement learning approach? Or ...
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How do I determine the optimal policy in a bandit problem with missing contexts?
Suppose I learn an optimal policy $\pi(a|c)$ for a contextual multi-armed bandit problem, where the context $c$ is a composite of multiple context variables $c = c_1, c_2, c_3$. For example, the ...
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How to implement a contextual reinforcement learning model?
In a reinforcement learning model, states depend on the previous actions chosen. In the case in which some of the states -but not all- are fully independent of the actions -but still obviously ...
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Why is it useful in some applications to use features that are shared by all arms?
In Li et al. (2010)'s highly cited paper, they talk about LinUCB with hybrid linear models in Section 3.2.
They motivate this by saying
In many applications including ours, it is helpful to use ...
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How can I incorporate domain knowledge to choose actions in the case of large action spaces in multi-armed bandits?
Suppose one is using a multi-armed bandit, and one has relatively few "pulls" (i.e. timesteps) relative to the action set. For example, maybe there are 200 timesteps and 100 possible actions....
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Name of a multiarmed bandit with only some levers available
In order to model a card game, as an exercise, I was thinking of an elementary setting as a multiarmed bandit, each lever being the distribution of expected rewards of a specific card.
But, of course,...
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