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Found 10 papers, 1 papers with code
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
An algorithm is sample-efficient if it uses a number of queries $n$ to the environment that is polynomial in the dimension $d$ of the problem.
Trading-Off Payments and Accuracy in Online Classification with Paid Stochastic Experts
Here, before making their prediction, each expert must be paid.
Delayed Feedback in Kernel Bandits
An abstraction of the problem can be formulated as a kernel based bandit problem (also known as Bayesian optimisation), where a learner aims at optimising a kernelized function through sequential noisy observations.
Delayed Feedback in Generalised Linear Bandits Revisited
However, the stringent requirement for immediate rewards is unmet in many real-world applications where the reward is almost always delayed.
Bandit problems with fidelity rewards
The fidelity bandits problem is a variant of the $K$-armed bandit problem in which the reward of each arm is augmented by a fidelity reward that provides the player with an additional payoff depending on how 'loyal' the player has been to that arm in the past.
Optimism and Delays in Episodic Reinforcement Learning
In this paper, we study the impact of delayed feedback in episodic reinforcement learning from a theoretical perspective and propose two general-purpose approaches to handling the delays.
Local Differential Privacy for Regret Minimization in Reinforcement Learning
Motivated by this, we study privacy in the context of finite-horizon Markov Decision Processes (MDPs) by requiring information to be obfuscated on the user side.
A Unifying View of Optimism in Episodic Reinforcement Learning
The principle of optimism in the face of uncertainty underpins many theoretically successful reinforcement learning algorithms.
Recovering Bandits
We study the recovering bandits problem, a variant of the stochastic multi-armed bandit problem where the expected reward of each arm varies according to some unknown function of the time since the arm was last played.
Bandits with Delayed, Aggregated Anonymous Feedback
In this problem, when the player pulls an arm, a reward is generated, however it is not immediately observed.
