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Found 11 papers, 1 papers with code
Cooperative Multi-Agent Constrained Stochastic Linear Bandits
We show that our regret bound is of order $ \mathcal{O}\left(\frac{d}{\tau-c_0}\frac{\log(NT)^2}{\sqrt{N}}\sqrt{\frac{T}{\log(1/|\lambda_2|)}}\right)$, where $\lambda_2$ is the second largest (in absolute value) eigenvalue of the communication matrix, and $\tau-c_0$ is the known cost gap of a feasible action.
Convex Methods for Constrained Linear Bandits
Recently, bandit optimization has received significant attention in real-world safety-critical systems that involve repeated interactions with humans.
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
In this paper, we prove the first Bayesian regret bounds for Thompson Sampling in reinforcement learning in a multitude of settings.
Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation
Generative Adversarial Networks (GAN) have emerged as a formidable AI tool to generate realistic outputs based on training datasets.
Collaborative Multi-agent Stochastic Linear Bandits
We propose a distributed upper confidence bound (UCB) algorithm and prove a high probability bound on its $T$-round regret in which we include a linear growth of regret associated with each communication round.
Multi-Environment Meta-Learning in Stochastic Linear Bandits
In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments.
Feature and Parameter Selection in Stochastic Linear Bandits
In the second setting, the reward parameter of the LB problem is arbitrarily selected from $M$ models represented as (possibly) overlapping balls in $\mathbb R^d$.
Stage-wise Conservative Linear Bandits
For this problem, we present two novel algorithms, stage-wise conservative linear Thompson Sampling (SCLTS) and stage-wise conservative linear UCB (SCLUCB), that respect the baseline constraints and enjoy probabilistic regret bounds of order O(\sqrt{T} \log^{3/2}T) and O(\sqrt{T} \log T), respectively.
Coagent Networks Revisited
Coagent networks formalize the concept of arbitrary networks of stochastic agents that collaborate to take actions in a reinforcement learning environment.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Safe Linear Thompson Sampling with Side Information
We compare the performance of our algorithm with UCB-based safe algorithms and highlight how the inherently randomized nature of TS leads to a superior performance in expanding the set of safe actions the algorithm has access to at each round.
Using Empirical Covariance Matrix in Enhancing Prediction Accuracy of Linear Models with Missing Information
When sparse models are also suffering from MI, the sparse recovery and inference of the missing models are taken into account simultaneously.
