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Found 15 papers, 3 papers with code
Regret Minimization via Saddle Point Optimization
A long line of works characterizes the sample complexity of regret minimization in sequential decision-making by min-max programs.
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
A practical challenge in reinforcement learning are combinatorial action spaces that make planning computationally demanding.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications
Partial monitoring is an expressive framework for sequential decision-making with an abundance of applications, including graph-structured and dueling bandits, dynamic pricing and transductive feedback models.
Near-optimal Policy Identification in Active Reinforcement Learning
Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces.
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle.
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate.
Bias-Robust Bayesian Optimization via Dueling Bandits
We consider Bayesian optimization in settings where observations can be adversarially biased, for example by an uncontrolled hidden confounder.
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback
Combinatorial bandits with semi-bandit feedback generalize multi-armed bandits, where the agent chooses sets of arms and observes a noisy reward for each arm contained in the chosen set.
Asymptotically Optimal Information-Directed Sampling
We introduce a simple and efficient algorithm for stochastic linear bandits with finitely many actions that is asymptotically optimal and (nearly) worst-case optimal in finite time.
Information Directed Sampling for Linear Partial Monitoring
Partial monitoring is a rich framework for sequential decision making under uncertainty that generalizes many well known bandit models, including linear, combinatorial and dueling bandits.
Distributionally Robust Bayesian Optimization
Attaining such robustness is the goal of distributionally robust optimization, which seeks a solution to an optimization problem that is worst-case robust under a specified distributional shift of an uncontrolled covariate.
Stochastic Bandits with Context Distributions
We introduce a stochastic contextual bandit model where at each time step the environment chooses a distribution over a context set and samples the context from this distribution.
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
In order to scale the method and keep its benefits, we propose an algorithm (LineBO) that restricts the problem to a sequence of iteratively chosen one-dimensional sub-problems that can be solved efficiently.
Information-Directed Exploration for Deep Reinforcement Learning
Efficient exploration remains a major challenge for reinforcement learning.
Information Directed Sampling and Bandits with Heteroscedastic Noise
In the stochastic bandit problem, the goal is to maximize an unknown function via a sequence of noisy evaluations.
