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Found 10 papers, 4 papers with code
The Value of Reward Lookahead in Reinforcement Learning
In particular, we measure the ratio between the value of standard RL agents and that of agents with partial future-reward lookahead.
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms
In this paper we propose a general methodology to derive regret bounds for randomized multi-armed bandit algorithms.
Towards an efficient and risk aware strategy for guiding farmers in identifying best crop management
Identification of best performing fertilizer practices among a set of contrasting practices with field trials is challenging as crop losses are costly for farmers.
Top Two Algorithms Revisited
Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), for parametric families of arms.
Efficient Algorithms for Extreme Bandits
In this paper, we contribute to the Extreme Bandit problem, a variant of Multi-Armed Bandits in which the learner seeks to collect the largest possible reward.
From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits
The stochastic multi-arm bandit problem has been extensively studied under standard assumptions on the arm's distribution (e. g bounded with known support, exponential family, etc).
From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits
The stochastic multi-arm bandit problem has been extensively studied under standard assumptions on the arm's distribution (e. g bounded with known support, exponential family, etc).
On Limited-Memory Subsampling Strategies for Bandits
There has been a recent surge of interest in nonparametric bandit algorithms based on subsampling.
Optimal Thompson Sampling strategies for support-aware CVaR bandits
In this paper we study a multi-arm bandit problem in which the quality of each arm is measured by the Conditional Value at Risk (CVaR) at some level alpha of the reward distribution.
Sub-sampling for Efficient Non-Parametric Bandit Exploration
In this paper we propose the first multi-armed bandit algorithm based on re-sampling that achieves asymptotically optimal regret simultaneously for different families of arms (namely Bernoulli, Gaussian and Poisson distributions).
