ContextualLogitBTSPolicy: Policy: ContextualLogitBTSPolicy
In contextual: Simulation and Analysis of Contextual Multi-Armed Bandit Policies

Description Usage See Also

Policy: ContextualLogitBTSPolicy

1	policy <- ContextualLogitBTSPolicy()

Core contextual classes: Bandit, Policy, Simulator, Agent, History, Plot

Bandit subclass examples: BasicBernoulliBandit, ContextualLogitBandit, OfflineReplayEvaluatorBandit

Policy subclass examples: EpsilonGreedyPolicy, ContextualLinTSPolicy

contextual documentation built on July 26, 2020, 1:06 a.m.

contextual index

README.md Demo: Bandits, Propensity Weighting & Simpson's Paradox in R" Demo: Basic Epsilon Greedy" Demo: Basic Synthetic cMAB Policies Demo: MAB Policies Comparison Demo: MAB Replication Eckles & Kaptein (Bootstrap Thompson Sampling) Demo: MovieLens 10M Dataset" Demo: Offline cMAB: CarsKit DePaul Movie Dataset" Demo: Offline cMAB LinUCB evaluation" Demo: Replication of John Myles White, Bandit Algorithms for Website Optimization" Demo: Replication Sutton & Barto, Reinforcement Learning: An Introduction, Chapter 2 Getting started: running simulations" Offline evaluation: Replication of Li et al 2010

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