Module: tf_agents.bandits.policies.falcon_reward_prediction_policy
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Policy that samples actions based on the FALCON algorithm.
This policy implements an action sampling distribution based on the following
paper: David Simchi-Levi and Yunzong Xu, "Bypassing the Monster: A Faster and
Simpler Optimal Algorithm for Contextual Bandits under Realizability",
Mathematics of Operations Research, 2021. https://arxiv.org/pdf/2003.12699.pdf
Classes
class FalconRewardPredictionPolicy: Policy that samples actions based on the FALCON algorithm.
Functions
get_number_of_trainable_elements(...): Gets the total # of elements in the network's trainable variables.
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[]]
View source on GitHub