# Module: tf_agents.bandits.policies.falcon_reward_prediction_policy

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/agents/blob/v0.19.0/tf_agents/bandits/policies/falcon_reward_prediction_policy.py) |

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`](https://www.tensorflow.org/agents/api_docs/python/tf_agents/bandits/policies/falcon_reward_prediction_policy/FalconRewardPredictionPolicy): Policy that samples actions based on the FALCON algorithm.

## Functions

[`get_number_of_trainable_elements(...)`](https://www.tensorflow.org/agents/api_docs/python/tf_agents/bandits/policies/falcon_reward_prediction_policy/get_number_of_trainable_elements): Gets the total # of elements in the network's trainable variables.