Off-Policy Evaluation and Counterfactual Methods in Dynamic Auction Environments
- URL: http://arxiv.org/abs/2501.05278v1
- Date: Thu, 09 Jan 2025 14:39:40 GMT
- Title: Off-Policy Evaluation and Counterfactual Methods in Dynamic Auction Environments
- Authors: Ritam Guha, Nilavra Pathak,
- Abstract summary: Off-Policy Evaluation allows researchers to assess new policies without costly experiments, speeding up the evaluation process.
Online experimental methods, such as A/B tests, are effective but often slow, thus delaying the policy selection and optimization process.
By utilizing counterfactual estimators as a preliminary step before conducting A/B tests, we aim to streamline the evaluation process.
- Score: 0.6445605125467574
- License:
- Abstract: Counterfactual estimators are critical for learning and refining policies using logged data, a process known as Off-Policy Evaluation (OPE). OPE allows researchers to assess new policies without costly experiments, speeding up the evaluation process. Online experimental methods, such as A/B tests, are effective but often slow, thus delaying the policy selection and optimization process. In this work, we explore the application of OPE methods in the context of resource allocation in dynamic auction environments. Given the competitive nature of environments where rapid decision-making is crucial for gaining a competitive edge, the ability to quickly and accurately assess algorithmic performance is essential. By utilizing counterfactual estimators as a preliminary step before conducting A/B tests, we aim to streamline the evaluation process, reduce the time and resources required for experimentation, and enhance confidence in the chosen policies. Our investigation focuses on the feasibility and effectiveness of using these estimators to predict the outcomes of potential resource allocation strategies, evaluate their performance, and facilitate more informed decision-making in policy selection. Motivated by the outcomes of our initial study, we envision an advanced analytics system designed to seamlessly and dynamically assess new resource allocation strategies and policies.
Related papers
- OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators [13.408838970377035]
offline policy evaluation (OPE) allows us to evaluate and estimate a new sequential decision-making policy's performance.
We propose a new algorithm that adaptively blends a set of OPE estimators given a dataset without relying on an explicit selection using a statistical procedure.
Our work contributes to improving ease of use for a general-purpose, estimator-agnostic, off-policy evaluation framework for offline RL.
arXiv Detail & Related papers (2024-05-27T23:51:20Z) - Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning [7.085987593010675]
This work investigates the offline formulation of the contextual bandit problem.
The goal is to leverage past interactions collected under a behavior policy to evaluate, select, and learn new, potentially better-performing, policies.
We introduce novel, fully empirical concentration bounds for a broad class of importance weighting risk estimators.
arXiv Detail & Related papers (2024-05-23T09:07:27Z) - Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy
Evaluation [17.319113169622806]
Off-Policy Evaluation (OPE) aims to assess the effectiveness of counterfactual policies using only offline logged data.
Existing evaluation metrics for OPE estimators primarily focus on the "accuracy" of OPE or that of downstream policy selection.
We develop a new metric, called SharpeRatio@k, which measures the risk-return tradeoff of policy portfolios formed by an OPE estimator.
arXiv Detail & Related papers (2023-11-30T02:56:49Z) - IOB: Integrating Optimization Transfer and Behavior Transfer for
Multi-Policy Reuse [50.90781542323258]
Reinforcement learning (RL) agents can transfer knowledge from source policies to a related target task.
Previous methods introduce additional components, such as hierarchical policies or estimations of source policies' value functions.
We propose a novel transfer RL method that selects the source policy without training extra components.
arXiv Detail & Related papers (2023-08-14T09:22:35Z) - On the Value of Myopic Behavior in Policy Reuse [67.37788288093299]
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
In this work, we present a framework called Selective Myopic bEhavior Control(SMEC)
SMEC adaptively aggregates the sharable short-term behaviors of prior policies and the long-term behaviors of the task policy, leading to coordinated decisions.
arXiv Detail & Related papers (2023-05-28T03:59:37Z) - Improved Policy Evaluation for Randomized Trials of Algorithmic Resource
Allocation [54.72195809248172]
We present a new estimator leveraging our proposed novel concept, that involves retrospective reshuffling of participants across experimental arms at the end of an RCT.
We prove theoretically that such an estimator is more accurate than common estimators based on sample means.
arXiv Detail & Related papers (2023-02-06T05:17:22Z) - A Deep Reinforcement Learning Approach to Marginalized Importance
Sampling with the Successor Representation [61.740187363451746]
Marginalized importance sampling (MIS) measures the density ratio between the state-action occupancy of a target policy and that of a sampling distribution.
We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy.
We evaluate the empirical performance of our approach on a variety of challenging Atari and MuJoCo environments.
arXiv Detail & Related papers (2021-06-12T20:21:38Z) - Reliable Off-policy Evaluation for Reinforcement Learning [53.486680020852724]
In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy.
We propose a novel framework that provides robust and optimistic cumulative reward estimates using one or multiple logged data.
arXiv Detail & Related papers (2020-11-08T23:16:19Z) - Adaptive Estimator Selection for Off-Policy Evaluation [48.66170976187225]
We develop a generic data-driven method for estimator selection in off-policy policy evaluation settings.
We establish a strong performance guarantee for the method, showing that it is competitive with the oracle estimator, up to a constant factor.
arXiv Detail & Related papers (2020-02-18T16:57:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.