Logarithmic Smoothing for Adaptive PAC-Bayesian Off-Policy Learning

• URL: http://arxiv.org/abs/2506.10664v1
• Date: Thu, 12 Jun 2025 12:54:09 GMT
• Title: Logarithmic Smoothing for Adaptive PAC-Bayesian Off-Policy Learning
• Authors: Maxime Haddouche, Otmane Sakhi,
• Abstract summary: Off-policy learning serves as the primary framework for learning optimal policies from logged interactions.<n>We extend this framework to the adaptive scenario using tools from online PAC-Bayesian theory.
• Score: 4.48890356952206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Off-policy learning serves as the primary framework for learning optimal policies from logged interactions collected under a static behavior policy. In this work, we investigate the more practical and flexible setting of adaptive off-policy learning, where policies are iteratively refined and re-deployed to collect higher-quality data. Building on the success of PAC-Bayesian learning with Logarithmic Smoothing (LS) in static settings, we extend this framework to the adaptive scenario using tools from online PAC-Bayesian theory. Furthermore, we demonstrate that a principled adjustment to the LS estimator naturally accommodates multiple rounds of deployment and yields faster convergence rates under mild conditions. Our method matches the performance of leading offline approaches in static settings, and significantly outperforms them when intermediate policy deployments are allowed. Empirical evaluations across diverse scenarios highlight both the advantages of adaptive data collection and the strength of the PAC-Bayesian formulation.

Related papers

• Improving DAPO from a Mixed-Policy Perspective [0.0]
  This paper introduces two novel modifications to the Dynamic sAmpling Policy Optimization (DAPO) algorithm.<n>We first propose a method that incorporates a pre-trained, stable guiding policy to provide off-policy experience.<n>We then extend this idea to re-utilize zero-reward samples, which are often discarded by dynamic sampling strategies.
  arXiv  Detail & Related papers  (2025-07-17T09:12:09Z)
• Relative Entropy Pathwise Policy Optimization [56.86405621176669]
  We show how to construct a value-gradient driven, on-policy algorithm that allow training Q-value models purely from on-policy data.<n>We propose Relative Entropy Pathwise Policy Optimization (REPPO), an efficient on-policy algorithm that combines the sample-efficiency of pathwise policy gradients with the simplicity and minimal memory footprint of standard on-policy learning.
  arXiv  Detail & Related papers  (2025-07-15T06:24:07Z)
• EXPO: Stable Reinforcement Learning with Expressive Policies [74.30151915786233]
  We propose a sample-efficient online reinforcement learning algorithm to maximize value with two parameterized policies.<n>Our approach yields up to 2-3x improvement in sample efficiency on average over prior methods.
  arXiv  Detail & Related papers  (2025-07-10T17:57:46Z)
• POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes [15.681058679765277]
  We propose POLAR, a pessimistic model-based policy learning algorithm for offline dynamic treatment regimes (DTRs)<n> POLAR estimates the transition dynamics from offline data and quantifies uncertainty for each history-action pair.<n>Unlike many existing methods that focus on average training performance, POLAR directly targets the suboptimality of the final learned policy and offers theoretical guarantees.<n> Empirical results on both synthetic data and the MIMIC-III dataset demonstrate that POLAR outperforms state-of-the-art methods and yields near-optimal, history-aware treatment strategies.
  arXiv  Detail & Related papers  (2025-06-25T13:22:57Z)
• Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data [3.6714630660726586]
  offline reinforcement learning (RL) aims to find optimal policies in dynamic environments in order to maximize the expected total rewards by leveraging pre-collected data.<n>Traditional methods focus on learning an optimal policy for all individuals with pre-collected data from a single episode or homogeneous batch episodes.<n>We propose an individualized offline policy optimization framework for heterogeneous time-stationary Markov decision processes.
  arXiv  Detail & Related papers  (2025-05-14T15:44:10Z)
• Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning [57.83919813698673]
  Projected Off-Policy Q-Learning (POP-QL) is a novel actor-critic algorithm that simultaneously reweights off-policy samples and constrains the policy to prevent divergence and reduce value-approximation error. In our experiments, POP-QL not only shows competitive performance on standard benchmarks, but also out-performs competing methods in tasks where the data-collection policy is significantly sub-optimal.
  arXiv  Detail & Related papers  (2023-11-25T00:30:58Z)
• Acceleration in Policy Optimization [50.323182853069184]
  We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimistic and adaptive updates. We define optimism as predictive modelling of the future behavior of a policy, and adaptivity as taking immediate and anticipatory corrective actions to mitigate errors from overshooting predictions or delayed responses to change. We design an optimistic policy gradient algorithm, adaptive via meta-gradient learning, and empirically highlight several design choices pertaining to acceleration, in an illustrative task.
  arXiv  Detail & Related papers  (2023-06-18T15:50:57Z)
• Latent-Variable Advantage-Weighted Policy Optimization for Offline RL [70.01851346635637]
  offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions. In practice, offline datasets are often heterogeneous, i.e., collected in a variety of scenarios. We propose to leverage latent-variable policies that can represent a broader class of policy distributions. Our method improves the average performance of the next best-performing offline reinforcement learning methods by 49% on heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-03-16T21:17:03Z)
• A Regularized Implicit Policy for Offline Reinforcement Learning [54.7427227775581]
  offline reinforcement learning enables learning from a fixed dataset, without further interactions with the environment. We propose a framework that supports learning a flexible yet well-regularized fully-implicit policy. Experiments and ablation study on the D4RL dataset validate our framework and the effectiveness of our algorithmic designs.
  arXiv  Detail & Related papers  (2022-02-19T20:22:04Z)
• Variance Reduction based Experience Replay for Policy Optimization [3.0790370651488983]
  Variance Reduction Experience Replay (VRER) is a framework for the selective reuse of relevant samples to improve policy gradient estimation. VRER forms the foundation of our sample efficient off-policy learning algorithm known as Policy Gradient with VRER.
  arXiv  Detail & Related papers  (2021-10-17T19:28:45Z)
• Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs [71.47895794305883]
  We study the problem of Safe Policy Improvement (SPI) under constraints in the offline Reinforcement Learning setting. We present an SPI for this RL setting that takes into account the preferences of the algorithm's user for handling the trade-offs for different reward signals.
  arXiv  Detail & Related papers  (2021-05-31T21:04:21Z)
• Non-Stationary Off-Policy Optimization [50.41335279896062]
  We study the novel problem of off-policy optimization in piecewise-stationary contextual bandits. In the offline learning phase, we partition logged data into categorical latent states and learn a near-optimal sub-policy for each state. In the online deployment phase, we adaptively switch between the learned sub-policies based on their performance.
  arXiv  Detail & Related papers  (2020-06-15T09:16:09Z)

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.