Cover Image

SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kiyohara, Haruka, Kishimoto, Ren, Kawakami, Kosuke, Kobayashi, Ken, Nakata, Kazuhide, Saito, Yuta
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
by: Kiyohara, Haruka, et al.
Published: (2023)

Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
by: Shimizu, Tatsuhiro, et al.
Published: (2024)

Balancing Immediate Revenue and Future Off-Policy Evaluation in Coupon Allocation
by: Nishimura, Naoki, et al.
Published: (2024)

Safely Exploring Novel Actions in Recommender Systems via Deployment-Efficient Policy Learning
by: Kiyohara, Haruka, et al.
Published: (2025)

Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction
by: Kiyohara, Haruka, et al.
Published: (2024)

Prompt Optimization with Logged Bandit Data
by: Kiyohara, Haruka, et al.
Published: (2025)

Off-Policy Evaluation and Learning for Survival Outcomes under Censoring
by: Kubota, Kohsuke, et al.
Published: (2026)

Content-Aware Ad Banner Layout Generation with Two-Stage Chain-of-Thought in Vision Language Models
by: Yoshitake, Kei, et al.
Published: (2025)

Off-Policy Evaluation and Learning for the Future under Non-Stationarity
by: Shimizu, Tatsuhiro, et al.
Published: (2025)

A General Framework for Off-Policy Learning with Partially-Observed Reward
by: Takehi, Rikiya, et al.
Published: (2025)

Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies
by: Lee, Haanvid, et al.
Published: (2024)

Evaluation-Time Policy Switching for Offline Reinforcement Learning
by: Neggatu, Natinael Solomon, et al.
Published: (2025)

OffRIPP: Offline RL-based Informative Path Planning
by: Gadipudi, Srikar Babu, et al.
Published: (2024)

OffSim: Offline Simulator for Model-based Offline Inverse Reinforcement Learning
by: Ahn, Woo-Jin, et al.
Published: (2025)

Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL
by: Luo, Yu, et al.
Published: (2024)

Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data
by: Madhow, Sunil, et al.
Published: (2023)

Policy Learning for Off-Dynamics RL with Deficient Support
by: Van, Linh Le Pham, et al.
Published: (2024)

Pessimistic Auxiliary Policy for Offline Reinforcement Learning
by: Zhang, Fan, et al.
Published: (2026)

Offline Trajectory Optimization for Offline Reinforcement Learning
by: Zhao, Ziqi, et al.
Published: (2024)

SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data
by: Romeo, Carlo, et al.
Published: (2026)

On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weighting
by: Zhang, Wenhao, et al.
Published: (2025)

Policy Expansion for Bridging Offline-to-Online Reinforcement Learning
by: Zhang, Haichao, et al.
Published: (2023)

MOBODY: Model Based Off-Dynamics Offline Reinforcement Learning
by: Guo, Yihong, et al.
Published: (2025)

Mediation Analysis for Probabilities of Causation
by: Kawakami, Yuta, et al.
Published: (2024)

Offline Reinforcement Learning with Generative Trajectory Policies
by: Feng, Xinsong, et al.
Published: (2025)

Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone
by: Mark, Max Sobol, et al.
Published: (2024)

Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation
by: Nakanishi, Kosuke, et al.
Published: (2025)

Moments of Causal Effects
by: Kawakami, Yuta, et al.
Published: (2025)

Decomposition of Probabilities of Causation with Two Mediators
by: Kawakami, Yuta, et al.
Published: (2025)

Policy-Based Trajectory Clustering in Offline Reinforcement Learning
by: Hu, Hao, et al.
Published: (2025)

Policy-regularized Offline Multi-objective Reinforcement Learning
by: Lin, Qian, et al.
Published: (2024)

Soft Policy Optimization: Online Off-Policy RL for Sequence Models
by: Cohen, Taco, et al.
Published: (2025)

Potential Outcome Rankings for Counterfactual Decision Making
by: Kawakami, Yuta, et al.
Published: (2025)

Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning
by: Xia, Zhangjie, et al.
Published: (2026)

CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning
by: Rowe, Luke, et al.
Published: (2024)

RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation
by: Kwon, Jeongyeol, et al.
Published: (2024)

Trust the Batch, On- or Off-Policy: Adaptive Policy Optimization for RL Post-Training
by: Fakoor, Rasool, et al.
Published: (2026)

Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback
by: Xiao, Teng, et al.
Published: (2024)

When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
by: Liu, Vincent, et al.
Published: (2023)

Stable Asynchrony: Variance-Controlled Off-Policy RL for LLMs
by: Huang, Luke J., et al.
Published: (2026)