CORL: Research-oriented Deep Offline Reinforcement Learning Library

Q: How reproducible is "CORL: Research-oriented Deep Offline Reinforcement Learning Library"?

Estimated time to first reproduction: a few days. Risk flags: No repository-level reproducibility signals are currently available, Estimate is based on paper-only reproduction flow. No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.

Denis Tarasov, Alexander Nikulin, D. Akimov, Vladislav Kurenkov, С. В. Колесников

Published: Oct 13, 2022

No direct implementation yet

Evidence: Inferred

Domain fit: AI-adjacent

Verified repos: 0

Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.

Time to first repro: a few days

2 risk flags

DOI Publisher

CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms. It emphasizes a simple developing experience with a straightforward codebase and a modern analysis tracking tool. In CORL, we isolate methods implementation into separate single files, making performance-relevant details easier to recognize. Addi ...

Read full abstract

tionally, an experiment tracking feature is available to help log metrics, hyperparameters, dependencies, and more to the cloud. Finally, we have ensured the reliability of the implementations by benchmarking commonly employed D4RL datasets providing a transparent source of results that can be reused for robust evaluation tools such as performance profiles, probability of improvement, or expected online performance.

Technical details

Canonical key: doi-10.48550_arxiv.2210.07105

Cache status: Stale (SWR served)

Generated at: Jun 22, 2026, 9:19 AM

Artifact coverage: sparse

HF provider: ok (mixed)

PWC source used: No

LLM status: not_generated

LLM model: n/a

LLM generated: Unknown

LLM content type: n/a

HF policy: hf-relevance-v27

context only

Benchmarks: missing

Time to repro: a few days

2 risk flags

Results & Benchmarks

Freshness tier: cold

Direct + Inferred Evidence

No concrete benchmark grounding is available yet. Treat the page as context or an implementation starting point only.

CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.

Implementation Evidence Summary

Confidence: low

Recommendation evidence is currently too limited for a maintained-repo choice. Use Implementation Status and Reproduction Path for a practical baseline plan.

Reproduction Risks

Estimate is based on paper-only reproduction flow

Hardware Notes

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Evidence disclosure

Evidence graph: 2 refs, 1 links.

Utility signals: depth 65/100, grounding 58/100, status medium.

Implementation Status

No verified maintained repo

There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.

No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
Start from related paper: Automatically Recommending Peer Reviewers in Modern Code Review.
Start from this likely method family: Reinforcement learning.

Time to first repro: a few days

Reproduction readiness

No Repo

Time to first repro: days

Last checked: Jun 22, 2026

Hardware requirements

Expect multi-day setup/compute for meaningful reproduction based on current guidance.

No verified implementation available

· No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.

No benchmark numbers could be verified. You will not be able to validate reproduction correctness against published numbers.

Hugging Face artifacts

No trustworthy direct or curated related Hugging Face artifacts were found yet.

Continue with targeted Hugging Face searches derived from the paper title and method context:

Models

CORL Reinforcement learning Reinforcement learning model

Datasets

CORL dataset Codebase dataset Reinforcement learning benchmark

Spaces

CORL demo Codebase demo Reinforcement learning gradio

Tip: start with models, then check datasets/spaces if you need evaluation data or demos.

Direct artifact matches are currently sparse. Use targeted Hugging Face searches to quickly locate candidate models, datasets, and demos.

Search models Search datasets Search spaces

Research context

Citations

References

Tasks

Computer science, Codebase, Benchmarking, Implementation, Offline learning, Source code, Feature (linguistics), Reliability (semiconductor)

Methods

Reinforcement learning

Domains

Machine learning, Artificial intelligence

Evaluation & Human Feedback Data

Open this paper in HFEPX to review benchmark signals, evaluation modes, and human-feedback protocol context.

Open in HFEPX

Explore Similar Papers

Jump to Paper2Code search queries derived from this paper's research context.

Computer science Codebase Benchmarking Implementation Offline learning Source code