tinkoff-ai/CORL

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

1372 stars · 177 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

CORL is a research-oriented Python library providing thoroughly benchmarked single-file implementations of state-of-the-art offline and offline-to-online reinforcement learning algorithms such as CQL, IQL, AWAC, DT, and ReBRAC. It emphasizes readable, research-friendly code with Weights & Biases experiment tracking and benchmarked results on D4RL datasets.

Use cases

• implement offline RL algorithms like CQL or IQL from a single readable file
• benchmark offline RL methods on D4RL datasets
• run offline-to-online fine-tuning experiments
• reproduce published offline RL results with transparent hyperparameters
• track and log RL experiments with Weights and Biases
• study performance-relevant implementation details of ORL algorithms

When to choose

• you need clean, single-file reference implementations of offline RL algorithms
• you want benchmarked, reproducible results on D4RL
• you are doing offline RL research and need to tune or extend algorithms
• you want experiment tracking integrated out of the box

When to avoid

• you need online RL algorithms (use CleanRL instead)
• you need discrete-control offline RL (see the authors' Katakomba library)
• you want a production RL training framework with modular abstractions
• you need actively maintained code with frequent updates

Facets

library · maturity maintenance

machine-learning reinforcement-learning benchmarking reinforcement-learning machine-learning artificial-intelligence python offline-rl offline-to-online-rl d4rl single-file-implementations wandb cleanrl-inspired research-codebase gpu docker

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markdown · JSON · MCP: product_card(name="tinkoff-ai/CORL")