- Robotics researchers running RL experiments across simulation and real hardware
- Teams fine-tuning VLA models with reinforcement learning
- Developers building agentic AI systems with RL-based training
Apache-2.0 · Bots
RLinf
Production-grade reinforcement learning infrastructure for embodied and agentic AI.
3.2K stars 0.4K forks Apache-2.0 license 2026-06-04 verified
$
pip install rlinf Open source
Overview
What is RLinf?
RLinf is a flexible and scalable open-source RL infrastructure designed for Embodied and Agentic AI. It supports real-world robot RL on Franka, XSquare Turtle2, and DOS-W1 arms, multiple simulation backends (ManiSkill, LIBERO, MetaWorld, IsaacLab, RoboCasa), and state-of-the-art VLA model fine-tuning (Pi0, Pi0.5, GR00T, OpenVLA). It also extends to agentic AI with support for Search-R1, rStar2, and multi-agent RL.
Unified RL across simulation and real hardware
RLinf supports 10+ simulation backends (ManiSkill, LIBERO, MetaWorld, IsaacLab, RoboCasa, Calvin, etc.) and real-world robots (Franka, XSquare Turtle2, DOS-W1) with the same API.
You can prototype in simulation and deploy on real hardware without rewriting your RL pipeline.State-of-the-art VLA RL fine-tuning
Fine-tune Pi0, Pi0.5, GR00T, OpenVLA, LingBot-VLA and other VLA models using RL algorithms like GRPO, PPO, and DAPO.
VLA models are typically trained with imitation learning only. RLinf enables RL-based post-training that can surpass demonstration quality.Real-world online RL with HG-DAgger
Human-Gated DAgger allows safe online RL on real robots — a human supervisor gates when the policy's actions are used vs. when human corrections are needed.
Online RL on real hardware is dangerous without safety mechanisms. HG-DAgger provides a practical bridge between human demonstrations and autonomous RL.Agentic AI support
Extends beyond robotics to support RL for language agents — Search-R1, rStar2, coding agents, and multi-agent systems.
RLinf is one of the few frameworks that bridges embodied RL and agentic RL in a single codebase. Install
One command to start
$
pip install rlinf Use cases
What teams use it for
Ecosystem
Tags & capabilities
Comparison
How it stacks up
Choose RLinf for production RL across robots and agents
vs specialized RL librariesStable-Baselines3 is simpler for standard RL benchmarks but lacks robot integration. RLinf provides the full stack from simulation to real hardware to agentic AI.
FAQ
Questions
What RL algorithms does RLinf support?
RLinf supports IQL, GRPO, PPO, DAPO, Reinforce++, SAC, CrossQ, RLPD, SAC-Flow, DSRL, and RECAP/CFG among others.
What robots are supported for real-world RL?
Franka Arm (with RealSense, ZED cameras, Franka Hand, Robotiq gripper), XSquare Turtle2 dual-arm, and DOS-W1. More robots are being added.
Can I use RLinf without real hardware?
Yes, RLinf supports 10+ simulation backends including ManiSkill, LIBERO, MetaWorld, IsaacLab, RoboCasa, Calvin, and more — all accessible with the same API.
Decision brief
JSON
Should you use RLinf?
- Beginners looking for a simple out-of-the-box robot control interface (start with LeRobot)
- Verified 2026-06-04
- License: Apache-2.0
- Repo: RLinf/RLinf
- Open-source signal
Deployment
cloud
Permission surface
messages, hardware
Decision signals
No extra signals recorded
Agent packet
Structured decision data for RLinf
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
robotics, messaging
Constraints
open source
Deployment
cloud
Permission surface
messages, hardware
Recommended workflows
Robotics or embodied agent workflow
Overview
What RLinf does
What it is
RLinf is a flexible and scalable RL infrastructure supporting 10+ simulation backends, real-world robot control, VLA model fine-tuning, and agentic AI. It implements major RL algorithms (PPO, GRPO, SAC, DAPO, IQL, CrossQ, RLPD) with a unified API that works identically across simulation and real hardware. Its real-world RL stack includes HG-DAgger for safe online training, and its agentic AI module extends RL to language agents.
Why it matters
Reinforcement learning for embodied AI has been held back by the gap between simulation research and real-world deployment. RLinf bridges this gap by providing the same API across 10+ simulators and multiple real robot platforms. It also bridges the gap between robotics RL and agentic RL — a convergence that is increasingly important as VLA models and language agents share architectures and training techniques.
How to evaluate it
RLinf provides a modular architecture where environments, policies, and algorithms are swappable components. An experiment is configured via YAML or Python dict, specifying the simulator backend (or real robot), the policy model (from MLP to VLA), and the RL algorithm. For real-world RL, the HG-DAgger loop runs a policy on hardware, a human supervisor monitors and intervenes via a GUI, and the system logs both autonomous and human-corrected episodes for training.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type bot
Category Bots
Maturity active
Difficulty Unknown
License Apache-2.0
Pricing open source
Verified 2026-06-04
Source confidence high
Risk level elevated
Fit matrix
Where RLinf fits in an agent stack
strong
Robotics or embodied agent workflow
RLinf has multiple signals for robotics or embodied agent workflow, including matching tags, capabilities, category, or positioning.
- Separate simulator claims from hardware claims and verify safety boundaries before real-world operation.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Coding agent workflow
RLinf has at least one signal for coding agent workflow, but should be checked against a real task before adoption.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Memory or RAG workflow
RLinf has at least one signal for memory or rag workflow, but should be checked against a real task before adoption.
- Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
RLinf has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Browser automation
RLinf is not primarily positioned for browser automation in the current metadata.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Connector or protocol layer
RLinf is not primarily positioned for connector or protocol layer in the current metadata.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Repositories, files, issues, terminal output, and test results
- Prompts, messages, documents, images, or model inputs
- Official setup instructions and a small real workflow
Likely outputs
- Diffs, commits, explanations, test results, or review notes
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
RLinf is listed as open source.
License metadata: Apache-2.0RLinf has a recorded GitHub repository: RLinf/RLinf.
Resource facts and GitHub source link.RLinf supports these recorded deployment modes: cloud.
OpenAgent decision signal metadata.RLinf is tagged with robotics, messaging capabilities.
OpenAgent capability taxonomy.- Dedicated docs link is missing.
- Repository freshness has not been recorded.
Next action
How to start evaluating RLinf
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open sourceInstall RLinf
Install RLinf from PyPI.
pip install rlinf Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about RLinf
What RL algorithms does RLinf support?
RLinf supports IQL, GRPO, PPO, DAPO, Reinforce++, SAC, CrossQ, RLPD, SAC-Flow, DSRL, and RECAP/CFG among others.
What robots are supported for real-world RL?
Franka Arm (with RealSense, ZED cameras, Franka Hand, Robotiq gripper), XSquare Turtle2 dual-arm, and DOS-W1. More robots are being added.
Can I use RLinf without real hardware?
Yes, RLinf supports 10+ simulation backends including ManiSkill, LIBERO, MetaWorld, IsaacLab, RoboCasa, Calvin, and more — all accessible with the same API.