Why RLlib?

Learn what makes RLlib stand out and why it’s the reinforcement learning library of choice for companies like Wildlife Studios, McKinsey / QuantumBlack and others.

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Vibrant community

Reinforcement learning is hard. Easily find code examples and connect with other developers and experts.

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Distributed-first

Iterate quickly without needing to rewrite again to go to production or scale to a large cluster.

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State of the art

Choose from the latest and greatest in reinforcement learning algorithms to find the one best suited for your problem. Enjoy multi-agent support in all.

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Supports external simulators

Optimize your policies using an industry- or problem-specific external simulator. Connect simulations to RLlib via its PolicyServer/Client architecture.

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Seriously fast

Experience really fast policy evaluation, with lower overhead than most algorithms.

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Tap into Ray ecosystem

Find the perfect set of hyperparameters using Ray Tune. Serve your trained model in a massively parallel way with Ray Serve.

Try it yourself

Install RLlib (and PyTorch for this example) with pip install 'ray[rllib]' torch and give this example a try.

from ray import tune
from ray.rllib.agents.ppo import PPOTrainer
 
tune.run(PPOTrainer, config={
   "env": "CartPole-v0",
   "framework": "torch",
   "log_level": "INFO"
})

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See RLlib in action

Learn how your peers are using RLlib to scale up and accelerate their reinforcement learning workloads.

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Wildlife Studios

Mobile gaming giant, Wildlife Studios improved player lifetime value by creating and serving better in-game offers with RLlib.

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Two Sigma

Learn how Financial Services company, Two Sigma, got a 24x speedup on their reinforcement learning use case with RLlib.

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Pathmind

Pathmind shares how they use RLlib for reinforcement learning in industrial applications, and why they think this approach can outperform others.

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Do more with Ray

Expand your Ray journey beyond reinforcement learning and bring fast and easy distributed execution to other use cases.

Ray SGD

Scale deep learning

Ray Tune

Scale hyperparameter search.

Ray Datasets

Scale data loading and processing.