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Reinforcement learning streamlined with RLOps.

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open-source v2 just released. 10x faster training.

Single-agent and Multi-agent Support

Train on-policy, off-policy, offline, multi-agent and contextual multi-armed bandits with unmatched speed and performance

Evolutionary Hyperparameter Optimization

Achieve automatic, optimal performance in a single training run through hyperparameter and neural network evolution

Distributed Training

Take full advantage of your entire compute stack for online and offline reinforcement learning with multi-GPU support

Hierarchical Skills

Solve complex problems by breaking down tasks into smaller, learnable sub-tasks with the AgileRL Skills wrapper

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Case studies

Discover how AgileRL is delivering value

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Optimising Logistics Efficiency

Dramatically increasing utilisation and reducing training time for complex bin-packing with Decision Lab

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Revolutionising Algorithmic Trading

Maximising returns from high-frequency trading with Maxvankekeren-IT

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Advancing Robot Learning

Pushing the boundaries of multi-agent reinforcement learning with University of Minnesota

Read the case study