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RLLab

Quickly experiment, intuitive UI, comprehensive library of algorithms & visualization tools to understand RL results.

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What is RLLab?

RLLab is a Python-based framework for reinforcement learning research and experimentation. It provides researchers and engineers with tools to design, run, and analyse RL algorithms without extensive boilerplate code. The platform includes a collection of standard algorithms, benchmark environments, and built-in visualisation tools to help you understand how your models perform. RLLab is aimed at machine learning researchers, academics, and practitioners who need to iterate quickly on RL experiments and compare results across different approaches.

Key features

Algorithm library

Pre-implemented reinforcement learning algorithms including policy gradient methods, Q-learning variants, and actor-critic approaches

Experiment runner

Straightforward interface to configure, launch, and monitor training runs without writing repetitive setup code

Visualisation tools

Built-in plotting and analysis features to examine training curves, policy behaviour, and other key metrics

Environment support

Integration with standard RL benchmarks and the ability to define custom environments

Logging and tracking

Automatic recording of experiment metadata, hyperparameters, and results for easy comparison

Documentation and examples

Tutorials and sample code to get started with common RL tasks

Pros & cons

Advantages

  • Reduces time spent on infrastructure and lets you focus on algorithm design and analysis
  • Good for comparing multiple algorithms against the same problem, thanks to consistent interfaces
  • Free and open-source, making it accessible to students and smaller teams
  • Active documentation and example code help new users get productive quickly

Limitations

  • Steeper learning curve if you're new to reinforcement learning; some prior knowledge of RL concepts is helpful
  • Community and third-party ecosystem smaller than some alternatives, which can mean fewer pre-built extensions
  • Performance optimisation may require understanding of the underlying implementation for large-scale experiments

Use cases

Academic research: testing new RL algorithms and publishing results with reproducible code

Algorithm comparison: benchmarking different methods on the same tasks to identify the best approach for your problem

Teaching and learning: hands-on exploration of how RL algorithms work in practice

Rapid prototyping: quickly testing ideas before committing to production-grade implementations

Ready to try RLLab?

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Pricing

Free

Free

Full access to core framework, algorithm library, visualisation tools, and documentation

Get started with RLLab

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Try RLLab free
  • Free plan available
  • No credit card