Package: maze-rl
Applied Reinforcement Learning with PythonMazeRL is an application oriented Deep Reinforcement Learning (RL) framework, addressing real-world decision problems.
Our vision is to cover the complete development life cycle of RL applications ranging from simulation
engineering up to agent development, training and deployment.This is a preliminary, non-stable release of Maze. It is not yet complete and not all of our interfaces have settled
yet. Hence, there might be some breaking changes on our way towards the first stable release.Spotlight FeaturesBelow we list a few selected Maze features.Design and visualize your policy and value networks with thePerception Module.
It is based on PyTorch and provides a large variety of neural network building blocks and model styles.
Quickly compose powerful representation learners from building blocks such as: dense,
convolution, graph convolution and attention, recurrent architectures, action- and observation masking,
self-attention etc.Create the conditions for efficient RL training without writing boiler plate code, e.g. by supporting
best practices likepre-processingandnormalizingyour observations.Maze supports advancedenvironment structuresreflecting
the requirements of real-world industrial decision problems such as multi-step and multi-agent scenarios.
You can of course work with existingGym-compatible environments.Use the providedMaze trainers(A2C, PPO, Impala, SAC, Evolution Strategies),
which are supporting dictionary action and observation spaces as well as multi-step (auto-regressive policies) training.
Or stick to your favorite tools and trainers bycombining Maze with other RL frameworks.Out of the box support for advanced training workflows such asimitation learning from teacher policies and
policy fine-tuning.Keep even complex application and experiment configuration manageable with theHydra Config System.Get StartedMake surePyTorchis installed and then get the latest released version of Maze as follows:pip install -U maze-rlRead more about other optionslike the installation of the latest
development version.:zap:  We encourage you to start withPython 3.7, as many popular environments like Atari or Box2D can not easily
be installed in newer Python environments. Maze itself supports newer Python versions, but for Python 3.9 you might have toinstall additional binary dependencies manuallyAlternatively you can work with Maze in acontainer with pre-installed Jupyter lab: Rundocker run -p 8888:8888 enliteai/maze:playgroundand openlocalhost:8888in your browser. This loads JupyterTo see Maze in action, check out afirst example.Try your own Gym envor visit ourMaze step-by-step tutorial.InstallationFirst ExampleStep by Step TutorialDocumentationClone thisproject template repoto start your own Maze project.Learn more about MazeThedocumentationis the starting point to learn more about
the underlying concepts, but most importantly also provides code snippets and minimum working examples to
get you started quickly.TheWorkflowsection guides you through typical tasks in a RL projectTrainingRolloutsCollection and Visualizing RolloutsImitation Learning and Fine-TuningPolicy and Value Networksintroduces you to thePerception Module,
how tocustomize action spaces and the underlying action probability distributionsand two styles of policy and value networks construction:Template modelsare composed directly from an environment's observation and action space,
allowing you to train with suitable agent networks on a new environment within minutes.Custom modelsgives you the full
flexibility of application specific models, either with the provided Maze building blocks or
directly with PyTorch.Learn more about coreconcepts and structuressuch as theMaze environment hierarchy, theMaze event systemproviding a convenient way to collect
statistics and KPIs, enable flexible reward formulation and supporting offline analysis.Structured Environments and Action Maskingintroduces you to a general concept, which can greatly improve
the performance of the trained agents in practical RL problems.LicenseMaze is freely available for research and non-commercial use. A commercial license is available, if interested please
contact us on ourcompany websiteor write us anemail.We believe in Open Source principles and aim at transitioning Maze to a commercial Open Source project,
releasing larger parts of the framework under a permissive license in the near future.