Welcome to ALPypeRL documentation!
ALPypeRL or AnyLogic Python Pipe for Reinforcement Learning is an open source library for connecting AnyLogic simulation models with reinforcement learning frameworks that are compatible with OpenAI Gymnasium interface (single agent).
Important
No license is required for single instance experimentation. AnyLogic PLE is free! Download it from here.
Note
ALPypeRL has been developed using ray rllib as the base RL framework. ray rllib is an industry leading open source package for Reinforcement Learning that offers lots of interesting features. Because of that, ALPypeRL has certain dependencies to it (e.g. trained policy deployment and evaluation).
- ALPypeRL
- The AnyLogic Connector
- How to train your first policy. The CartPole-v0 example.
- How to define a space
- How to set continuous actions. The CartPole-v1 example.
- How to set an array of continuous actions. The CarPole-v2 example.
- How to set an array of mixed actions. The CarPole-v3 example.
- Evaluating your trained policy
- Simulation randomness and how to handle failed runs
- How to set simulation parameter values from your training script