Connecting Reinforcement Learning to Simulation Software
summary
Pathmind connects AnyLogic simulation models to Ray RLlib by adding an AnyLogic plugin Pathmind Helper that exports a standalone model to a web app, trains policies with PPO with population-based training inside the Ray ecosystem, and returns a deployable policy via a REST policy serving endpoint. The factory example uses a tuple of actions (one per table), five discrete moves per car, one‑hot position and target encoding, reward on reaching production steps (time‑discounted), and yields coordinated multi‑agent solutions on a 6‑car scenario.
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chapters (7)
- 0:00Introduction to RL simulation
- 0:28Factory automation challenge
- 3:16Integrating simulation software
- 5:54Bridging worlds with Pathmind
- 7:12Solving the factory puzzle
- 10:53Pathmind features and deployment
- 13:42Closing remarks
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