This document discusses using the AnyLogic simulation software together with reinforcement learning. It provides an example of using AnyLogic to model traffic light control and training a reinforcement learning agent using RL4J to optimize the traffic light policy. Key points covered include:
- AnyLogic allows building simulation models that can integrate with reinforcement learning libraries like RL4J.
- A traffic light control simulation was built in AnyLogic and used as the reinforcement learning environment to train an agent's policy for optimizing traffic flow.
- The trained policy was able to improve traffic flow compared to the base simulation and fixed optimization approaches.
- Learned policies from simulation and reinforcement learning can be deployed in real systems to enable adaptive autonomous decision making.