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.
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.