AI-Driven Train Control

In this chapter, we will discuss the use of artificial intelligence to control trains in a railway network simulation. The AI module developed in this project uses reinforcement learning (RL) to manage the train's actions, optimizing the train's movement through the network while ensuring safety and efficiency.

8.1 Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with its environment. The agent performs actions in the environment and receives feedback in the form of rewards or penalties. By exploring different actions and learning from the consequences, the agent gradually improves its decision-making abilities.

8.2 TrainAgentAI

The TrainAgentAI struct represents a train agent controlled by a reinforcement learning algorithm. This struct contains several fields, including:

• railway_graph: The railway graph representing the train network
• current_node: The current node the train is at
• target_node: The target node the train is heading to
• agent_rl: The reinforcement learning agent responsible for controlling the train
• trainer: The trainer responsible for training the reinforcement learning agent

8.3 Training the AI Agent

The train method of the TrainAgentAI struct is used to train the reinforcement learning agent for a specified number of iterations. During training, the agent explores different actions and updates its knowledge based on the rewards it receives. The agent uses Q-learning, a widely-used RL algorithm, to learn the best actions for each state.

8.4 Making Decisions

Once the AI agent is trained, it can make decisions based on the current state of the train in the simulation. The best_action method returns the best action for the given state according to the trained reinforcement learning agent.

8.5 Observing the Environment

The observe method allows the train agent to update its internal state based on the current position, target node, and other relevant information. This method ensures that the AI agent has the most up-to-date information about the environment when making decisions.

8.6 Implementing the DecisionAgent Trait

By implementing the DecisionAgent trait for the TrainAgentAI struct, we enable the AI agent to interact with the simulation environment. The next_action and observe methods from the trait implementation allow the AI agent to make decisions and update its state based on the simulation environment.

8.7 Testing the AI Agent

The test case in the tests module demonstrates how to create and train a TrainAgentAI instance. The test uses a sample railway graph and trains the AI agent for a specified number of iterations. After training, the agent can make decisions based on its current state, as shown by the best_action method.

In summary, this chapter has introduced an AI-driven approach to train control in a railway network simulation. Using reinforcement learning, the AI agent learns to make decisions that optimize train movement while maintaining safety and efficiency.