{
  "sections": [
    {
      "id": "what-is-agent",
      "title": "What is an Agent?",
      "icon": "01",
      "content": "An agent is a program that observes the world, makes decisions, and takes actions. Your autonomous car is an agent. It uses sensor data to decide how to drive.",
      "example": "A thermostat is a simple agent: it reads the temperature (observe), compares to target (decide), and turns heating on or off (act).",
      "key_point": "Agents observe → decide → act."
    },
    {
      "id": "what-is-state",
      "title": "What is State?",
      "icon": "02",
      "content": "State is everything the agent can observe about the world right now. In DriveAI, your car's state includes its speed, how close it is to boundaries, and its direction.",
      "example": "{ speed: 42, front_distance: 72, left_distance: 18, right_distance: 35 }",
      "key_point": "State tells the agent where it is and what it can perceive."
    },
    {
      "id": "what-is-action",
      "title": "What is an Action?",
      "icon": "03",
      "content": "An action is what the agent chooses to do. Your car can ACCELERATE, BRAKE, TURN_LEFT, TURN_RIGHT, or COAST. The action changes the environment.",
      "example": "If front_distance is small, a smart agent might BRAKE to avoid a collision.",
      "key_point": "Actions change the world. Choose them based on what you observe."
    },
    {
      "id": "what-is-reward",
      "title": "What is a Reward?",
      "icon": "04",
      "content": "A reward is feedback that tells the agent how well it is doing. Good behaviour gets positive rewards. Bad behaviour gets negative rewards (penalties).",
      "example": "Moving forward: +1. Reaching a checkpoint: +50. Crashing: -100.",
      "key_point": "The agent learns to maximize total reward over time."
    },
    {
      "id": "what-is-rl",
      "title": "What is Reinforcement Learning?",
      "icon": "05",
      "content": "Reinforcement Learning (RL) is how the agent learns from experience. It tries actions, receives rewards, and updates its behaviour to get more rewards in the future.",
      "example": "After many attempts, the agent learns that TURN_RIGHT near a left wall leads to better outcomes than ACCELERATE.",
      "key_point": "RL = trial, error, feedback, improvement."
    },
    {
      "id": "how-q-learning-works",
      "title": "How Q-Learning Works",
      "icon": "06",
      "content": "Q-Learning assigns a value (Q-value) to every (state, action) pair. The Q-value estimates future reward. The agent learns these values through experience and picks the action with the highest Q-value.",
      "example": "Q(near_left_wall, TURN_RIGHT) = 0.83 — the agent has learned turning right is good here.",
      "key_point": "Q-values are the agent's learned memory of what works."
    }
  ]
}
