📌 Reward Shaping Summary

Reward shaping is a technique used in reinforcement learning where additional signals are given to an agent to guide its learning process. By providing extra rewards or feedback, the agent can learn desired behaviours more quickly and efficiently. This helps the agent avoid unproductive actions and focus on strategies that lead to the main goal.

🙋🏻‍♂️ Explain Reward Shaping Simply

Imagine you are learning to ride a bike and your coach cheers you on every time you get closer to balancing, not just when you finally ride perfectly. These small cheers help you know you are on the right track, making it easier to improve. Reward shaping works the same way for artificial agents, giving encouragement for progress, not just the final achievement.

📅 How Can it be used?

Reward shaping can help speed up training in a robot navigation system by giving feedback for each step towards the destination.

🗺️ Real World Examples

In a video game AI, reward shaping is used to encourage non-player characters to collect helpful items along the way to their objectives, not just to reach the end goal. By giving small rewards for picking up items, the AI learns to play more effectively and appears more natural to players.

For a warehouse robot, reward shaping can provide extra points each time the robot successfully avoids obstacles while moving towards a shelf. This helps the robot learn safer and more efficient paths through the warehouse.

✅ FAQ

Reward shaping is a way to help a computer or robot learn tasks faster by giving it extra hints in the form of small rewards. These extra rewards guide it towards making better choices, much like giving a child encouragement when they are learning something new.

Reward shaping is helpful because it makes learning more efficient. Without it, an agent might spend a lot of time trying out actions that do not help it reach its goal. By offering extra feedback, reward shaping keeps the agent focused on actions that actually move it in the right direction.

Yes, if the extra rewards are not planned well, the agent might learn to care more about the hints than the actual goal. This could lead to unwanted behaviour, so it is important to design the rewards so they truly encourage the right actions.

📚 Categories

• Artificial Intelligence
• Model Optimisation Techniques
• Reinforcement Learning Systems

🔗 External Reference Links

Reward Shaping link

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