What is reward shaping?

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Reward shaping in reinforcement learning (RL) is a technique used to guide an agent’s learning by modifying or supplementing the reward signal it receives from the environment. The main idea is to make learning faster and more efficient, especially in environments where rewards are sparse, delayed, or difficult to achieve.

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Key Concepts:

1. Purpose

   • Helps the agent learn desirable behaviors faster by providing more frequent feedback.

   • Encourages progress toward the goal even if the final reward is far away.

2. How It Works

   • Add a shaping reward $F(s, a, s')$ to the original reward $R(s, a, s')$:

     $$R'(s, a, s') = R(s, a, s') + F(s, a, s')$$

   • The shaping reward is designed to guide the agent without changing the optimal policy.

3. Examples

   • Maze navigation: reward the agent for moving closer to the exit.

   • Robot arm: reward for reducing distance to the target before reaching it.

   • Game AI: reward for intermediate objectives like collecting coins or achieving milestones.

4. Benefits

   • Speeds up learning.

   • Reduces the need for extensive random exploration.

   • Helps in environments where final rewards are delayed.

5. Caution

   • Poorly designed shaping rewards can bias the agent toward suboptimal behaviors.

   • Use potential-based shaping to ensure the agent’s optimal policy remains unchanged.

✅ Summary:
Reward shaping is like giving the agent hints or intermediate rewards along the way, helping it learn faster and more efficiently in complex or sparse-reward environments.

Read more:

What is exploration vs exploitation?

What is Markov Decision Process (MDP)?

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