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What are the key points in reward function design in deep reinforcement learning?

Created on 2025-04-28 16:08:17
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The key points in reward function design in deep reinforcement learning include:

  1. Clarity and Simplicity: The reward function should be clear and easy to understand. It should directly reflect the goal of the task.

    • Example: In a game where the goal is to collect coins, the reward function could simply be +1 for each coin collected.

  2. Sparse vs. Dense Rewards: Sparse rewards are given only at the end of a task, while dense rewards are provided more frequently.

    • Example: In a maze-solving task, a sparse reward could be given only when the exit is reached, whereas dense rewards could be given for each step closer to the exit.

  3. Consistency: The reward function should be consistent with the desired behavior, reinforcing actions that lead to the goal and penalizing those that do not.

    • Example: In a self-driving car simulation, crashing should incur a negative reward, while safe driving should receive a positive or neutral reward.

  4. Robustness: The reward function should be robust to small changes in the environment or task parameters.

    • Example: If the layout of a maze changes slightly, the reward function should still effectively guide the agent to the exit.

  5. Scalability: The reward function should scale well with more complex tasks or larger state spaces.

    • Example: In a complex strategy game, the reward function should be designed to handle multiple objectives and interactions.

  6. Avoiding Unintended Consequences: Care must be taken to avoid rewarding undesirable behaviors that are not directly related to the main goal.

    • Example: In a robotics task, rewarding the robot for moving quickly might inadvertently encourage it to take unsafe shortcuts.

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