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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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A reward optimization method based on action subrewards in hierarchical reinforcement learning.

Yuchen Fu1, Quan Liu2, Xionghong Ling2

  • 1Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, Jiangsu 215123, China ; School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China.

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Summary

This study introduces a hierarchical reinforcement learning (RL) method using action subrewards to address the curse of dimensionality in RL. The approach significantly enhances convergence speed and reduces state spaces, as demonstrated in Tetris online learning.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Reinforcement Learning

Background:

  • Reinforcement learning (RL) methods are characterized by trial-and-error learning and reward signals.
  • The 'curse of dimensionality' poses challenges in RL, leading to exponential state space growth and slow convergence.
  • Existing RL methods struggle with large state spaces and inefficient learning.

Purpose of the Study:

  • To propose a hierarchical reinforcement learning (HRL) method utilizing action subrewards.
  • To mitigate the 'curse of dimensionality' in reinforcement learning.
  • To improve convergence speed and optimize reward functions in RL algorithms.

Main Methods:

  • Developed a hierarchical reinforcement learning framework incorporating action subrewards.
  • Applied the proposed HRL method to the online learning environment of the Tetris game.
  • Compared and analyzed the performance of the algorithm with various parameters.

Main Results:

  • The HRL method with action subrewards significantly enhanced algorithm convergence speed.
  • The proposed approach effectively reduced state spaces, addressing the 'curse of dimensionality' to a certain extent.
  • Experimental results validated the efficiency and improved performance of the new method.

Conclusions:

  • Hierarchical reinforcement learning combined with action subrewards offers an effective solution for the 'curse of dimensionality'.
  • The developed method optimizes reward functions and accelerates convergence in complex learning tasks.
  • This approach shows promise for improving the efficiency and applicability of reinforcement learning.