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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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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
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Chaos-based reinforcement learning with TD3.

Toshitaka Matsuki1, Yusuke Sakemi2, Kazuyuki Aihara2

  • 1National Defense Academy of Japan, Kanagawa, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|October 28, 2025
PubMed
Summary
This summary is machine-generated.

Chaos-based reinforcement learning (CBRL) agents using Twin Delayed Deep Deterministic Policy Gradients (TD3) can learn to explore and exploit environments. Optimal chaos strength allows agents to adapt to changing conditions.

Keywords:
Chaos-based reinforcement learningEcho state networkTD3

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Chaos-based reinforcement learning (CBRL) utilizes internal chaotic dynamics for agent exploration.
  • Existing CBRL algorithms lack development and integration with recent reinforcement learning advancements.

Purpose of the Study:

  • To integrate the state-of-the-art Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm into CBRL.
  • To investigate the effectiveness of TD3 within CBRL for continuous action spaces.

Main Methods:

  • Implemented TD3, a deep reinforcement learning algorithm, as the learning mechanism for CBRL.
  • Validated the approach on a goal-reaching task with deterministic and continuous action spaces.

Main Results:

  • TD3 successfully functions as a learning algorithm for CBRL in a goal-reaching task.
  • CBRL agents with TD3 demonstrated autonomous suppression of exploration upon learning and resumed exploration when the environment shifted.
  • A suitable range of chaos strength was identified for balancing exploration and exploitation, enhancing adaptation to environmental changes.

Conclusions:

  • TD3 is a viable and effective learning algorithm for advancing CBRL.
  • CBRL agents can dynamically adjust their exploration strategies based on learning progress and environmental dynamics.
  • Optimizing chaos strength is crucial for robust and adaptive reinforcement learning agent behavior.