What question did this study set out to answer?

The aim is to address the exploration-exploitation trade-off in reinforcement learning using chaos theory.

June 14, 2026Open Access

Employing Chaos Theory for Exploration-Exploitation Balance in Reinforcement Learning

Key Points

The aim is to address the exploration-exploitation trade-off in reinforcement learning using chaos theory.
Implemented chaotic systems to generate numbers for action selection.
Tested in environments like Multi-Armed Bandit, taxi-domain, and cliff-walking.
Compared results with traditional action selection methods.
Chaotic methods significantly increased the speed of learning.
Achieved higher rewards compared to traditional action selection methods.
Demonstrated effective exploration of different states and actions.

Abstract

The exploration-exploitation trade-off poses a significant challenge in reinforcement learning. For this reason, action selection methods such as ε-greedy and Soft-Max approaches are used instead of the greedy method. These methods use random numbers to select an action that balances exploration and exploitation. Chaos is commonly utilized across various scientific disciplines because of its features, including non-periodicity, unpredictability, ergodicity and pseudorandom behavior. In this paper, we employ numbers generated by different chaotic systems to select action and identify better maps in diverse states and quantities of actions. Based on our experiments on various environments such as the Multi-Armed Bandit (MAB), taxi-domain, and cliff-walking, we found that many of the chaotic methods increase the speed of learning and achieve higher rewards.

AI से पूछें

Bookmark

View Full Paper