Dueling Network Architectures for Deep Reinforcement Learning

Published October 30, 2023


1 min read

Image of Jarek Liesen

Jarek Liesen

Introducing the “Dueling Network Architecture” in reinforcement learning, this paper breaks away from conventional neural network structures by independently estimating the state value function and state-dependent action advantage function. This novel factorization facilitates more effective policy evaluation in scenarios with multiple similar-valued actions, all while seamlessly integrating with existing reinforcement learning algorithms. The paper’s results demonstrate that this groundbreaking approach outperforms state-of-the-art methods in the challenging Atari 2600 domain, offering a promising leap forward in the realm of artificial intelligence and game-playing agents.

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