Combining Variational Autoencoders with Reinforcement Learning for Efficient Representation Learning and Adaptive Decision-Making in Complex Environments
Keywords:
Variational Autoencoders, Reinforcement Learning, Deep Learning, Representation Learing, Adaptive Decision-Making, Artificial Intelligence, Machine LearningAbstract
Deep reinforcement learning (DRL) has achieved significant success in various domains; however, its reliance on raw high-dimensional data limits sample efficiency and generalization. Variational Autoencoders (VAEs) offer a powerful method for representation learning by encoding raw observations into a lower-dimensional latent space. This paper explores the integration of VAEs with DRL to enhance learning efficiency and adaptability in complex environments. We discuss existing research from 2023, propose a novel approach that optimizes latent-space representations for policy learning, and evaluate its performance in benchmark environments. Our experiments demonstrate that combining VAEs with reinforcement learning improves both convergence speed and generalization capability.
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