New RL framework uses sparse Gaussian mixture models for efficient learning

Researchers have developed a novel online reinforcement learning framework utilizing sparse Gaussian-mixture-model Q-functions (S-GMM-QFs). This approach employs Hadamard overparametrization to enable interpretable sparsification and smooth regularization, facilitating optimization within the parameter space's Riemannian structure. The framework adaptively identifies meaningful components from a large pool, resulting in sparse models where each component's parameters explicitly encode its geometric role in the state-action space. Numerical tests indicate that S-GMM-QFs achieve performance comparable to or exceeding deep RL methods with significantly fewer parameters and faster learning, while maintaining strong generalization. AI

IMPACT This research introduces a more parameter-efficient and interpretable approach to reinforcement learning, potentially improving generalization in complex environments.

RANK_REASON The cluster contains an academic paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Gaussian-Mixture-Model Q-Functions
• Hadamard Overparametrization
• Online Gradient Descent Learning Algorithms
• reinforcement learning
• S-GMM-QFs

• paper
• other

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