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Oral Reinforcement Learnings

2024

The Sample-Communication Complexity Trade-off in Federated Q-Learning
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Reinforcement Learning 🏢 Carnegie Mellon University
Federated Q-learning achieves optimal sample & communication complexities simultaneously via Fed-DVR-Q, a novel algorithm.
Statistical Efficiency of Distributional Temporal Difference Learning
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Reinforcement Learning 🏢 Peking University
Researchers achieve minimax optimal sample complexity bounds for distributional temporal difference learning, enhancing reinforcement learning algorithm efficiency.
Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs
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Reinforcement Learning 🏢 University of Wisconsin-Madison
This paper achieves minimax-optimal bounds for learning near-optimal policies in average-reward MDPs, addressing a long-standing open problem in reinforcement learning.
Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity
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Reinforcement Learning 🏢 University of Michigan
This paper pioneers a modular framework for reinforcement learning, addressing the challenge of learning under complex observations and simpler latent dynamics, offering both statistical and algorithm…
Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models
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Reinforcement Learning 🏢 Korea Institute for Advanced Study
Boosting diffusion model sample quality, especially with few steps, is achieved via a novel maximum entropy inverse reinforcement learning approach, jointly training the model and an energy-based mode…
Learning Formal Mathematics From Intrinsic Motivation
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Reinforcement Learning 🏢 Stanford University
AI agent MINIMO learns to generate challenging mathematical conjectures and prove them, bootstrapping from axioms alone and self-improving in both conjecture generation and theorem proving.
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
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Reinforcement Learning 🏢 Singapore Management University
Boosting AI generalization: CENIE framework quantifies environment novelty via state-action coverage, enhancing unsupervised environment design for robust generalization.