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Found 8 papers, 1 papers with code
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated outputs?
Grounding Gaps in Language Model Generations
To understand the roots of the identified grounding gap, we examine the role of instruction tuning and preference optimization, finding that training on contemporary preference data leads to a reduction in generated grounding acts.
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training.
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
Stackelberg equilibria arise naturally in a range of popular learning problems, such as in security games or indirect mechanism design, and have received increasing attention in the reinforcement learning literature.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Collaboration Promotes Group Resilience in Multi-Agent AI
AI agents need to be robust to unexpected changes in their environment in order to safely operate in real-world scenarios.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games
Human demonstrations of video game play can serve as vital surrogate representations of real-world behaviors, access to which would facilitate rapid progress in several complex learning settings (e. g. behavior classification, imitation learning, offline RL etc.).
Reinforcement Learning of Sequential Price Mechanisms
We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms, which generalizes both serial dictatorship and posted price mechanisms and essentially characterizes all strongly obviously strategyproof mechanisms.
Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
The diagnosis of Alzheimer's disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations.
