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Found 5 papers, 1 papers with code
In-Context Ensemble Learning from Pseudo Labels Improves Video-Language Models for Low-Level Workflow Understanding
We report that in-context learning helps video-language models to generate more temporally accurate SOP, and the proposed in-context ensemble learning can consistently enhance the capabilities of the video-language models in SOP generation.
Training a Vision Language Model as Smartphone Assistant
Addressing the challenge of a digital assistant capable of executing a wide array of user tasks, our research focuses on the realm of instruction-based mobile device control.
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria
A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate.
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
We introduce sequential social dilemmas that share the mixed incentive structure of matrix game social dilemmas but also require agents to learn policies that implement their strategic intentions.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation
We propose solution methods for previously-unsolved constrained MDPs in which actions can continuously modify the transition probabilities within some acceptable sets.
