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Found 3 papers, 2 papers with code
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration.
Fine-Tuning Generative Models as an Inference Method for Robotic Tasks
Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions.
Multi-Agent Reinforcement Learning with Multi-Step Generative Models
We consider model-based reinforcement learning (MBRL) in 2-agent, high-fidelity continuous control problems -- an important domain for robots interacting with other agents in the same workspace.
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