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This paper introduces projection agents for graph combinatorial optimization using reinforcement learning and graph neural networks, operating in a continuous action embedding space to improve generalization and scalability, and releases the LaGCO-RL library.
This paper introduces RLA-WM, a visual feature-based world model that leverages residual latent actions and flow matching to efficiently predict future visual states. The method outperforms existing video-diffusion and feature-based approaches while enabling novel robot learning techniques from offline, actionless demonstration videos.