Natural language-conditioned reinforcement learning (RL) enables agents to follow human instructions.

Graph neural networks (GNNs) have emerged as a powerful tool for learning softwareengineering tasksincluding codecompletion, bugfinding,andprogram repair. The IPA-GNN can be seen either as a continuous relaxation of the RNN model or as a GNN variant more tailored to execution.

Multi-agent games allow sophisticated interactions between agents and environment. Feasible solutions may require non-trivial intra-agent coordination, which leads to substantially more complex strategies than the single-agent setting.

In many real-world scenarios, data to train machine learning models becomes availableovertime.

Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks.

When predicting an image from avectorfield, we introduce color inthe output based on the targetcategory.