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Found 2 papers, 2 papers with code
Learning to Synthesize Programs as Interpretable and Generalizable Policies
To alleviate the difficulty of learning to compose programs to induce the desired agent behavior from scratch, we propose to first learn a program embedding space that continuously parameterizes diverse behaviors in an unsupervised manner and then search over the learned program embedding space to yield a program that maximizes the return for a given task.
Multi-agent Trajectory Prediction with Fuzzy Query Attention
Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning.
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