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Found 6 papers, 2 papers with code
Testing the Effect of Code Documentation on Large Language Model Code Understanding
Large Language Models (LLMs) have demonstrated impressive abilities in recent years with regards to code generation and understanding.
A Survey of Ad Hoc Teamwork Research
Ad hoc teamwork is the research problem of designing agents that can collaborate with new teammates without prior coordination.
Learning a Robust Multiagent Driving Policy for Traffic Congestion Reduction
This article establishes for the first time that a multiagent driving policy can be trained in such a way that it generalizes to different traffic flows, AV penetration, and road geometries, including on multi-lane roads.
Expected Value of Communication for Planning in Ad Hoc Teamwork
We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly.
Scalable Multiagent Driving Policies For Reducing Traffic Congestion
Next, we propose a modular transfer reinforcement learning approach, and use it to scale up a multiagent driving policy to outperform human-like traffic and existing approaches in a simulated realistic scenario, which is an order of magnitude larger than past scenarios (hundreds instead of tens of vehicles).
Evolutionary Optimization of Deep Learning Activation Functions
The choice of activation function can have a large effect on the performance of a neural network.
