Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
Deep reinforcement learning is one of the most interesting branches ofartificial intelligence. It is behind some of the most remarkable achievements of the AI community, including beating human ...
MemRL separates stable reasoning from dynamic memory, giving AI agents continual learning abilities without model fine-tuning ...
Machine learning technique teaches power-generating kites to extract energy from turbulent airflows more effectively, boosting their efficiency ...
First, authors provide the mathematical model describing a multi-impulse linear rendezvous problem and the RL algorithms used, and present the RL-based approach to rendezvous design. For the ...
Among those interviewed, one RL environment founder said, “I’ve seen $200 to $2,000 mostly. $20k per task would be rare but ...
At the core of reinforcement learning is the concept that the optimal behavior or action is reinforced by a positive reward. Similar to toddlers learning how to walk who adjust actions based on the ...
We demonstrate the design of low entropy state schemes that increase coherent control in quantum evolution through laser cooling. To do so, we construct an example problem of high impulse lasers that ...
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