3 accepted papers
Simulation-based reinforcement learning (RL) is central for robotic control when expert demonstrations are unavailable. However, scaling RL to high-dimensional robots remains challenging. On-policy methods such as PPO are reliable but require large amounts of simulation because they discard past dat…
Text-to-image models are increasingly utilized in design workflows, but articulating nuanced design intentions solely through text remains a challenge. This work proposes a method that extracts visual attributes from a reference image and injects them directly into the generation pipeline. Specifica
Scaling up the model size and computation has brought consistent performance improvements in supervised learning. However, this lesson often fails to apply to reinforcement learning (RL) because training the model on non-stationary data easily leads to overfitting and unstable optimization. In resp…