ChatGPT, an artificial intelligence generated content (AIGC) model developed by OpenAI,
has attracted world-wide attention for its capability of dealing with challenging language …

This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

First-person view (FPV) drone racing is a televised sport in which professional competitors
pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the …

Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for
training models with broad, general capabilities for text, images, and other modalities …

Reinforcement learning (RL) provides a theoretical framework for continuously improving an
agent's behavior via trial and error. However, efficiently learning policies from scratch can be …

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range
of complex yet specific control tasks. Yet training generalist agents that can quickly adapt to …

We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven
exploration in visually complex environments. BYOL-Explore learns the world …

Offline reinforcement learning (RL) provides a promising direction to exploit massive amount
of offline data for complex decision-making tasks. Due to the distribution shift issue, current …

Abstract The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in
recent times as they may lead to real-world applications of near-term quantum devices …

Effective exploration is a challenge in reinforcement learning (RL). Novelty-based
exploration methods can suffer in high-dimensional state spaces, such as continuous …