[HTML][HTML] Review of large vision models and visual prompt engineering
Visual prompt engineering is a fundamental methodology in the field of visual and image
artificial general intelligence. As the development of large vision models progresses, the …
artificial general intelligence. As the development of large vision models progresses, the …
Deep reinforcement learning for robotics: A survey of real-world successes
Reinforcement learning (RL), particularly its combination with deep neural networks,
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
referred to as deep RL (DRL), has shown tremendous promise across a wide range of …
Champion-level drone racing using deep reinforcement learning
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 …
pilot high-speed aircraft through a 3D circuit. Each pilot sees the environment from the …
Mastering the game of Stratego with model-free multiagent reinforcement learning
We introduce DeepNash, an autonomous agent that plays the imperfect information game
Stratego at a human expert level. Stratego is one of the few iconic board games that artificial …
Stratego at a human expert level. Stratego is one of the few iconic board games that artificial …
Toward an AI era: advances in electronic skins
Electronic skins (e-skins) have seen intense research and rapid development in the past two
decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors …
decades. To mimic the capabilities of human skin, a multitude of flexible/stretchable sensors …
d3rlpy: An offline deep reinforcement learning library
In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL)
library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy …
library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy …
Robust lane change decision making for autonomous vehicles: An observation adversarial reinforcement learning approach
Reinforcementlearning holds the promise of allowing autonomous vehicles to learn complex
decision making behaviors through interacting with other traffic participants. However, many …
decision making behaviors through interacting with other traffic participants. However, many …
Loss of plasticity in deep continual learning
Artificial neural networks, deep-learning methods and the backpropagation algorithm form
the foundation of modern machine learning and artificial intelligence. These methods are …
the foundation of modern machine learning and artificial intelligence. These methods are …
Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning
The advanced cruise control system has expanded the energy-saving potential of the hybrid
electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only …
electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only …
[HTML][HTML] AI-enabled organoids: construction, analysis, and application
L Bai, Y Wu, G Li, W Zhang, H Zhang, J Su - Bioactive Materials, 2024 - Elsevier
Organoids, miniature and simplified in vitro model systems that mimic the structure and
function of organs, have attracted considerable interest due to their promising applications in …
function of organs, have attracted considerable interest due to their promising applications in …