A comprehensive survey of data augmentation in visual reinforcement learning
Visual reinforcement learning (RL), which makes decisions directly from high-dimensional
visual inputs, has demonstrated significant potential in various domains. However …
visual inputs, has demonstrated significant potential in various domains. However …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Learning cut selection for mixed-integer linear programming via hierarchical sequence model
Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which
formulate a wide range of important real-world applications. Cut selection--which aims to …
formulate a wide range of important real-world applications. Cut selection--which aims to …
De novo molecular generation via connection-aware motif mining
De novo molecular generation is an essential task for science discovery. Recently, fragment-
based deep generative models have attracted much research attention due to their flexibility …
based deep generative models have attracted much research attention due to their flexibility …
State sequences prediction via fourier transform for representation learning
While deep reinforcement learning (RL) has been demonstrated effective in solving complex
control tasks, sample efficiency remains a key challenge due to the large amounts of data …
control tasks, sample efficiency remains a key challenge due to the large amounts of data …
Reinforcement Learning within Tree Search for Fast Macro Placement
Macro placement is a crucial step in modern chip design, and reinforcement learning (RL)
has recently emerged as a promising technique for improving the placement quality …
has recently emerged as a promising technique for improving the placement quality …
Learning to stop cut generation for efficient mixed-integer linear programming
Cutting planes (cuts) play an important role in solving mixed-integer linear programs
(MILPs), as they significantly tighten the dual bounds and improve the solving performance …
(MILPs), as they significantly tighten the dual bounds and improve the solving performance …
Himacmic: Hierarchical multi-agent deep reinforcement learning with dynamic asynchronous macro strategy
Multi-agent deep reinforcement learning (MADRL) has been widely used in many scenarios
such as robotics and game AI. However, existing methods mainly focus on the optimization …
such as robotics and game AI. However, existing methods mainly focus on the optimization …
Road planning for slums via deep reinforcement learning
Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate
road infrastructure within slums, and road planning for slums is critical to the sustainable …
road infrastructure within slums, and road planning for slums is critical to the sustainable …
Learning robust representation for reinforcement learning with distractions by reward sequence prediction
Reinforcement learning algorithms have achieved remarkable success in acquiring
behavioral skills directly from pixel inputs. However, their application in real-world scenarios …
behavioral skills directly from pixel inputs. However, their application in real-world scenarios …