A survey on computation offloading in edge systems: From the perspective of deep reinforcement learning approaches
Driven by the demand of time-sensitive and data-intensive applications, edge computing
has attracted wide attention as one of the cornerstones of modern service architectures. An …
has attracted wide attention as one of the cornerstones of modern service architectures. An …
State-wise safe reinforcement learning: A survey
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation
environments, applying RL to real-world applications still faces many challenges. A major …
environments, applying RL to real-world applications still faces many challenges. A major …
A survey of safety and trustworthiness of large language models through the lens of verification and validation
Large language models (LLMs) have exploded a new heatwave of AI for their ability to
engage end-users in human-level conversations with detailed and articulate answers across …
engage end-users in human-level conversations with detailed and articulate answers across …
Toward general-purpose robots via foundation models: A survey and meta-analysis
Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …
Constrained decision transformer for offline safe reinforcement learning
Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the
environment. We aim to tackle a more challenging problem: learning a safe policy from an …
environment. We aim to tackle a more challenging problem: learning a safe policy from an …
Safety gymnasium: A unified safe reinforcement learning benchmark
Artificial intelligence (AI) systems possess significant potential to drive societal progress.
However, their deployment often faces obstacles due to substantial safety concerns. Safe …
However, their deployment often faces obstacles due to substantial safety concerns. Safe …
Safe multi-agent reinforcement learning for multi-robot control
A challenging problem in robotics is how to control multiple robots cooperatively and safely
in real-world applications. Yet, developing multi-robot control methods from the perspective …
in real-world applications. Yet, developing multi-robot control methods from the perspective …
Diffusion models for reinforcement learning: A survey
Diffusion models surpass previous generative models in sample quality and training
stability. Recent works have shown the advantages of diffusion models in improving …
stability. Recent works have shown the advantages of diffusion models in improving …
Trustworthy reinforcement learning against intrinsic vulnerabilities: Robustness, safety, and generalizability
A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …
Iterative reachability estimation for safe reinforcement learning
Ensuring safety is important for the practical deployment of reinforcement learning (RL).
Various challenges must be addressed, such as handling stochasticity in the environments …
Various challenges must be addressed, such as handling stochasticity in the environments …