Deep reinforcement learning for cyber security
The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …
being exposed to cyberattacks more than ever. The complexity and dynamics of …
Reinforcement learning-based physical cross-layer security and privacy in 6G
Sixth-generation (6G) cellular systems will have an inherent vulnerability to physical (PHY)-
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …
Mastering diverse domains through world models
Developing a general algorithm that learns to solve tasks across a wide range of
applications has been a fundamental challenge in artificial intelligence. Although current …
applications has been a fundamental challenge in artificial intelligence. Although current …
A generalist agent
Inspired by progress in large-scale language modeling, we apply a similar approach
towards building a single generalist agent beyond the realm of text outputs. The agent …
towards building a single generalist agent beyond the realm of text outputs. The agent …
Mastering visual continuous control: Improved data-augmented reinforcement learning
We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual
continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data …
continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data …
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
We propose a simple data augmentation technique that can be applied to standard model-
free reinforcement learning algorithms, enabling robust learning directly from pixels without …
free reinforcement learning algorithms, enabling robust learning directly from pixels without …
Solving rubik's cube with a robot hand
We demonstrate that models trained only in simulation can be used to solve a manipulation
problem of unprecedented complexity on a real robot. This is made possible by two key …
problem of unprecedented complexity on a real robot. This is made possible by two key …
Critic regularized regression
Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy
optimization from large pre-recorded datasets without online environment interaction. It …
optimization from large pre-recorded datasets without online environment interaction. It …
Learning latent dynamics for planning from pixels
Planning has been very successful for control tasks with known environment dynamics. To
leverage planning in unknown environments, the agent needs to learn the dynamics from …
leverage planning in unknown environments, the agent needs to learn the dynamics from …
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
We propose a simple data augmentation technique that can be applied to standard model-
free reinforcement learning algorithms, enabling robust learning directly from pixels without …
free reinforcement learning algorithms, enabling robust learning directly from pixels without …