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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 …
Autonomous unmanned aerial vehicle navigation using reinforcement learning: A systematic review
There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones,
in different applications such as packages delivery, traffic monitoring, search and rescue …
in different applications such as packages delivery, traffic monitoring, search and rescue …
Mastering diverse domains through world models
D Hafner, J Pasukonis, J Ba, T Lillicrap - ar** 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 …
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 …
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 …
Federated reinforcement learning: Techniques, applications, and open challenges
This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL),
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …