Federated reinforcement learning in IoT: applications, opportunities and open challenges
The internet of things (IoT) represents a disruptive concept that has been changing society in
several ways. There have been several successful applications of IoT in the industry. For …
several ways. There have been several successful applications of IoT in the industry. For …
Reincarnating reinforcement learning: Reusing prior computation to accelerate progress
Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in
reinforcement learning (RL) research. However, RL systems, when applied to large-scale …
reinforcement learning (RL) research. However, RL systems, when applied to large-scale …
{MSRL}: Distributed Reinforcement Learning with Dataflow Fragments
A wide range of reinforcement learning (RL) algorithms have been proposed, in which
agents learn from interactions with a simulated environment. Executing such RL training …
agents learn from interactions with a simulated environment. Executing such RL training …
Towards designing a generic and comprehensive deep reinforcement learning framework
Reinforcement learning (RL) has emerged as an effective approach for building an
intelligent system, which involves multiple self-operated agents to collectively accomplish a …
intelligent system, which involves multiple self-operated agents to collectively accomplish a …
Benchmarking real-time reinforcement learning
P Thodoroff, W Li, ND Lawrence - NeurIPS 2021 Workshop …, 2022 - proceedings.mlr.press
Decision-making algorithms can require fast response time in applications as diverse as self-
driving cars and minimizing load times of webpages. Yet, modern algorithms (deep …
driving cars and minimizing load times of webpages. Yet, modern algorithms (deep …
Guided deep reinforcement learning in the geofriends2 environment
In recent years, the artificial intelligence community has taken big strides in the application of
reinforcement learning to games or similar environments using deep learning. From Atari to …
reinforcement learning to games or similar environments using deep learning. From Atari to …
Application of reinforcement learning to wireless sensor networks: models and algorithms
Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which
are used to monitor events or environmental parameters, such as movement, temperature …
are used to monitor events or environmental parameters, such as movement, temperature …
Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the
central network, incorporating edge nodes close to end devices. This expansion facilitates …
central network, incorporating edge nodes close to end devices. This expansion facilitates …
Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues
In wireless networks, context awareness and intelligence are capabilities that enable each
host to observe, learn, and respond to its complex and dynamic operating environment in an …
host to observe, learn, and respond to its complex and dynamic operating environment in an …
[图书][B] Applied Reinforcement Learning with Python: With OpenAI Gym, Tensorflow, and Keras
T Beysolow II - 2019 - books.google.com
Delve into the world of reinforcement learning algorithms and apply them to different use-
cases via Python. This book covers important topics such as policy gradients and Q learning …
cases via Python. This book covers important topics such as policy gradients and Q learning …