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 …
Mastering atari with discrete world models
Intelligent agents need to generalize from past experience to achieve goals in complex
environments. World models facilitate such generalization and allow learning behaviors …
environments. World models facilitate such generalization and allow learning behaviors …
Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
In this work we aim to solve a large collection of tasks using a single reinforcement learning
agent with a single set of parameters. A key challenge is to handle the increased amount of …
agent with a single set of parameters. A key challenge is to handle the increased amount of …
From predicting to decision making: Reinforcement learning in biomedicine
X Liu, J Zhang, Z Hou, YI Yang… - Wiley Interdisciplinary …, 2024 - Wiley Online Library
Reinforcement learning (RL) is one important branch of artificial intelligence (AI), which
intuitively imitates the learning style of human beings. It is commonly derived from solving …
intuitively imitates the learning style of human beings. It is commonly derived from solving …
Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments
Rescue robots can be used in urban search and rescue (USAR) applications to perform the
important task of exploring unknown cluttered environments. Due to the unpredictable …
important task of exploring unknown cluttered environments. Due to the unpredictable …
Dopamine: A research framework for deep reinforcement learning
Deep reinforcement learning (deep RL) research has grown significantly in recent years. A
number of software offerings now exist that provide stable, comprehensive implementations …
number of software offerings now exist that provide stable, comprehensive implementations …
Path planning for UAV ground target tracking via deep reinforcement learning
B Li, Y Wu - IEEE access, 2020 - ieeexplore.ieee.org
In this paper, we focus on the study of UAV ground target tracking under obstacle
environments using deep reinforcement learning, and an improved deep deterministic policy …
environments using deep reinforcement learning, and an improved deep deterministic policy …
Muesli: Combining improvements in policy optimization
We propose a novel policy update that combines regularized policy optimization with model
learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the …
learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the …
Resource management in wireless networks via multi-agent deep reinforcement learning
We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …