Deep reinforcement learning based mobile robot navigation: A review
K Zhu, T Zhang - Tsinghua Science and Technology, 2021 - ieeexplore.ieee.org
Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement
Learning (DRL) has received significant attention because of its strong representation and …
Learning (DRL) has received significant attention because of its strong representation and …
Deep reinforcement learning: A brief survey
K Arulkumaran, MP Deisenroth… - IEEE Signal …, 2017 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …
(AI) and represents a step toward building autonomous systems with a higher-level …
Memory-based model editing at scale
Even the largest neural networks make errors, and once-correct predictions can become
invalid as the world changes. Model editors make local updates to the behavior of base (pre …
invalid as the world changes. Model editors make local updates to the behavior of base (pre …
Video pretraining (vpt): Learning to act by watching unlabeled online videos
Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for
training models with broad, general capabilities for text, images, and other modalities …
training models with broad, general capabilities for text, images, and other modalities …
Offline reinforcement learning as one big sequence modeling problem
Reinforcement learning (RL) is typically viewed as the problem of estimating single-step
policies (for model-free RL) or single-step models (for model-based RL), leveraging the …
policies (for model-free RL) or single-step models (for model-based RL), leveraging the …
Transfer learning in deep reinforcement learning: A survey
Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …
problems. Recent years have witnessed remarkable progress in reinforcement learning …
An introduction to deep reinforcement learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …
learning. This field of research has been able to solve a wide range of complex …
[图书][B] Synthetic data for deep learning
SI Nikolenko - 2021 - Springer
You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
anymore? Anyway, you are reading this, and it means that I have managed to release one of …
Actor-attention-critic for multi-agent reinforcement learning
Reinforcement learning in multi-agent scenarios is important for real-world applications but
presents challenges beyond those seen in single-agent settings. We present an actor-critic …
presents challenges beyond those seen in single-agent settings. We present an actor-critic …