Offline reinforcement learning: Tutorial, review, and perspectives on open problems
In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …
started on research on offline reinforcement learning algorithms: reinforcement learning …
Path planning and obstacle avoidance for AUV: A review
C Cheng, Q Sha, B He, G Li - Ocean Engineering, 2021 - Elsevier
Autonomous underwater vehicle plays a more and more important role in the exploration of
marine resources. Path planning and obstacle avoidance is the core technology to realize …
marine resources. Path planning and obstacle avoidance is the core technology to realize …
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 …
Mastering atari, go, chess and shogi by planning with a learned model
Constructing agents with planning capabilities has long been one of the main challenges in
the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge …
the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge …
[引用][C] An introduction to variational autoencoders
An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
When to trust your model: Model-based policy optimization
Designing effective model-based reinforcement learning algorithms is difficult because the
ease of data generation must be weighed against the bias of model-generated data. In this …
ease of data generation must be weighed against the bias of model-generated data. In this …
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 …
Soft actor-critic algorithms and applications
Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a
range of challenging sequential decision making and control tasks. However, these methods …
range of challenging sequential decision making and control tasks. However, these methods …
Model-based reinforcement learning for atari
Model-free reinforcement learning (RL) can be used to learn effective policies for complex
tasks, such as Atari games, even from image observations. However, this typically requires …
tasks, such as Atari games, even from image observations. However, this typically requires …