[HTML][HTML] GOPS: A general optimal control problem solver for autonomous driving and industrial control applications
Solving optimal control problems serves as the basic demand of industrial control tasks.
Existing methods like model predictive control often suffer from heavy online computational …
Existing methods like model predictive control often suffer from heavy online computational …
End-to-end autonomous driving through dueling double deep Q-network
Recent years have seen the rapid development of autonomous driving systems, which are
typically designed in a hierarchical architecture or an end-to-end architecture. The …
typically designed in a hierarchical architecture or an end-to-end architecture. The …
Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information
The combination of electrification and advanced dedicated hybrid engines (DHEs) has been
attached with promising prospects for the abatement of fuel consumption and emissions. To …
attached with promising prospects for the abatement of fuel consumption and emissions. To …
Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
A reinforcement learning benchmark for autonomous driving in general urban scenarios
Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …
decision and control in autonomous driving. However, current approaches have yet to …
A hybrid stock market prediction model based on GNG and reinforcement learning
Y Wu, Z Fu, X Liu, Y Bing - Expert Systems with Applications, 2023 - Elsevier
The stock market is a dynamic, complex, and chaotic environment, which makes predictions
for the stock market difficult. Many prediction methods are applied to the stock market, but …
for the stock market difficult. Many prediction methods are applied to the stock market, but …
DSAC-T: Distributional soft actor-critic with three refinements
Reinforcement learning (RL) has proven to be highly effective in tackling complex decision-
making and control tasks. However, prevalent model-free RL methods often face severe …
making and control tasks. However, prevalent model-free RL methods often face severe …
Mixed policy gradient
Reinforcement learning (RL) has great potential in sequential decision-making. At present,
the mainstream RL algorithms are data-driven, relying on millions of iterations and a large …
the mainstream RL algorithms are data-driven, relying on millions of iterations and a large …
Intuitive Fine-Tuning: Towards Unifying SFT and RLHF into a Single Process
Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF)
are two fundamental processes for enhancing the capabilities of Language Models (LMs) …
are two fundamental processes for enhancing the capabilities of Language Models (LMs) …
On the stability of datatic control systems
The development of feedback controllers is undergoing a paradigm shift from $\textit
{modelic} $(model-driven) control to $\textit {datatic} $(data-driven) control. Stability, as a …
{modelic} $(model-driven) control to $\textit {datatic} $(data-driven) control. Stability, as a …