Integrating machine learning and model predictive control for automotive applications: A review and future directions
In this review paper, the integration of Machine Learning (ML) and Model Predictive Control
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …
Reinforcement learning with guarantees: a review
Reinforcement learning is concerned with a generic concept of an agent acting in an
environment. From the control theory standpoint, reinforcement learning may be considered …
environment. From the control theory standpoint, reinforcement learning may be considered …
Active observing in continuous-time control
The control of continuous-time environments while actively deciding when to take costly
observations in time is a crucial yet unexplored problem, particularly relevant to real-world …
observations in time is a crucial yet unexplored problem, particularly relevant to real-world …
Where to go next: Learning a subgoal recommendation policy for navigation in dynamic environments
Robotic navigation in environments shared with other robots or humans remains
challenging because the intentions of the surrounding agents are not directly observable …
challenging because the intentions of the surrounding agents are not directly observable …
Heuristic-guided reinforcement learning
We provide a framework to accelerate reinforcement learning (RL) algorithms by heuristics
that are constructed by domain knowledge or offline data. Tabula rasa RL algorithms require …
that are constructed by domain knowledge or offline data. Tabula rasa RL algorithms require …
Model predictive control for micro aerial vehicles: A survey
This paper presents a review of the design and application of model predictive control
strategies for Micro Aerial Vehicles and specifically multirotor configurations such as …
strategies for Micro Aerial Vehicles and specifically multirotor configurations such as …
Lyapunov-stable neural-network control
Deep learning has had a far reaching impact in robotics. Specifically, deep reinforcement
learning algorithms have been highly effective in synthesizing neural-network controllers for …
learning algorithms have been highly effective in synthesizing neural-network controllers for …
Event-triggered model predictive control with deep reinforcement learning for autonomous driving
Event-triggered model predictive control (eMPC) is a popular optimal control method with an
aim to alleviate the computation and/or communication burden of MPC. However, it …
aim to alleviate the computation and/or communication burden of MPC. However, it …
Beyond black-box advice: learning-augmented algorithms for MDPs with Q-value predictions
We study the tradeoff between consistency and robustness in the context of a single-
trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned …
trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned …
Model predictive actor-critic: Accelerating robot skill acquisition with deep reinforcement learning
Substantial advancements to model-based reinforcement learning algorithms have been
impeded by the model-bias induced by the collected data, which generally hurts …
impeded by the model-bias induced by the collected data, which generally hurts …