A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Blending MPC Value Function Approximation for Efficient Reinforcement Learning
[article]
2021
arXiv
pre-print
Preserved Fulltext
Other Versions
We present a framework for improving on MPC with model-free reinforcement learning (RL). The key insight is to view MPC as constructing a series of local Q-function approximations. ...
We show that by using a parameter λ, similar to the trace decay parameter in TD(λ), we can systematically trade-off learned value estimates against the local Q-function approximations. ...
MITIGATING BIAS IN MPC VIA REINFORCEMENT LEARNING In this section, we develop our approach to systematically deal with model bias in MPC by blending-in learned value estimates. ...
arXiv:2012.05909v2
fatcat:jbd52k2g65ahdotaohtmom3rrm
Combining Reinforcement Learning with Model Predictive Control for On-Ramp Merging
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2011.08484v2 fatcat:fchltobjxjhijnfbmatdj6ux7qOther Versions
Two broad classes of techniques have been proposed to solve motion planning problems in autonomous driving: Model Predictive Control (MPC) and Reinforcement Learning (RL). ...
We subsequently present an algorithm which blends the model-free RL agent with the MPC solution and show that it provides better trade-offs between all metrics -- passenger comfort, efficiency, crash rate ...
) and Reinforcement Learning (RL), perform for this problem. ...
arXiv:2011.08484v3
fatcat:rq573i743rg7blmsykdzfnibky
Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies
2019
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. ...
This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future. ...
Reinforcement learning, as a new research hotspot, uses neural networks to approximate the Q function. ...
doi:10.3390/app9102026
fatcat:tbkqyk26grh55e3yjxl4jag3ny
Tailored neural networks for learning optimal value functions in MPC
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
However, efficiently learning the optimal control policy, the optimal value function, or the Q-function requires suitable function approximators. ...
In this paper, we provide a similar result for representing the optimal value function and the Q-function that are both known to be piecewise quadratic for linear MPC. ...
Blending MPC and
−1 +0 −1 +1 +0 −1 value function approximation for efficient reinforcement learning.
+0 −1 +0 −1 −1 ...
arXiv:2112.03975v1
fatcat:wexhgbh2kvcjvitbqfwiadvbnq
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2108.06266v1 fatcat:szah35r4tbf6vdnatxfeewwt6a
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. ...
control and reinforcement learning approaches. ...
Learning Backward Value Functions. ...
arXiv:2108.06266v2
fatcat:gbbe3qyatfgelgzhqzglecr5qm
A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms
2022
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed. ...
The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. ...
Acknowledgments: The authors would like to thank all anonymous reviewers and editors for their helpful suggestions for the improvement of this paper. ...
doi:10.3390/app12041995
fatcat:hdy753pdbfhjvihdopaw6lip2u
Hierarchical Evasive Path Planning using Reinforcement Learning and Model Predictive Control
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This paper presents a solution based on Reinforcement Learning for a point-free avoidance path generation for the emergency double-lane change situation. ...
The agent achieves greater efficiency in higher speed cases with this solution. µ max = 0.0037e v0 0.0693 The reward function (10) subtracts the maximum values that occur during the route's execution ...
doi:10.1109/access.2020.3031037
fatcat:w6bemcaf3jdyjni6lio5koeqiu
Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?
2020
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
This work presents a systematic literature review (SLR) of the more recent works that developed EMSs for HEVs. ...
predictive control, RL-reinforcement learning). ...
Some interesting sub-categories of learning-based strategies are reinforcement learning (RL) and neural network learning (NNL). ...
doi:10.3390/app10238744
fatcat:jacrh6igaffm3etle65xbs6pnm
Evaluating model-based planning and planner amortization for continuous control
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning; the learned policy serves as a proposal for MPC. ...
We find that well-tuned model-free agents are strong baselines even for high DoF control problems but MPC with learned proposals and models (trained on the fly or transferred from related tasks) can significantly ...
Other recently proposed algorithmic innovations blend MPC with learned value estimates to trade off model and value errors (Bhardwaj et al., 2021) . ...
arXiv:2110.03363v1
fatcat:do6vywo47jdf3jzcjabw5e6ngm
Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies
2019
Renewable & Sustainable Energy Reviews
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Achieving an energy-efficient powertrain requires tackling several conflicting control objectives such as the drivability, fuel economy, reduced emissions, and battery state of charge preservation, which ...
Full-electric vehicle Energy management strategy optimisation Online EMS Offline EMS Optimal control strategy A B S T R A C T Hybrid and electric vehicles have been demonstrated as auspicious solutions for ...
We also acknowledge Flanders Make and VLAIO for the support of our research group. ...
doi:10.1016/j.rser.2019.109596
fatcat:ybks774km5htvb6f7foyetum7m
Reinforcement Learning Optimized Look-Ahead Energy Management of a Parallel Hybrid Electric Vehicle
2017
IEEE/ASME transactions on mechatronics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This paper presents a predictive energy management strategy for a parallel hybrid electric vehicle (HEV) based on velocity prediction and reinforcement learning (RL). ...
The design procedure starts with modeling the parallel HEV as a systematic control-oriented model and defining a cost function. ...
VELOCITY PREDICTION AND REINFORCEMENT LEARNING
A. ...
doi:10.1109/tmech.2017.2707338
fatcat:yv6bliu6orenjfedacino7fesa
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
While Reinforcement Learning (RL) can be used to compute optimal policies with little prior knowledge about the environment, it suffers from slow convergence. ...
Our method uses scalable approximate inference to learn and updates probabilistic models in an online incremental fashion while also computing optimal control policies via successive local approximations ...
) a second-order local approximation of the value function. ...
arXiv:1608.06235v2
fatcat:hj4ghkf5djbwbcbd5ualtgw4v4
A Tour of Reinforcement Learning: The View from Continuous Control
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
This manuscript surveys reinforcement learning from the perspective of optimization and control with a focus on continuous control applications. ...
In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. ...
I'd like to thank Chris Wiggins for sharing his taxonomy on machine learning, Roy Frostig for shaping the Section 3.3, Pavel Pravdin for consulting on how to get policy gradient methods up and running, ...
arXiv:1806.09460v2
fatcat:4ago566hwzcudj3mcbb3j5wyr4
Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends
2020
IEEE Open Journal of Vehicular Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Index Terms-data-driven methods, electric vehicles, intelligent energy management strategy, reinforcement learning, powertrain architecture. ...
By increasing the electrified vehicles trend, it is crucial to have a survey of what is done before and what is the open challenge for improving this industry. ...
DRL employs deep neural network (DNN) in order to express state value function V (S t ), action value function Q(S t , A t ), and policy function π(S) with function approximation instead of tabular approach ...
doi:10.1109/ojvt.2020.3018146
fatcat:7pqrnf52nze5fhpuyyhz4mjvtm
Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In order to improve the data efficiency, we propose visual TORCS (VTORCS), a deep reinforcement learning environment which is based on the open racing car simulator (TORCS). ...
The trained reinforcement learning controller outperforms the linear quadratic regulator (LQR) controller and model predictive control (MPC) controller on different tracks. ...
The critic Q µ (s t , a t ; w Q 2 ) where w Q 2 are the network weights approximates the optimal action-value function. ...
arXiv:1810.12778v1
fatcat:2yz3olk2v5g6tpxb2u67tozhpy
« Previous
Showing results 1 — 15 out of 365 results