Dual regularized policy updating and shiftpoint detection for automated deployment of reinforcement learning controllers on industrial mechatronic systems
V Vantilborgh, T Staessens, W De Groote… - Control Engineering …, 2024 - Elsevier
We propose an algorithmic pipeline enabling deep reinforcement learning controllers to
detect when a significant change in system characteristics has occurred and update the …
detect when a significant change in system characteristics has occurred and update the …
[HTML][HTML] Evaluating semi-cooperative Nash/Stackelberg Q-learning for traffic routes plan in a single intersection
As traffic congestion grows tremendous and frequent in the urban transportation system,
many efficient models with reinforcement learning (RL) methods have already been …
many efficient models with reinforcement learning (RL) methods have already been …
Q-learning with experience replay in a dynamic environment
M Pieters, MA Wiering - 2016 IEEE Symposium Series on …, 2016 - ieeexplore.ieee.org
Most research in reinforcement learning has focused on stationary environments. In this
paper, we propose several adaptations of Q-learning for a dynamic environment, for both …
paper, we propose several adaptations of Q-learning for a dynamic environment, for both …
Active perception in adversarial scenarios using maximum entropy deep reinforcement learning
We pose an active perception problem where an autonomous agent actively interacts with a
second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the …
second agent with potentially adversarial behaviors. Given the uncertainty in the intent of the …
A deep recurrent Q network towards self‐adapting distributed microservice architecture
B Magableh, M Almiani - Software: Practice and Experience, 2020 - Wiley Online Library
One desired aspect of microservice architecture is the ability to self‐adapt its own
architecture and behavior in response to changes in the operational environment. To …
architecture and behavior in response to changes in the operational environment. To …
Decentralized multi-agent actor-critic with generative inference
Recent multi-agent actor-critic methods have utilized centralized training with decentralized
execution to address the non-stationarity of co-adapting agents. This training paradigm …
execution to address the non-stationarity of co-adapting agents. This training paradigm …
Optimization of traffic signal control based on game theoretical framework
This paper presents a model of intelligent traffic signal control using a game theoretical
framework based on decision-making operations. This model aims at finding an optimal …
framework based on decision-making operations. This model aims at finding an optimal …
Optimization of traffic signal control with different game theoretical strategies
This paper presents a model of intelligent traffic signal control using a game theoretical
framework based on decision-making combinations. This model aims at finding an optimal …
framework based on decision-making combinations. This model aims at finding an optimal …
[PDF][PDF] Multi-agent traffic control using game theory and reinforcement learning
GUO Jian - 2022 - repozitorium.omikk.bme.hu
The intelligent transportation system has recently received more attention, which plays an
important role in easing traffic congestion. Regulating traffic flow effectively and efficiently is …
important role in easing traffic congestion. Regulating traffic flow effectively and efficiently is …
Private and Provably Efficient Federated Decision-Making
A Dubey - 2022 - dspace.mit.edu
In this thesis, we study sequential multi-armed bandit and reinforcement learning in the
federated setting, where a group of agents collaborates to improve their collective reward by …
federated setting, where a group of agents collaborates to improve their collective reward by …