A practical guide to multi-objective reinforcement learning and planning
Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …
between multiple, often conflicting, objectives. Despite this, the majority of research in …
Scalar reward is not enough: A response to silver, singh, precup and sutton (2021)
The recent paper “Reward is Enough” by Silver, Singh, Precup and Sutton posits that the
concept of reward maximisation is sufficient to underpin all intelligence, both natural and …
concept of reward maximisation is sufficient to underpin all intelligence, both natural and …
Explainable reinforcement learning for broad-xai: a conceptual framework and survey
Broad-XAI moves away from interpreting individual decisions based on a single datum and
aims to provide integrated explanations from multiple machine learning algorithms into a …
aims to provide integrated explanations from multiple machine learning algorithms into a …
Self-organizing maps for storage and transfer of knowledge in reinforcement learning
T George Karimpanal, R Bouffanais - Adaptive Behavior, 2019 - journals.sagepub.com
The idea of reusing or transferring information from previously learned tasks (source tasks)
for the learning of new tasks (target tasks) has the potential to significantly improve the …
for the learning of new tasks (target tasks) has the potential to significantly improve the …
Multi-objective deep reinforcement learning for emergency scheduling in a water distribution network
In recent years, water contamination incidents have happened frequently, causing serious
losses and impacts on society. Therefore, how to quickly respond to emergency pollution …
losses and impacts on society. Therefore, how to quickly respond to emergency pollution …
Detection of threats to IoT devices using scalable VPN-forwarded honeypots
Attacks on Internet of Things (IoT) devices, exploiting inherent vulnerabilities, have
intensified over the last few years. Recent large-scale attacks, such as Persirai, Hakai, etc …
intensified over the last few years. Recent large-scale attacks, such as Persirai, Hakai, etc …
An application of multi-objective reinforcement learning for efficient model-free control of canals deployed with IoT networks
Canals have been widely constructed to deliver water from rich areas to poor areas to ease
water shortages. Efficient controlling of canals is essential for high-performance water …
water shortages. Efficient controlling of canals is essential for high-performance water …
Goal-conditioned offline reinforcement learning through state space partitioning
Offline reinforcement learning (RL) aims to create policies for sequential decision-making
using exclusively offline datasets. This presents a significant challenge, especially when …
using exclusively offline datasets. This presents a significant challenge, especially when …
Experience replay using transition sequences
TG Karimpanal, R Bouffanais - Frontiers in neurorobotics, 2018 - frontiersin.org
Experience replay is one of the most commonly used approaches to improve the sample
efficiency of reinforcement learning algorithms. In this work, we propose an approach to …
efficiency of reinforcement learning algorithms. In this work, we propose an approach to …
Scalable VPN-forwarded honeypots: Dataset and threat intelligence insights
After distributed denial-of-service attacks by the Mirai malware in 2016, large-scale attacks
exploiting IoT devices raise significant security concerns for the stakeholders involved. The …
exploiting IoT devices raise significant security concerns for the stakeholders involved. The …