Communication plays a vital role in multi-agent systems, fostering
colla...
For effective decision support in scenarios with conflicting objectives,...
Experts advising decision-makers are likely to display expertise which v...
Safe reinforcement learning (RL) with hard constraint guarantees is a
pr...
Although deep reinforcement learning (DRL) has many success stories, the...
Partially Observable Markov Decision Processes (POMDPs) are useful tools...
Individual-based epidemiological models support the study of fine-graine...
Many instances of similar or almost-identical industrial machines or too...
Multi-objective reinforcement learning (MORL) algorithms tackle sequenti...
A classic model to study strategic decision making in multi-agent system...
Reinforcement learning (RL) is a promising optimal control technique for...
In multi-objective optimization, learning all the policies that reach
Pa...
Infectious disease outbreaks can have a disruptive impact on public heal...
Multi-agent reinforcement learning (MARL) enables us to create adaptive
...
We consider the challenge of policy simplification and verification in t...
We provide an in-depth study of Nash equilibria in multi-objective norma...
We study the problem of multiple agents learning concurrently in a
multi...
Quite some real-world problems can be formulated as decision-making prob...
Today's advanced Reinforcement Learning algorithms produce black-box
pol...
Model-predictive-control (MPC) offers an optimal control technique to
es...
Real-world decision-making tasks are generally complex, requiring trade-...
Many real-world multi-agent interactions consider multiple distinct crit...
Epidemics of infectious diseases are an important threat to public healt...
In the context of some machine learning applications, obtaining data
ins...
In multi-objective multi-agent systems (MOMAS), agents explicitly consid...
We present a new model-based reinforcement learning algorithm, Cooperati...
In many settings, as for example wind farms, multiple machines are
insta...
Multi-agent coordination is prevalent in many real-world applications.
H...
Multi-agent coordination is prevalent in many real-world applications.
H...
Over the last decade, the demand for better segmentation and classificat...
The majority of multi-agent system (MAS) implementations aim to optimise...
For a robot to learn a good policy, it often requires expensive equipmen...
Value-based reinforcement-learning algorithms are currently state-of-the...
Actor-critic algorithms learn an explicit policy (actor), and an accompa...
Many real-world decision problems are characterized by multiple objectiv...
Many currently deployed Reinforcement Learning agents work in an environ...
In multi-objective decision planning and learning, much attention is pai...
Pandemic influenza has the epidemic potential to kill millions of people...
A temporally abstract action, or an option, is specified by a policy and...
Many real-world reinforcement learning problems have a hierarchical natu...
Congestion problems are omnipresent in today's complex networks and repr...
Since the introduction of the stable marriage problem (SMP) by Gale and
...
Potential-based reward shaping (PBRS) is an effective and popular techni...
Recent advances of gradient temporal-difference methods allow to learn
o...
The Stable Marriage Problem (SMP) is a well-known matching problem first...