For effective decision support in scenarios with conflicting objectives,...
Partially Observable Markov Decision Processes (POMDPs) are useful tools...
Multi-objective reinforcement learning (MORL) algorithms tackle sequenti...
A classic model to study strategic decision making in multi-agent system...
In many risk-aware and multi-objective reinforcement learning settings, ...
Obstacles on the sidewalk often block the path, limiting passage and
res...
Many real-world problems contain multiple objectives and agents, where a...
Infectious disease outbreaks can have a disruptive impact on public heal...
Multi-agent reinforcement learning (MARL) enables us to create adaptive
...
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...
In many real-world scenarios, the utility of a user is derived from the
...
Real-world decision-making tasks are generally complex, requiring trade-...
In many risk-aware and multi-objective reinforcement learning settings, ...
Wind farms are a crucial driver toward the generation of ecological and
...
Many real-world multi-agent interactions consider multiple distinct crit...
Not all generate-and-test search algorithms are created equal. Bayesian
...
We generalize the well-studied problem of gait learning in modular robot...
In multi-objective multi-agent systems (MOMAS), agents explicitly consid...
We present a new model-based reinforcement learning algorithm, Cooperati...
Multi-agent coordination is prevalent in many real-world applications.
H...
Multi-agent coordination is prevalent in many real-world applications.
H...
The majority of multi-agent system (MAS) implementations aim to optimise...
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...
Many real-world reinforcement learning problems have a hierarchical natu...
We propose Deep Optimistic Linear Support Learning (DOL) to solve
high-d...
Zero-sum stochastic games provide a rich model for competitive decision
...
In cooperative multi-agent sequential decision making under uncertainty,...