Evolutionary Algorithms and Deep Reinforcement Learning have both
succes...
For effective decision support in scenarios with conflicting objectives,...
Many decision-making problems feature multiple objectives. In such probl...
In many risk-aware and multi-objective reinforcement learning settings, ...
Many real-world problems contain multiple objectives and agents, where a...
Infectious disease outbreaks can have a disruptive impact on public heal...
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, ...
When developing reinforcement learning agents, the standard approach is ...
Many real-world multi-agent interactions consider multiple distinct crit...
With the development of deep representation learning, the domain of
rein...
In multi-objective multi-agent systems (MOMAS), agents explicitly consid...
The majority of multi-agent system (MAS) implementations aim to optimise...
Correctly identifying vulnerable road users (VRUs), e.g. cyclists and
pe...
Deep Reinforcement Learning (DRL) has become increasingly powerful in re...