Real-world sequential decision-making tasks are generally complex, requiring trade-offs
between multiple, often conflicting, objectives. Despite this, the majority of research in …

Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision
problems where agents may have different preferences over (possibly conflicting) reward …

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user
is derived from the single execution of a policy. In these settings, making decisions based on …

In many risk-aware and multi-objective reinforcement learning settings, the utility of the user
is derived from a single execution of a policy. In these settings, making decisions based on …

In many real-world scenarios, the utility of a user is derived from a single execution of a
policy. In this case, to apply multi-objective reinforcement learning, the expected utility of the …

In sequential multi-objective decision making (MODeM) settings, when the utility of a user is
derived from a single execution of a policy, policies for the expected scalarised returns …

Many real-world problems contain multiple objectives and agents, where a trade-off exists
between objectives. Key to solving such problems is to exploit sparse dependency …

In sequential multi-objective decision making (MODeM) settings, when the utility of a user is
derived from a single execution of a policy, policies for the expected scalarised returns …

If widespread deployment of AI systems is to be accepted by society in the future, it is crucial
that such systems are trustworthy. Trustworthiness for autonomous systems has a number of …

Many real world reinforcement learning (RL) problems consist of multiple conflicting
objective functions that need to be optimized simultaneously. Finding these optimal policies …