Sequential decision-making problems with multiple objectives arise naturally in practice and
pose unique challenges for research in decision-theoretic planning and learning, which has …

Reinforcement learning (RL) is a powerful paradigm for sequential decision-making under
uncertainties, and most RL algorithms aim to maximize some numerical value which …

Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned
on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further …

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

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 …

Many real-world problems involve the optimization of multiple, possibly conflicting
objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard …

While a number of algorithms for multiobjective reinforcement learning have been proposed,
and a small number of applications developed, there has been very little rigorous empirical …

As the capabilities of artificial intelligence (AI) systems improve, it becomes important to
constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of …

This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL)
framework based on deep Q-networks. We develop a high-performance MODRL framework …

Caches ideally should have low miss rates and short access times, and should be power
efficient at the same time. Such design goals are often contradictory in practice. Recent …