Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Balancing exploration and conservatism in the constrained setting is an important problem if
we are to use reinforcement learning for meaningful tasks in the real world. In this paper, we …

In the last few decades we have witnessed a significant development in Artificial Intelligence
(AI) thanks to the availability of a variety of testbeds, mostly based on simulated …

Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained
capabilities, as recently showcased by the successful applications of foundation models …

Cooperative multiagent reinforcement learning (MARL) has attracted significant attention
and has the potential for many real-world applications. Previous arts mainly focus on …

Lifelong reinforcement learning (RL) has been developed as a paradigm for extending
single-task RL to more realistic, dynamic settings. In lifelong RL, the" life" of an RL agent is …

A longstanding goal of artificial general intelligence is highly capable generalists that can
learn from diverse experiences and generalize to unseen tasks. The language and vision …

Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained
capabilities, as recently showcased by the successful applications of foundation models …

Modern machine learning models, such as neural networks which are the focus of this
thesis, have been shown to be extremely powerful tools for learning function mappings from …

Multi-objective reinforcement learning (MORL) approaches address real-world problems
with multiple objectives by learning policies maximizing returns weighted by different user …