A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization
from existing datasets followed by fast online fine-tuning with limited interaction. However …

Problems of cooperation--in which agents seek ways to jointly improve their welfare--are
ubiquitous and important. They can be found at scales ranging from our daily routines--such …

Games are abstractions of the real world, where artificial agents learn to compete and
cooperate with other agents. While significant achievements have been made in various …

Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess
generalization to novel situations as their primary objective (unlike supervised learning …

The success of reinforcement learning in a variety of challenging sequential decision-
making problems has been much discussed, but often ignored in this discussion is the …

In this survey, we review the recent advances in control design methods for robotic multi-
agent systems (MAS), focusing on learning-based methods with safety considerations. We …

Combinatorial optimization problems (COPs) are a class of NP-hard problems with great
practical significance. Traditional approaches for COPs suffer from high computational time …

Transformer has been considered the dominating neural architecture in NLP and CV, mostly
under supervised settings. Recently, a similar surge of using Transformers has appeared in …

While Deep Reinforcement Learning (DRL) has achieved outstanding performance in
extensive applications, exploiting its vulnerability with adversarial attacks is essential …

Measuring and promoting policy diversity is critical for solving games with strong non-
transitive dynamics where strategic cycles exist, and there is no consistent winner (eg, Rock …