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 …

Building and power generation decarbonization present new challenges in electric grid
reliability as a result of renewable energy source intermittency and increase in grid load …

Recent research in offline reinforcement learning (RL) has demonstrated that return-
conditioned supervised learning is a powerful paradigm for decision-making problems …

Reinforcement learning (RL) has become a dominant decision-making paradigm and has
achieved notable success in many real-world applications. Notably, deep neural networks …

As a promising approach to managing distributed energy, the use of microgrids has attracted
significant attention among those managing continuous connections to distribution networks …

Deep reinforcement learning~(RL) has achieved remarkable successes in complex single-
task settings. However, designing RL agents that can learn multiple tasks and leverage prior …

In the field of unsupervised skill discovery (USD), a major challenge is limited exploration,
primarily due to substantial penalties when skills deviate from their initial trajectories. To …

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be
widely used in mobile networks, social computing, and recommendation systems. It aims at …

The novel technology based on reinforcement learning (RL) is considered as a promising
direction to achieve cognitive and even intelligent anti-jamming communication. However …

Most recent work in goal oriented visual navigation resorts to large-scale machine learning
in simulated environments. The main challenge lies in learning compact representations …