- Offline Reinforcement Learning
Offline-RL learns policy by utilizing only the experience it gathers without additional interaction with the environment. There are still some challenges, such as distribution shifts and out-of-distribution problem.

- Domain Adaptation/Imitation Learning
Imitation learning is a branch of research aiming to apply reinforcement learning to real-life scenarios, focusing on learning policies that mimic the actions of experts. Recently, there has been progress in research on Domain Adaptation and Cross-Domain studies, enabling imitation of actions from experts in different domains,

- Multi-Agent Reinforcement Learning
In multi-agent RL, multiple agents aim to learn policies that would maximize the expected return from a shared environment. Coordination among the agents is essential for achieving this goal as the agents effect themselves as learning progresses.