Abstract text summarization:

It involves generating a concise and coherent summary of a long document or collection of documents. Various machine learning approaches, such as supervised learning and reinforcement learning are used in order, to develop algorithms that can accurately summarize long texts.

Incorporating human feedback into RL algorithms:

RL algorithms can learn from experience, but they do not have the ability to incorporate human knowledge or feedback. Researchers are exploring ways to incorporate human feedback into RL algorithms to improve their performance.

Developing robust RL algorithms for real world scenarios:

The creation of RL algorithms that are reliable and secure to use in real-world settings is another topic of research. This includes exploring methods for addressing issues such as exploration-exploitation trade-offs, reward shaping, and handling of off-policy data.