A Review of Offline Reinforcement Learning Based on Representation Learning

However, the online data collection may raise issues of security, time, or cost, greatly limiting the practical applications of reinforcement learning. Meanwhile, tackling intricate high-dimensional data input problems has also become a significant challenge for reinforcement learning due to the intricate and multifaceted nature of raw data.

Fortunately, offline reinforcement learning based on representation learning can learn the policy only from historical experience data without interacting with the environment. It utilizes representation learning techniques to map the features of the offline dataset into low-dimensional vectors, which are subsequently employed to train the offline reinforcement learning model. This data-driven paradigm provides a new opportunity to realize the general artificial intelligence.