Exploring Offline Reinforcement Learning RL: Offering Practical Advice for Domain-Specific Practitioners and Future Algorithm Development

Offline Reinforcement Learning (RL) offers practical and valuable solutions for control problems across various fields by converting offline datasets into policies. However, challenges arise from differences in state-action distributions between the dataset and the learned policy, leading to overestimation of values. To address these challenges, researchers have found that using behavior-regularized policy gradient methods such as DDPG+BC consistently performs better. Additionally, incorporating high-coverage datasets and test-time policy extraction techniques can improve the accuracy of the policy on new states, thus enhancing generalization in offline RL. Looking ahead, future research in offline RL will focus on extracting the best policy from the learned value function and training policies to generalize well on test-time states. For businesses, AI solutions offer practical opportunities for automation, KPI management, and AI sales bot implementations, redefining the way work is done. To explore these possibilities, free consultations are available through the AI Lab in Telegram @itinai and updates can be found on Twitter @itinaicom.