Efficient Offline Reinforcement Learning via Sample Filtering

This research explores a sample-efficient approach to offline deep reinforcement learning using policy constraints and sample filtering. The work likely addresses the challenge of limited data availability in offline RL settings, offering a potential improvement in training performance.

• •Focuses on offline deep reinforcement learning.
• •Employs sample filtering to improve efficiency.
• •Uses policy constraints for enhanced learning.