Offline Reinforcement Learning Developments

The field of reinforcement learning is moving towards offline learning, where agents learn from static datasets without interacting with the environment. This direction is driven by the need to learn robust policies from large-scale, real-world datasets, particularly in applications such as autonomous driving. Recent work has focused on developing algorithms that can balance conservatism and performance, preventing overestimation and improving the robustness of learned policies. Notable papers in this area include:

  • Mildly Conservative Regularized Q-learning, which proposes a framework for balancing conservatism and performance in offline reinforcement learning.
  • From Imitation to Optimization, which demonstrates the effectiveness of offline reinforcement learning in autonomous driving by achieving a 3.2x higher success rate and a 7.4x lower collision rate than the strongest behavioral cloning baseline.

Sources

Mildly Conservative Regularized Evaluation for Offline Reinforcement Learning

From Imitation to Optimization: A Comparative Study of Offline Learning for Autonomous Driving

Efficient Reward Identification In Max Entropy Reinforcement Learning with Sparsity and Rank Priors

A Tutorial: An Intuitive Explanation of Offline Reinforcement Learning Theory

Value Function Initialization for Knowledge Transfer and Jump-start in Deep Reinforcement Learning

Distilling Reinforcement Learning into Single-Batch Datasets

Built with on top of