Conservative Q-Learning: An Algorithmic Framework for Addressing Value Overestimation in Offline Reinforcement Learning

A key challenge in applying reinforcement learning to the real world is directly utilizing pre-collected datasets without interacting with the environment. However, standard off-policy reinforcement learning methods often suffer from value overestimation due to distribution shift between the dataset and the learned policy, particularly when handling complex and multimodal data distributions. To address this issue, researchers from the University of California, Berkeley, and Google Research proposed the "Conservative Q-Learning" (CQL) algorithm. The core idea of this algorithm is to learn a conservative Q-function such that the expected value of the policy under this Q-function is lower than its true value, thereby preventing overly optimistic estimates caused by out-of-distribution actions.

CQL achieves this goal by adding a simple Q-value regularization term to the standard Bellman error objective. This method not only theoretically proves to produce a lower bound on the current policy's value but can also be integrated into policy learning processes with theoretical improvement guarantees. In practical applications, CQL requires adding fewer than 20 lines of code to existing deep Q-learning and actor-critic implementations. Experiments show that in both discrete and continuous control domains, CQL significantly outperforms existing offline reinforcement learning methods, typically improving final returns by 2 to 5 times, with particularly outstanding performance when learning from complex and multimodal data distributions. For researchers and engineers looking to learn effective policies from static data without interacting with the environment, the paper "Conservative Q-Learning for Offline Reinforcement Learning," published in 2020, provides a robust and easy-to-implement solution.