Summary of Acl-ql: Adaptive Conservative Level in Q-learning For Offline Reinforcement Learning, by Kun Wu et al.
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning
by Kun Wu, Yinuo Zhao, Zhiyuan Xu, Zhengping Che, Chengxiang Yin, Chi Harold Liu, Feiferi Feng, Jian Tang
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, Adaptive Conservative Level in Q-Learning (ACL-QL), addresses two challenges in Offline Reinforcement Learning. The first challenge is mitigating the problem of Q-value overestimation, which typically leads to overly conservative policies. The second challenge is optimizing each sample with fixed constraints, resulting in a lack of fine-grained control over the conservative level. ACL-QL limits the Q-values within a mild range and enables adaptive control over each state-action pair, lifting Q-values for good transitions and reducing them for bad transitions. This framework is theoretically analyzed to determine the conditions under which the learned Q-function’s conservative level can be limited and optimized adaptively. The algorithm uses two learnable weight functions to control the conservative level, with monotonicity loss and surrogate losses training the weights, Q-function, and policy network alternatively. ACL-QL achieves state-of-the-art performance on the D4RL benchmark and outperforms existing offline DRL baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline Reinforcement Learning is a way to learn good control policies without needing to interact with the environment every time. Right now, most methods are too cautious because they try to avoid making mistakes. But this can also mean they miss opportunities. The new framework, Adaptive Conservative Level in Q-Learning (ACL-QL), helps solve these two problems at once. It makes sure the control policy is not too cautious and gives more freedom to make good choices when things go well. |
Keywords
» Artificial intelligence » Reinforcement learning
