Summary of Efficient Offline Reinforcement Learning: the Critic Is Critical, by Adam Jelley et al.
Efficient Offline Reinforcement Learning: The Critic is Critical
by Adam Jelley, Trevor McInroe, Sam Devlin, Amos Storkey
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a hybrid approach to offline reinforcement learning, combining the benefits of supervised and off-policy reinforcement learning methods. The authors observe that traditional off-policy approaches can be inefficient and unstable due to temporal difference bootstrapping, but by first pre-training with a supervised Monte-Carlo value-error, they demonstrate improved efficiency and stability on standard benchmarks. The proposed algorithms, TD3+BC+CQL and EDAC+BC, regularize both the actor and critic towards the behavior policy, leading to more reliable improvements from limited human demonstrations. The authors also release code at https://github.com/AdamJelley/EfficientOfflineRL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers learn better when they’re given instructions without being shown what to do first. Right now, some ways of teaching computers are slow and unreliable. The researchers found a way to speed up the process by using a combination of two approaches: one that teaches the computer based on examples, and another that helps it make good choices even if it’s not perfect. This new method works better than old ones and is more stable. It also helps computers learn from limited human guidance, which can be helpful when we only have a little data to work with. |
Keywords
» Artificial intelligence » Bootstrapping » Reinforcement learning » Supervised
