Summary of The Virtues Of Pessimism in Inverse Reinforcement Learning, by David Wu and Gokul Swamy and J. Andrew Bagnell and Zhiwei Steven Wu and Sanjiban Choudhury
The Virtues of Pessimism in Inverse Reinforcement Learning
by David Wu, Gokul Swamy, J. Andrew Bagnell, Zhiwei Steven Wu, Sanjiban Choudhury
First submitted to arxiv on: 4 Feb 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 The paper proposes an alternative approach to speeding up Inverse Reinforcement Learning (IRL) by leveraging expert demonstrations in the inner-loop reinforcement learning (RL) problem. Instead of resetting the learner to expert states, the authors suggest using pessimism, or staying close to the expert’s data distribution, achieved via offline RL algorithms. The paper formalizes a connection between offline RL and IRL, enabling the use of any offline RL algorithm to improve the sample efficiency of IRL. Experimental results demonstrate a strong correlation between the efficacy of an offline RL algorithm and its performance as part of an IRL procedure. By using a strong offline RL algorithm in IRL, the authors show that policies can be found that match expert performance significantly more efficiently than prior art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us learn complex behaviors from experts by using demonstrations. Normally, this process is slow because it needs to solve another big problem. The researchers suggest a new way: instead of going back to expert states, they propose staying close to the expert’s data and use special offline learning algorithms. This helps make the process faster and more efficient. By combining these algorithms with expert demonstrations, we can find good policies much quicker than before. |
Keywords
* Artificial intelligence * Reinforcement learning
