Summary of Convergence Of a Model-free Entropy-regularized Inverse Reinforcement Learning Algorithm, by Titouan Renard et al.
Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm
by Titouan Renard, Andreas Schlaginhaufen, Tingting Ni, Maryam Kamgarpour
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel model-free algorithm for entropy-regularized inverse reinforcement learning (IRL) is proposed, which uses stochastic gradient descent and soft policy iteration updates. The approach is guaranteed to recover a reward for which an expert is ε-optimal using O(1/ε^2) samples of the Markov decision process (MDP). Additionally, with O(1/ε^4) samples, the optimal policy corresponding to the recovered reward is ε-close to the expert policy in total variation distance. This work demonstrates the effectiveness of IRL in recovering rewards and policies from expert demonstrations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Inverse reinforcement learning helps us figure out what makes someone an expert at doing something. It’s like trying to reverse-engineer a recipe by watching a chef cook. The researchers came up with a new way to do this that doesn’t require knowing the exact rules of the game, and it works pretty well! They showed that their method can find a reward that makes an expert optimal, and also get close to the expert’s policy. |
Keywords
* Artificial intelligence * Reinforcement learning * Stochastic gradient descent
