Summary of Off-policy Maximum Entropy Rl with Future State and Action Visitation Measures, by Adrien Bolland et al.
Off-Policy Maximum Entropy RL with Future State and Action Visitation Measures
by Adrien Bolland, Gaspard Lambrechts, Damien Ernst
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 introduces a novel maximum entropy reinforcement learning framework that leverages the distribution of states and actions visited by a policy. This framework adds an intrinsic reward function to the Markov decision process, which is designed to encourage exploration. The intrinsic reward is defined as the relative entropy of the discounted distribution of states and actions (or features) visited during subsequent time steps. The paper shows that an optimal exploration policy, which maximizes the expected discounted sum of intrinsic rewards, also maximizes a lower bound on the state-action value function under certain assumptions. Furthermore, it proves that the visitation distribution used in the intrinsic reward definition is the fixed point of a contraction operator. To learn this fixed point and compute the intrinsic rewards, existing algorithms are adapted to enhance exploration. A new off-policy maximum entropy reinforcement learning algorithm is also introduced, which efficiently computes high-performing control policies while achieving good state-action space coverage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way for machines to learn from experience. It creates a system that encourages them to explore and try new things by giving rewards for visiting different states and actions. This helps the machine to learn more quickly and make better decisions in the long run. The paper shows how this works mathematically, and then describes how to use existing algorithms to make it work in practice. The results are impressive, with machines learning to control complex systems efficiently while covering a wide range of possibilities. |
Keywords
» Artificial intelligence » Reinforcement learning
