Summary of Active Reinforcement Learning Strategies For Offline Policy Improvement, by Ambedkar Dukkipati et al.
Active Reinforcement Learning Strategies for Offline Policy Improvement
by Ambedkar Dukkipati, Ranga Shaarad Ayyagari, Bodhisattwa Dasgupta, Parag Dutta, Prabhas Reddy Onteru
First submitted to arxiv on: 17 Dec 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 research paper proposes an active reinforcement learning method that reduces additional online interaction with the environment by up to 75% compared to competitive baselines. The approach focuses on collecting trajectories that can augment existing offline data, which is essential for problems involving sequential decision-making tasks, such as selecting candidates for medical trials and training agents in complex navigation environments. The proposed method demonstrates improved performance across various continuous control environments like Gym-MuJoCo locomotion environments, Maze2d, AntMaze, CARLA, and IsaacSimGo1. By leveraging existing offline data, the approach provides a more efficient way to learn from sequential decision-making tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an innovative method for active reinforcement learning that helps reduce the need for online interactions with the environment. The goal is to make better use of existing data collected by unknown behavior policies. This is especially important in situations where collecting new data can be expensive or time-consuming, such as selecting medical trial candidates or training agents in complex environments. By reusing old data and collecting new trajectories that augment it, the method shows significant improvements across various challenges. |
Keywords
» Artificial intelligence » Reinforcement learning
