Summary of Streaming Deep Reinforcement Learning Finally Works, by Mohamed Elsayed et al.
Streaming Deep Reinforcement Learning Finally Works
by Mohamed Elsayed, Gautham Vasan, A. Rupam Mahmood
First submitted to arxiv on: 18 Oct 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 The proposed stream-x algorithms are a novel class of deep reinforcement learning (RL) methods that overcome the “stream barrier” and achieve sample efficiency comparable to batch RL. The stream barrier refers to the frequent instability and failure to learn in existing deep RL algorithms, particularly those using streaming updates. In this paper, the authors introduce three new stream-x algorithms – stream Q, stream AC, and stream TD – which demonstrate successful stable learning in various environments, including Mujoco Gym, DM Control Suite, and Atari Games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The stream-x algorithms work by applying a set of common techniques that enable their success with a single set of hyperparameters. These techniques can be extended to other algorithms, making streaming RL a viable option for real-world applications where resource constraints are a concern. The authors demonstrate the effectiveness of their approach through experiments in various environments and tasks. |
Keywords
* Artificial intelligence * Reinforcement learning
