Summary of Recurrent Reinforcement Learning with Memoroids, by Steven Morad et al.
Recurrent Reinforcement Learning with Memoroids
by Steven Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki, Jakob Foerster, Amanda Prorok
First submitted to arxiv on: 15 Feb 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 monoid-based framework called memoroids is introduced to improve the scalability of memory models for Partially Observable Markov Decision Processes (POMDPs). The existing Linear Recurrent Models are reformulated using this framework, enabling better handling of long sequences. By revisiting traditional batching methods in recurrent reinforcement learning, a new approach is proposed that boosts sample efficiency, increases returns, and simplifies implementation of recurrent loss functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Memory models like RNNs and Transformers help solve POMDP problems by linking trajectories to hidden Markov states. However, these models don’t handle long sequences well. A new type of memory model called Linear Recurrent Models is better at this task. The way these models work is similar to a mathematical structure called a monoid. This idea leads us to create a new framework that improves how we do memory-based learning. |
Keywords
* Artificial intelligence * Reinforcement learning
