Summary of Mastering Memory Tasks with World Models, by Mohammad Reza Samsami and Artem Zholus and Janarthanan Rajendran and Sarath Chandar
Mastering Memory Tasks with World Models
by Mohammad Reza Samsami, Artem Zholus, Janarthanan Rajendran, Sarath Chandar
First submitted to arxiv on: 7 Mar 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 A new family of state space models (SSMs) is integrated into world models to improve temporal coherence in model-based reinforcement learning (MBRL) agents. The Recall to Imagine (R2I) method enhances both long-term memory and long-horizon credit assignment, allowing for effective solving of tasks with extended time gaps between actions and outcomes. R2I establishes a new state-of-the-art for challenging memory and credit assignment RL tasks such as BSuite and POPGym, while also showcasing superhuman performance in the complex memory domain of Memory Maze. Additionally, it upholds comparable performance in classic RL tasks like Atari and DMC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve long-term dependencies in reinforcement learning by using a new type of model called Recall to Imagine (R2I). This makes robots and computers better at remembering things that happened a long time ago, which is important for doing tasks that need this kind of memory. The R2I method works really well for hard problems like solving puzzles or playing games, and it’s even faster than other methods that are already good. |
Keywords
* Artificial intelligence * Recall * Reinforcement learning
