Summary of Dynamite-rl: a Dynamic Model For Improved Temporal Meta-reinforcement Learning, by Anthony Liang et al.
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
by Anthony Liang, Guy Tennenholtz, Chih-wei Hsu, Yinlam Chow, Erdem Bıyık, Craig Boutilier
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach for approximate inference in environments where the latent state evolves at varying rates. The proposed method models episode sessions – parts of the episode where the latent state is fixed – and makes three key modifications to existing meta-RL methods: consistency of latent information within sessions, session masking, and prior latent conditioning. The authors demonstrate the importance of these modifications in various domains, including Gridworld, continuous-control, and simulated robot assistive tasks, showing that DynaMITE-RL outperforms state-of-the-art baselines in sample efficiency and inference returns. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn from experiences. Imagine playing different levels of a game where some things change slowly and others quickly. The researchers created a new method called DynaMITE-RL that can handle this kind of situation better than other methods. They made three important changes to the original approach: making sure information within each level is consistent, hiding certain parts of the level, and using previous experiences to help make decisions. They tested their method in different types of games and showed it works better than others in terms of how many tries it takes to learn and how well it performs. |
Keywords
* Artificial intelligence * Inference * Reinforcement learning