Summary of Iqrl — Implicitly Quantized Representations For Sample-efficient Reinforcement Learning, by Aidan Scannell et al.
iQRL – Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
by Aidan Scannell, Kalle Kujanpää, Yi Zhao, Mohammadreza Nakhaei, Arno Solin, Joni Pajarinen
First submitted to arxiv on: 4 Jun 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 paper proposes a new method for learning representations in reinforcement learning (RL), specifically for continuous control tasks. The approach, called iQRL, uses self-supervised latent-state consistency loss to map observations to latent states and predict future latent states. This is achieved through an encoder and dynamics model combination. To prevent representation collapse, the latent representation is quantized to preserve its rank. The method is shown to outperform other recently proposed representation learning methods in continuous control benchmarks from DeepMind Control Suite. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper develops a new way for artificial intelligence (AI) to learn and improve by itself without being explicitly taught. This AI system uses observations and predictions to create a mental map of the world, which helps it make better decisions. The new method is easy to use with any type of AI algorithm and performs well in complex tasks. |
Keywords
» Artificial intelligence » Encoder » Reinforcement learning » Representation learning » Self supervised
