Summary of Dmc-vb: a Benchmark For Representation Learning For Control with Visual Distractors, by Joseph Ortiz et al.
DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors
by Joseph Ortiz, Antoine Dedieu, Wolfgang Lehrach, Swaroop Guntupalli, Carter Wendelken, Ahmad Humayun, Guangyao Zhou, Sivaramakrishnan Swaminathan, Miguel Lázaro-Gredilla, Kevin Murphy
First submitted to arxiv on: 26 Sep 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 The paper proposes the DeepMind Control Visual Benchmark (DMC-VB) to evaluate the robustness of offline reinforcement learning (RL) agents for solving continuous control tasks from visual input. The dataset, which is an order of magnitude larger than prior works, combines locomotion and navigation tasks with varying difficulties, static and dynamic visual variations, policies with different skill levels, and hidden goals. The authors also propose three benchmarks to evaluate representation learning methods for pretraining. Experiments show that pretrained representations do not help policy learning on DMC-VB, but expert data is limited, policy learning can benefit from representations pretrained on suboptimal data or tasks with stochastic hidden goals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how well artificial intelligence (AI) agents learn by showing them images and videos. The AI agents are asked to control robots to do different tasks like walking and navigating. But, the agents often get confused if there is something in the background that they haven’t seen before. To fix this, the researchers created a big dataset with many examples of these tasks and added some extra challenges like moving objects or changing backgrounds. They also tested how well AI agents do when they are given old data to learn from instead of new data. The results show that the best way for AI agents to learn is by using all the data, including the hard ones. |
Keywords
» Artificial intelligence » Pretraining » Reinforcement learning » Representation learning
