Summary of Coarse-to-fine Q-network with Action Sequence For Data-efficient Robot Learning, by Younggyo Seo et al.
Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning
by Younggyo Seo, Pieter Abbeel
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 introduces a novel value-based reinforcement learning algorithm called Coarse-to-fine Q-Network with Action Sequence (CQN-AS) for robotics tasks. The algorithm learns to predict the long-term consequences of executing action sequences, taking into account noisy robotic data and complex robot movements. In contrast to traditional RL methods that focus on individual actions, CQN-AS trains a critic network to output Q-values over sequences of actions. This allows it to better understand the effects of individual actions in robotics tasks. The algorithm is evaluated on 53 robotic tasks from BiGym, HumanoidBench, and RLBench, outperforming various baselines, particularly in humanoid control tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way for robots to learn how to move using a type of artificial intelligence called reinforcement learning. Traditionally, these algorithms focus on what happens when one action is taken, but this doesn’t work well with robots because their movements are made up of many small actions put together. To solve this problem, the researchers developed an algorithm that learns to predict the outcomes of sequences of actions, not just individual ones. This helps the robot understand how its different movements affect its overall actions and makes it better at controlling itself. |
Keywords
* Artificial intelligence * Reinforcement learning
