Summary of Pwm: Policy Learning with Multi-task World Models, by Ignat Georgiev et al.
PWM: Policy Learning with Multi-Task World Models
by Ignat Georgiev, Varun Giridhar, Nicklas Hansen, Animesh Garg
First submitted to arxiv on: 2 Jul 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 A novel model-based reinforcement learning algorithm called Policy learning with multi-task World Models (PWM) is introduced, which efficiently solves complex tasks in continuous control settings. PWM utilizes well-regularized world models to generate smoother optimization landscapes, facilitating first-order optimization. Pre-training the world model on offline data and then extracting policies using first-order optimization enables PWM to solve tasks with up to 152 action dimensions in under 10 minutes per task. PWM outperforms existing baselines without relying on costly online planning, achieving up to 27% higher rewards in an 80-task setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Policy learning with multi-task World Models (PWM) is a new way to use computers to teach themselves how to do things. It’s like teaching a robot to pick up objects or drive a car. PWM uses a special kind of computer simulation to help the robot learn, and it works really well. The algorithm can even solve very complex problems with lots of different actions involved. This is important because it means that robots and computers can get better at doing things on their own. |
Keywords
* Artificial intelligence * Multi task * Optimization * Reinforcement learning
