Summary of No Regrets: Investigating and Improving Regret Approximations For Curriculum Discovery, by Alexander Rutherford et al.
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
by Alexander Rutherford, Michael Beukman, Timon Willi, Bruno Lacerda, Nick Hawes, Jakob Foerster
First submitted to arxiv on: 27 Aug 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 This paper tackles the problem of selecting training environments for reinforcement learning agents. It focuses on Unsupervised Environment Design (UED) methods, which aim to create adaptive curricula that enable agents to be robust to in- and out-of-distribution tasks. The study investigates how existing UED methods prioritize tasks, finding that practical approximations do not correlate with regret but with success rate. This means that agents spend a significant portion of their experience on environments they have already mastered, offering little contribution to enhancing their abilities. The authors develop a new method that directly trains on scenarios with high learnability and outperforms existing UED methods in several binary-outcome environments, including Minigrid and a novel robotics-inspired setting. Additionally, the paper introduces an adversarial evaluation procedure for measuring robustness using conditional value at risk (CVaR). The authors provide open-source code and visualizations of final policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about how to help artificial intelligence agents learn better. Right now, there are different ways to train these agents, but they’re not all very good. Some methods try to make the agents more robust, meaning they can handle unexpected situations. But it turns out that most of the training happens in environments where the agent has already mastered the task, so it’s not really helping them learn anything new. The researchers developed a new method that focuses on learning from scenarios where the agent can sometimes solve the problem, but not always. This approach works better than other methods in some tests. They also created a way to measure how well an agent can handle unexpected situations. |
Keywords
» Artificial intelligence » Reinforcement learning » Unsupervised
