Summary of Chirps: Change-induced Regret Proxy Metrics For Lifelong Reinforcement Learning, by John Birkbeck et al.
CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning
by John Birkbeck, Adam Sobey, Federico Cerutti, Katherine Heseltine Hurley Flynn, Timothy J. Norman
First submitted to arxiv on: 5 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 This paper tackles the challenges faced by reinforcement learning (RL) agents when they are exposed to changing environments. Currently, RL agents perform poorly in such scenarios due to catastrophic forgetting or lack of forward transfer. To address this issue, researchers have proposed various lifelong RL agent designs. However, prior work has not established whether the impact on agent performance can be predicted from the change itself. This paper proposes Change-Induced Regret Proxy (CHIRP) metrics to link changes to agent performance drops and demonstrates their utility in lifelong learning using two environments. The authors show that a simple CHIRP-based agent achieved 48% higher performance than the next best method in one benchmark and attained the best success rates in 8 out of 10 tasks in another benchmark, which proved challenging for existing lifelong RL agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning (RL) is a way for computers to learn from mistakes. But when things change, like rules or rewards, RL agents often struggle to adapt. This makes them hard to use in real-life situations where things are always changing. Scientists have tried to make better RL agents that can handle these changes better. But they haven’t figured out how to predict exactly what will happen when something changes. In this paper, researchers propose a new way to measure how well an agent adapts to change. They test this idea with two examples and show that it leads to better results. |
Keywords
» Artificial intelligence » Reinforcement learning
