Summary of Overestimation, Overfitting, and Plasticity in Actor-critic: the Bitter Lesson Of Reinforcement Learning, by Michal Nauman et al.
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning
by Michal Nauman, Michał Bortkiewicz, Piotr Miłoś, Tomasz Trzciński, Mateusz Ostaszewski, Marek Cygan
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 recent study in off-policy Reinforcement Learning (RL) has led to significant improvements in sample efficiency due to various regularization techniques. While these advancements have been tested in limited settings and against well-known algorithms, the specific mechanisms driving RL improvements remain unclear. To address this, researchers implemented over 60 different off-policy agents, each integrating established regularization techniques from state-of-the-art algorithms. The agents were tested across 14 diverse tasks from two simulation benchmarks, measuring training metrics related to overestimation, overfitting, and plasticity loss. The findings reveal that certain combinations of regularization techniques consistently demonstrate robust and superior performance, with a simple Soft Actor-Critic agent reliably finding better-performing policies within the training regime. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Off-policy Reinforcement Learning has made big progress recently. Researchers have found ways to make algorithms learn faster by using special tricks called “regularization” techniques. These techniques help agents avoid mistakes and find better solutions more quickly. However, most of these tests have been done on simple tasks in limited settings. This makes it hard to understand what’s really going on behind the scenes. To fix this, scientists tried 60 different off-policy agents with various regularization techniques. They tested these agents on 14 different tasks from two different simulation environments. The results show that some combinations of tricks work better than others for certain types of problems. One simple algorithm called Soft Actor-Critic can find a good solution most of the time, even without using complex models. |
Keywords
* Artificial intelligence * Overfitting * Regularization * Reinforcement learning
