Summary of Simba: Simplicity Bias For Scaling Up Parameters in Deep Reinforcement Learning, by Hojoon Lee et al.
SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
by Hojoon Lee, Dongyoon Hwang, Donghu Kim, Hyunseung Kim, Jun Jet Tai, Kaushik Subramanian, Peter R. Wurman, Jaegul Choo, Peter Stone, Takuma Seno
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Recent advances in computer vision (CV) and natural language processing (NLP) have largely been driven by scaling up network parameters, despite traditional theories suggesting that larger networks are prone to overfitting. To mitigate this issue, components inducing a simplicity bias were integrated into these models, guiding them toward simple and generalizable solutions. However, in deep reinforcement learning (RL), designing and scaling up networks has been less explored. This paper presents SimBa, an architecture designed to scale up parameters in deep RL by injecting a simplicity bias. SimBa consists of three components: observation normalization, residual feedforward blocks, and layer normalization. By scaling up parameters with SimBa, the sample efficiency of various deep RL algorithms, including off-policy, on-policy, and unsupervised methods, is consistently improved. Furthermore, solely by integrating SimBa into Soft Actor-Critic (SAC), it matches or surpasses state-of-the-art deep RL methods with high computational efficiency across DMC, MyoSuite, and HumanoidBench. These results demonstrate SimBa’s broad applicability and effectiveness across diverse RL algorithms and environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make artificial intelligence (AI) learn faster and more efficiently. Currently, AI models get better by having more information, but this can sometimes lead to them becoming too good at the specific task they’re learning for and not being able to generalize well to new situations. To solve this problem, researchers have been designing AI models that are simpler and more generalizable. However, when it comes to a type of AI called reinforcement learning (RL), which is used in games like Go or video games, there hasn’t been much work done on how to make these models learn faster and better. The authors of this paper propose a new architecture for RL models that can be scaled up to learn more efficiently. They tested their idea with several different types of RL algorithms and found that it worked well across many different environments. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Overfitting » Reinforcement learning » Unsupervised
