Summary of Overcoming Slow Decision Frequencies in Continuous Control: Model-based Sequence Reinforcement Learning For Model-free Control, by Devdhar Patel et al.
Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control
by Devdhar Patel, Hava Siegelmann
First submitted to arxiv on: 11 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 Medium Difficulty summary: Reinforcement learning (RL) has reached human-level control capabilities but requires specialized hardware and fast decision-making. To address this, we introduce Sequence Reinforcement Learning (SRL), a novel algorithm that produces action sequences for given input states. SRL employs model-actor-critic architectures operating at different temporal scales, with a “temporal recall” mechanism to estimate intermediate states between primitive actions. We evaluate SRL on continuous control tasks and demonstrate comparable performance to state-of-the-art algorithms while reducing actor sample complexity. To assess performance across varying decision frequencies, we introduce the Frequency-Averaged Score (FAS) metric, showing that SRL outperforms traditional RL algorithms in terms of FAS. Additionally, we compare SRL with model-based online planning, highlighting its biological relevance by replicating the “action chunking” behavior observed in the basal ganglia. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Reinforcement learning is a way for machines to learn from trial and error. But current algorithms require fast decisions, which isn’t practical for real-world situations. Our new algorithm, Sequence Reinforcement Learning (SRL), allows machines to make decisions at slower speeds while still performing well. SRL uses two different approaches to learn actions and predict what will happen next. We tested SRL on various tasks and found it works just as well as other top algorithms, but with more efficient use of resources. To compare results across different decision-making frequencies, we came up with a new metric called Frequency-Averaged Score (FAS). Our tests show that SRL performs better than traditional RL algorithms in terms of FAS. |
Keywords
» Artificial intelligence » Recall » Reinforcement learning
