Summary of Coprocessor Actor Critic: a Model-based Reinforcement Learning Approach For Adaptive Brain Stimulation, by Michelle Pan et al.
Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
by Michelle Pan, Mariah Schrum, Vivek Myers, Erdem Bıyık, Anca Dragan
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 A novel, model-based reinforcement learning (MBRL) approach, Coprocessor Actor Critic, is introduced to learn neural coprocessor policies for brain stimulation. The method leverages the combination of learning optimal actions and inducing optimal actions through stimulation in an injured brain. This work addresses limitations of traditional model-free reinforcement learning (MFRL) methods by overcoming sample inefficiency and improving task success. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help people with brain injuries or diseases like Parkinson’s is being developed. Right now, treatments require one-size-fits-all approaches that don’t always work best for each individual. The goal is to find a personalized treatment plan using a learning method called reinforcement learning. This approach can learn how to stimulate the brain in a way that helps people with these conditions recover better and faster. |
Keywords
» Artificial intelligence » Reinforcement learning
