Coprocessor Actor Critic: A Model-based Reinforcement Learning Approach For Adaptive Brain Stimulation
2024 Β· Michelle Pan, Mariah Schrum, Vivek Myers, et al.
Abstract
Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each patient requires a unique stimulation policy to achieve optimal neural responses. Model-free reinforcement learning (MFRL) holds promise in learning effective policies for a variety of similar control tasks, but is limited in domains like brain stimulation by a need for numerous costly environment interactions. In this work we introduce Coprocessor Actor Critic, a novel, model-based reinforcement learning (MBRL) approach for learning neural coprocessor policies for brain stimulation. Our key insight is that coprocessor policy learning is a combination of learning how to act optimally in the world and learning how to induce optimal actions in the world through stimulation of an injured brain. We show that our approach overcomes the limitations of traditional MFRL methods in terms of sample
Authors
(none)
Tags
Stats
Related papers
- Sample-efficient Reinforcement Learning Controller For Deep Brain Stimulation In Parkinson's Disease (2025)0.00
- Reinforcement Learning Framework For Deep Brain Stimulation Study (2020)7.50
- Reinforcement Learning With Brain-inspired Modulation Can Improve Adaptation To Environmental Changes (2022)0.00
- Efficient Deep Reinforcement Learning With Predictive Processing Proximal Policy Optimization (2022)0.00
- Context Meta-reinforcement Learning Via Neuromodulation (2021)6.34
- Novel Reinforcement Learning Algorithm For Suppressing Synchronization In Closed Loop Deep Brain Stimulators (2022)2.26
- Partial Models For Building Adaptive Model-based Reinforcement Learning Agents (2024)0.00
- Robustifying A Policy In Multi-agent RL With Diverse Cooperative Behaviors And Adversarial Style Sampling For Assistive Tasks (2024)0.00