nikhilxb/ncap-swimmer

"Neural Circuit Architectural Priors for Embodied Control" (NeurIPS 2022)

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Emerging

This research explores how pre-defined neural circuit structures, inspired by biological systems, can improve the learning process for embodied control tasks. It takes in simulated environments and applies these architectural priors to create more efficient and robust control policies. Researchers in computational neuroscience or robotics seeking to design more biologically plausible and sample-efficient learning agents would find this useful.

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Use this if you are a researcher developing AI agents for embodied control and want to investigate the impact of neural circuit architectural priors on learning efficiency and performance.

Not ideal if you are looking for a plug-and-play solution for real-world robotic control or are not familiar with reinforcement learning concepts and simulation environments.

computational-neuroscience reinforcement-learning-research robotics-simulation neuromorphic-computing embodied-ai
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 14 / 25

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Stars

13

Forks

3

Language

Python

License

MIT

Last pushed

Oct 10, 2024

Commits (30d)

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