Summary of Continuous Time Continuous Space Homeostatic Reinforcement Learning (ctcs-hrrl) : Towards Biological Self-autonomous Agent, by Hugo Laurencon et al.
Continuous Time Continuous Space Homeostatic Reinforcement Learning (CTCS-HRRL) : Towards Biological Self-Autonomous Agent
by Hugo Laurencon, Yesoda Bhargava, Riddhi Zantye, Charbel-Raphaël Ségerie, Johann Lussange, Veeky Baths, Boris Gutkin
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 The HRRL (Homeostatic Regulated Reinforcement Learning) framework aims to explain the learned behavior of homeostasis by linking Drive Reduction Theory and Reinforcement Learning. The previous discrete-time-space implementation has been successful, but this work advances the framework to a continuous-time-space environment, introducing CTCS-HRRL (Continuous Time Continuous Space HRRL). To achieve this, the authors design a model that mimics real-world biological agents’ homeostatic mechanisms using neural networks and Reinforcement Learning. The model is validated through simulation-based experiments, demonstrating the agent’s ability to dynamically choose policies favoring homeostasis in changing internal-state environments. This framework has promising applications for modeling animal dynamics and decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Homeostasis is when living things keep themselves healthy by balancing what’s inside them. Scientists thought that this was something we learned over time. A new way of understanding this, called HRRL (Homeostatic Regulated Reinforcement Learning), links two ideas: how our bodies work and how computers learn. This idea has been tried in a computer simulation where things happen one at a time, but now scientists have made it work for when things happen continuously. They did this by making a model that works like real animals’ bodies do. The model uses special math and computer learning to figure out what’s best for the animal. By testing this model on a computer, they showed that it can help an animal learn to take care of itself in changing situations. This new way of understanding could be helpful for studying how animals make decisions. |
Keywords
* Artificial intelligence * Reinforcement learning
