Summary of Coupled Autoregressive Active Inference Agents For Control Of Multi-joint Dynamical Systems, by Tim N. Nisslbeck et al.
Coupled autoregressive active inference agents for control of multi-joint dynamical systems
by Tim N. Nisslbeck, Wouter M. Kouw
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Systems and Control (eess.SY)
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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 paper proposes an active inference agent that can identify and control a mechanical system with multiple bodies connected by joints. The agent is composed of multiple scalar autoregressive model-based agents, which are coupled together through shared memories. Each subagent infers parameters using Bayesian filtering and controls the system by minimizing expected free energy over a finite time horizon. The paper demonstrates the effectiveness of this coupled agent in learning the dynamics of a double mass-spring-damper system and driving it to a desired position through a balance of explorative and exploitative actions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates an AI that can control a mechanical system with multiple moving parts. This AI, called an active inference agent, is made up of smaller agents that work together and share information. Each small agent learns about the system by looking at past data and makes decisions to move the system towards a goal position. The AI outperforms individual agents when working together, making it more effective in controlling the mechanical system. |
Keywords
» Artificial intelligence » Autoregressive » Inference
