https://github.com/papachristoumarios/ntua-neuro-fuzzy-control

Neuro-Fuzzy Control Assignments for ECE NTUA 9th Semester Course "Neuro-Fuzzy Control and Applications" (ECE1374)
https://github.com/papachristoumarios/ntua-neuro-fuzzy-control

neural-control q-learning reinforcement-learning

Last synced: 8 months ago
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Neuro-Fuzzy Control Assignments for ECE NTUA 9th Semester Course "Neuro-Fuzzy Control and Applications" (ECE1374)

• Host: GitHub
• URL: https://github.com/papachristoumarios/ntua-neuro-fuzzy-control
• Owner: papachristoumarios
• License: mit
• Created: 2019-01-15T10:32:30.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-02-03T21:09:49.000Z (over 6 years ago)
• Last Synced: 2025-01-12T18:37:44.204Z (9 months ago)
• Topics: neural-control, q-learning, reinforcement-learning
• Language: TeX
• Size: 624 KB
• Stars: 5
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Neuro-Fuzzy Control

Neuro-Fuzzy Control Assignments for ECE NTUA 9th Semester Course "Neuro-Fuzzy Control and Applications" (ECE1374).

## Q-Learning

This repository contains an implementation of Q-Learning Reinforcement Learning Technique for Optimal Control Problems (Approximate Dynamic Programming). In particular, we are designing a state feedback controller for a linear dynamical system (without knowing the model) that stabilizes our system and minimizes an Quadratic Cost Criterion (LQR). 

The agent tries a random input `u(k) = - L x(k)`  on the dynamical system `x(k+1) = f(x(k), u(k)) ` and by sampling the response 	`x(k)` it learns the optimal gain matrix `K` for the LQR Criterion.  

We use samples from the distribution and construct a least squares problem `ZH =R` which is solved in order to get the unvectorized matrix `H` and the gain. More information can be found in [1].

The implementation is done in MATLAB.

## References

[1] Bradtke, Steven J. "Reinforcement learning applied to linear quadratic regulation." *Advances in neural information processing systems*. 1993.