Reinforcement Learning based PID Tuner

This project is the implemetation of the Reinforcement Learning based Online PID Tuner. The Tuner is based on A2C. I trained the RL tuner and tested on Lunarlander, one of OpenAi gym env..

Procedure

Flowchart

Pseudo code

Init (P,I,D) of the environment
Init the policy π
for episode = 0, M do
	Inint state
	Set done = False
	Reset the environment
	while not done do
		action = π(state)
		next_state, reward, done = step(action)
		Train π
		state = next_state
	end while
end for

Environment

Using Simple PID control example to build PID environment.

• MDP
  • state (5,) : Set Point, feedback, error, I-term, P
  • action (1,) : P
  • reward (1,) : if abs(error) in a certain range, give 1. Or, give -1

Result

Please check here - Experiment Report (Korean)

Pretrain result

• Before training

• After training

• Training plot

Test PID control with auto tuner in Lunarlander-v2

It do not need any tuning process.

• Render

• Error Plot

Orange line represents set-points, and blue line represents feedbacks. (left) Angular controller. (Right) Vertical controller.

Usage

Training

cd ./A2C/
python a2c_main.py

Test

cd ./envs/
python ./LunarLanderContinuous_keyboard_agent_tuner_applied.py

requirements

tensorflow==2.5.0
scikit-learn==0.23.2
matplotlib==3.8.3
gym

reference

/~https://github.com/ivmech/ivPID

/~https://github.com/pasus/Reinforcement-Learning-Book