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Intelligent Control – Reinforcement Learning

The application of Artificial Intelligence (AI) and Reinforcement Learning (RL) techniques has grown in recent years. The idea of learning and optimizing from experience can be effectively used in control strategies in simulation. But the execution of these techniques directly into real-life applications can be challenging due to the presence of uncertainties and disturbances. Our aim is to develop and implement concepts from AI and RL to design effective control strategies and test them in actual systems in our lab. The inverted pendulum (IP) system is one of the most popular benchmark devices to test new control algorithms. An RL-based control strategy coupled with a PID controller is tested to swing up and balance the linear IP in our lab. The RL Agent is trained in simulation before applying it to the system in our lab. Now work is being done so that the Agent can be trained online using different algorithms. The diagrams below show the IP setup in the lab, the schematic of the control strategy where the Agent is trained in simulation and then deployed to the real system along with a PID controller to stabilize the balance, and the average reward received by different RL Agents during simulation.

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