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How to establish Reinforcement Learning setup between simulink and ROS2 Gazebo?

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I am trying to establsh communication between the Reinforcement Learning (RL) agent block defined in simulink and the Gazebo Simulator that responds to ROS2 action calls. I am using a ROS2 Service call block in Simulink, and a pythoin script is used in the middle to call the appropriate action for each service request.
In this setup, I want to ensure that every action of the RL agent is passed as a discrete service request, and that returns with a response.
However, as soon as I begin the simulation, too many service requests are continuously placed, even before the RL agent produces the next action.
I need assistance on controlling the rate of service requests placed, and especially I want the RL agent to be dictator of calling each and every service requests.

Respuestas (1)

Gaurav Bhosale
Gaurav Bhosale el 27 de Nov. de 2023
Look like this issue migh be due to ROS-Simulink and Gazebo are not in synchronization.
We are providing Gazebo-Simulink connectivity without ROS, which maintains syncronization between Simulink and Gazebo. You can check Gazebo Co-Sim Example. Let me know if you need further details.


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