How to create an custom Reinforcement Learning Environment + DQN agent

I want to create custom reinforcement learning environment in vehicular communication network using deep Q network agent.

How can I do this ?

Answer 1

[Harsh]

Hi Vartika

To create a custom reinforcement learning environment and train deep Q-Network agent please follow the below steps:

• Create a Simulink environment model that represents the world as seen from the agent. Then add the RL agent block to it. Please refer to the following documentation to understand more about it - https://www.mathworks.com/help/reinforcement-learning/ug/create-custom-simulink-environments.html
• Use “rlSimulinkEnv” to create an object to train and simulate agents in the same way as with any other environment. Please refer to the following documentation for the above-mentioned function - https://www.mathworks.com/help/releases/R2022a/reinforcement-learning/ref/rlsimulinkenv.html
• A Deep Q-Network (DQN) agent is a value-based reinforcement learning agent that trains a critic to estimate the return or future rewards. For information regarding how to create and train DQN agents please refer to the following documentation - https://www.mathworks.com/help/releases/R2022a/reinforcement-learning/ug/dqn-agents.html