Hi,
The answers to your queries are as follows:
1.How to define simulink environment?
To define a Simulink environment, you can use the ‘rlSimulinkEnv’ function provided by the Reinforcement Learning Toolbox in MATLAB. https://www.mathworks.com/help/reinforcement-learning/ref/rlsimulinkenv.html
This function allows you to specify the Simulink model, agent, observation and action specifications.
2. Which agent is suited for the above given environment?
You can start with a simple algorithm and then try progressively more complicated ones if they don't perform as desired. The built-in RL agents don't support mixed-action space.
As a workaround you can try multi-agent training, i.e. use one agent for continuous actions and another agent for discrete actions. You can use a combination of DQN (Deep Q-Network) agent (for discrete action space) and DDPG(Deep Deterministic Policy Gradient) agent (for continuous action space) as these are the simplest compatible agents. Refer to this example which uses multiple agents, to get more information:
3. Is it possible to have multiple action paths for an agent?
Yes, we can have multiple action signals for one agent. For your case, it's better to have different agents for the different action spaces as shown in the above example.
4. Does the dependency of [A, B] need to be defined in the environment or somewhere else (actor or critic net)?
The dependency of [A, B] could be defined in the environment itself. The environment can take the actions [A; B] as inputs and generate the corresponding dependent actions [x1, x3, x5] and [x2, x4, x6] internally.
