For this example, consider the rlSimplePendulumModel Simulink model. The model is a simple frictionless pendulum that is initially hanging in a downward position.

Open the model.

mdl = 'rlSimplePendulumModel';
open_system(mdl)

Create rlNumericSpec and rlFiniteSetSpec objects for the observation and action information, respectively.

obsInfo = rlNumericSpec([3 1]) % vector of 3 observations: sin(theta), cos(theta), d(theta)/dt

obsInfo = 
  rlNumericSpec with properties:

     LowerLimit: -Inf
     UpperLimit: Inf
           Name: [0x0 string]
    Description: [0x0 string]
      Dimension: [3 1]
       DataType: "double"

actInfo = rlFiniteSetSpec([-2 0 2]) % 3 possible values for torque: -2 Nm, 0 Nm and 2 Nm

actInfo = 
  rlFiniteSetSpec with properties:

       Elements: [3x1 double]
           Name: [0x0 string]
    Description: [0x0 string]
      Dimension: [1 1]
       DataType: "double"

You can use dot notation to assign property values for the rlNumericSpec and rlFiniteSetSpec objects.

obsInfo.Name = 'observations';
actInfo.Name = 'torque';

Assign the agent block path information, and create the reinforcement learning environment for the Simulink model using information extracted in the previous steps.

agentBlk = [mdl '/RL Agent'];
env = rlSimulinkEnv(mdl,agentBlk,obsInfo,actInfo)

env = 
SimulinkEnvWithAgent with properties:

           Model : rlSimplePendulumModel
      AgentBlock : rlSimplePendulumModel/RL Agent
        ResetFcn : []
  UseFastRestart : on

You can also include a reset function using dot notation. For this example, randomly initialize theta0 in the model workspace.

env.ResetFcn = @(in) setVariable(in,'theta0',randn,'Workspace',mdl)

env = 
SimulinkEnvWithAgent with properties:

           Model : rlSimplePendulumModel
      AgentBlock : rlSimplePendulumModel/RL Agent
        ResetFcn : @(in)setVariable(in,'theta0',randn,'Workspace',mdl)
  UseFastRestart : on

If the actor for your reinforcement learning agent has multiple outputs, each with a discrete action space, you can specify the possible discrete actions combinations using an rlFiniteSetSpec object.

Suppose that the valid values for a two-output system are [1 2] for the first output and [10 20 30] for the second output. Create a discrete action space specification for all possible input combinations.

actionSpec = rlFiniteSetSpec({[1 10],[1 20],[1 30],...
                              [2 10],[2 20],[2 30]})

actionSpec = 
  rlFiniteSetSpec with properties:

       Elements: {6x1 cell}
           Name: [0x0 string]
    Description: [0x0 string]
      Dimension: [1 2]
       DataType: "double"