rlFiniteSetSpec

Create discrete action or observation data specifications for reinforcement learning environments

Description

An rlFiniteSetSpec object specifies discrete action or observation data specifications for reinforcement learning environments.

Creation

Description

example

spec = rlFiniteSetSpec(elements) creates a data specification with a discrete set of actions or observations, setting the Elements property.

Properties

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Set of valid actions or observations for the environment, specified as one of the following:

  • Vector — Specify valid numeric values for a single action or single observation.

  • Cell array — Specify valid numeric value combinations when you have more than one action or observation. Each entry of the cell array must have the same dimensions.

Name of the rlFiniteSetSpec object, specified as a string. Use this property to set a meaningful name for your finite set.

Description of the rlFiniteSetSpec object, specified as a string. Use this property to specify a meaningful description of the finite set values.

This property is read-only.

Size of each element, specified as a vector.

If you specify Elements as a vector, then Dimension is [1 1]. Otherwise, if you specify a cell array, then Dimension indicates the size of the entries in Elements.

This property is read-only.

Information about the type of data, specified as a string.

Object Functions

rlSimulinkEnvCreate a reinforcement learning environment using a dynamic model implemented in Simulink
rlFunctionEnvSpecify custom reinforcement learning environment dynamics using functions
rlRepresentationModel representation for reinforcement learning agents

Examples

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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)

Assign the agent block path information, and create rlNumericSpec and rlFiniteSetSpec objects for the observation and action information. You can use dot notation to assign property values of the rlNumericSpec and rlFiniteSetSpec objects.

agentBlk = [mdl '/RL Agent'];
obsInfo = rlNumericSpec([3 1])
obsInfo = 
  rlNumericSpec with properties:

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

actInfo = rlFiniteSetSpec([2 1])
actInfo = 
  rlFiniteSetSpec with properties:

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

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

Create the reinforcement learning environment for the Simulink model using information extracted in the previous steps.

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, consider randomly initializing 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"

Introduced in R2019a