rlNumericSpec

Create continuous action or observation data specifications for reinforcement learning environments

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Description

An rlNumericSpec object specifies continuous action or observation data specifications for reinforcement learning environments.

Creation

Syntax

spec = rlNumericSpec(dimension)
spec = rlNumericSpec(dimension,Name,Value)

Description

example

spec = rlNumericSpec(dimension) creates a data specification for continuous actions or observations and sets the Dimension property.

spec = rlNumericSpec(dimension,Name,Value) sets Properties using name-value pair arguments.

Properties

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Lower limit of the data space, specified as a scalar or matrix of the same size as the data space. When LowerLimit is specified as a scalar, rlNumericSpec applies it to all entries in the data space.

Upper limit of the data space, specified as a scalar or matrix of the same size as the data space. When UpperLimit is specified as a scalar, rlNumericSpec applies it to all entries in the data space.

Name of the rlNumericSpec object, specified as a string.

Description of the rlNumericSpec object, specified as a string.

This property is read-only.

Dimension of the data space, specified as a numeric vector.

This property is read-only.

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

Object Functions

rlSimulinkEnvCreate reinforcement learning environment using dynamic model implemented in Simulink
rlFunctionEnvSpecify custom reinforcement learning environment dynamics using functions
rlRepresentation(Not recommended) Model representation for reinforcement learning agents

Examples

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This example uses:

Open Live Script

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

See Also

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Topics

Introduced in R2019a

Reinforcement Learning Toolbox Documentation

Support