rlNumericSpec

Create continuous action or observation data specifications for reinforcement learning environments

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

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

LowerLimit — Lower limit of the data space

'-inf' (default) | scalar | matrix

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.

UpperLimit — Upper limit of the data space

'inf' (default) | scalar | matrix

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 — Name of the rlNumericSpec object

string (default)

Name of the rlNumericSpec object, specified as a string.

Description — Description of the rlNumericSpec object

string (default)

Description of the rlNumericSpec object, specified as a string.

Dimension — Dimension of the data space

numeric vector (default)

This property is read-only.

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

DataType — Information about the type of data

string (default)

This property is read-only.

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

Object Functions

rlSimulinkEnv	Create reinforcement learning environment using dynamic model implemented in Simulink
rlFunctionEnv	Specify custom reinforcement learning environment dynamics using functions
rlRepresentation	(Not recommended) Model representation for reinforcement learning agents

Examples

Reinforcement Learning Environment for Simulink Model

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