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rlFiniteSetSpec

Create discrete action or observation data specifications for reinforcement learning environments

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Description

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

Creation

Syntax

spec = rlFiniteSetSpec(elements)

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, such as "double" or "single".

Object Functions

rlSimulinkEnvCreate reinforcement learning environment using dynamic model implemented in Simulink
rlFunctionEnvSpecify custom reinforcement learning environment dynamics using functions
rlValueFunctionValue function approximator object for reinforcement learning agents
rlQValueFunction Q-Value function approximator object for reinforcement learning agents
rlVectorQValueFunction Vector Q-value function approximator for reinforcement learning agents
rlContinuousDeterministicActor Deterministic actor with a continuous action space for reinforcement learning agents
rlDiscreteCategoricalActorStochastic categorical actor with a discrete action space for reinforcement learning agents
rlContinuousGaussianActorStochastic Gaussian actor with a continuous action space 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 initially hangs in a downward position.

Open the model.

mdl = 'rlSimplePendulumModel';
open_system(mdl)

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

The observation is a vector containing three signals: the sine, cosine, and time derivative of the angle.

obsInfo = rlNumericSpec([3 1]) 
obsInfo = 
  rlNumericSpec with properties:

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

The action is a scalar expressing the torque and can be one of three possible values, -2 Nm, 0 Nm and 2 Nm.

actInfo = rlFiniteSetSpec([-2 0 2])
actInfo = 
  rlFiniteSetSpec with properties:

       Elements: [3×1 double]
           Name: [0×0 string]
    Description: [0×0 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 the 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
Open Live Script

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"

Version History

Introduced in R2019a

See Also

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