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getObservationInfo

Obtain observation data specifications from reinforcement learning environment, agent, or experience buffer

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Syntax

obsInfo = getObservationInfo(env)
obsInfo = getObservationInfo(agent)
obsInfo = getObservationInfo(buffer)

Description

obsInfo = getObservationInfo(env) extracts observation information from reinforcement learning environment env.

example

obsInfo = getObservationInfo(agent) extracts observation information from reinforcement learning agent agent.

obsInfo = getObservationInfo(buffer) extracts observation information from experience buffer buffer.

Examples

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Open Live Script

The reinforcement learning environment for this example is a longitudinal dynamics model comprising two cars, a leader and a follower. The vehicle model is also used in the Adaptive Cruise Control System Using Model Predictive Control (Model Predictive Control Toolbox) example.

Open the model.

mdl = "rlACCMdl";
open_system(mdl);

Specify path to the agent block in the model.

agentblk = mdl + "/RL Agent";

Create the observation and action specifications.

% Observation specifications
obsInfo = rlNumericSpec([3 1],LowerLimit=-inf*ones(3,1),UpperLimit=inf*ones(3,1));
obsInfo.Name = "observations";
obsInfo.Description = "information on velocity error and ego velocity";

% Action specifications
actInfo = rlNumericSpec([1 1],LowerLimit=-3,UpperLimit=2);
actInfo.Name = "acceleration";

Define environment interface.

env = rlSimulinkEnv(mdl,agentblk,obsInfo,actInfo)
env = 
SimulinkEnvWithAgent with properties:

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

The reinforcement learning environment env is a SimulinkEnvWithAgent object.

Extract the action and observation specifications from env.

actInfoExt = getActionInfo(env)
actInfoExt = 
  rlNumericSpec with properties:

     LowerLimit: -3
     UpperLimit: 2
           Name: "acceleration"
    Description: [0x0 string]
      Dimension: [1 1]
       DataType: "double"


obsInfoExt = getObservationInfo(env)
obsInfoExt = 
  rlNumericSpec with properties:

     LowerLimit: [3x1 double]
     UpperLimit: [3x1 double]
           Name: "observations"
    Description: "information on velocity error and ego velocity"
      Dimension: [3 1]
       DataType: "double"

The action information contains acceleration values while the observation information contains the velocity and velocity error values of the ego vehicle.

Input Arguments

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Reinforcement learning environment from which to extract the observation information, specified as one of the following objects.

For more information on reinforcement learning environments, see Reinforcement Learning Environments and Create Custom Simulink Environments.

Reinforcement learning agent from which to extract the observation information, specified as one of the following objects.

For more information on reinforcement learning agents, see Reinforcement Learning Agents.

Experience buffer, specified as one of the following replay memory objects.

Output Arguments

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Observation data specifications extracted from the reinforcement learning environment, returned as an array of one of the following:

Each element in the array defines the properties of an environment observation channel, such as its dimensions, data type, and name.

You can extract observationInfo from an existing environment, function approximator, or agent using getObservationInfo. You can also construct the specifications manually using rlFiniteSetSpec or rlNumericSpec.

Version History

Introduced in R2019a

See Also

Functions

Objects

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