getObservationInfo

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

Syntax

obsInfo = getObservationInfo(env)

obsInfo = getObservationInfo(agent)

obsInfo = getObservationInfo(buffer)

Description

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

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

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

Examples

Extract Action and Observation Specifications from Reinforcement Learning Environment

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";

Create environment object.

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: [0×0 string]
      Dimension: [1 1]
       DataType: "double"

obsInfoExt = getObservationInfo(env)

obsInfoExt = 
  rlNumericSpec with properties:

     LowerLimit: [3×1 double]
     UpperLimit: [3×1 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

env — Reinforcement learning environment

rlFunctionEnv object | SimulinkEnvWithAgent object | rlNeuralNetworkEnvironment object | predefined MATLAB environment object

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

• MATLAB^® environment represented as one of the following objects.

  • rlFunctionEnv

  • rlNeuralNetworkEnvironment

  • Predefined MATLAB environment created using rlPredefinedEnv

• Simulink^® environment represented as a SimulinkEnvWithAgent object.

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

agent — Reinforcement learning agent

rlQAgent object | rlSARSAAgent object | rlDQNAgent object | rlPGAgent object | rlDDPGAgent object | rlTD3Agent object | rlACAgent object | rlPPOAgent object | rlTRPOAgent object | rlSACAgent object | rlMBPOAgent object

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

• rlQAgent

• rlSARSAAgent

• rlDQNAgent

• rlDDPGAgent

• rlTD3Agent

• rlPGAgent

• rlACAgent

• rlPPOAgent

• rlTRPOAgent

• rlSACAgent

• rlMBPOAgent

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

buffer — Experience buffer

rlReplayMemory object | rlPrioritizedReplayMemory object | rlHindsightReplayMemory object | rlHindsightPrioritizedReplayMemory object

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

• rlReplayMemory

• rlPrioritizedReplayMemory

• rlHindsightReplayMemory

• rlHindsightPrioritizedReplayMemory

Output Arguments

obsInfo — Observation data specifications

array of rlNumericSpec objects | array of rlFiniteSetSpec objects

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

• rlNumericSpec objects

• rlFiniteSetSpec objects

• A mix of rlNumericSpec and rlFiniteSetSpec objects

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