getObservationInfo
Obtain observation data specifications from reinforcement learning environment, agent, or experience buffer
Syntax
Description
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";
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
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.
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.
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.
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
objectsrlFiniteSetSpec
objectsA mix of
rlNumericSpec
andrlFiniteSetSpec
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
See Also
Functions
Objects
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