getActionInfo
Obtain action data specifications from reinforcement learning environment, agent, or experience buffer
Description
Examples
Extract Action and Observation Information from Reinforcement Learning Environment
Extract action and observation information that you can use to create other environments or agents.
The reinforcement learning environment for this example is the simple longitudinal dynamics for ego car and lead car. The training goal is to make the ego car travel at a set velocity while maintaining a safe distance from lead car by controlling longitudinal acceleration (and braking). This example uses the same vehicle model as the Adaptive Cruise Control System Using Model Predictive Control (Model Predictive Control Toolbox) example.
Open the model and create the reinforcement learning environment.
mdl = 'rlACCMdl'; open_system(mdl); agentblk = [mdl '/RL Agent']; % create the observation info 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 Info actInfo = rlNumericSpec([1 1],'LowerLimit',-3,'UpperLimit',2); actInfo.Name = 'acceleration'; % define environment 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 SimulinkWithAgent
object with the above properties.
Extract the action and observation information from the reinforcement learning environment 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 action information, specified as one of the following:
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 Create MATLAB Reinforcement Learning Environments and Create Simulink Reinforcement Learning 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 action 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
Experience buffer from which to extract the action information, specified as an
rlReplayMemory
or
rlPrioritizedReplayMemory
object.
Output Arguments
actInfo
— Action data specifications
array of rlNumericSpec
objects | array of rlFiniteSetSpec
objects
Action 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
Version History
Introduced in R2019a
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