Dot indexing is not supported for variables of this type.

Error:

Dot indexing is not supported for variables of this type.

Error in rl.util.expstruct2timeserstruct (line 7)

observation = {experiences.Observation};

Error in rl.env.AbstractEnv/sim (line 130)

s = rl.util.expstruct2timeserstruct(exp,time,oinfo,ainfo);

Code:

open_system("draft.slx")

Define the observation specification obsInfo and action specification actInfo.
% Observation info
obsInfo = rlNumericSpec([3 1],...
    LowerLimit=[-inf -inf 0  ]',...
    UpperLimit=[ inf  inf inf]');

% Name and description are optional and not used by the software
obsInfo.Name = "observations";
obsInfo.Description = "integrated error, error, and measured height";

% Action info
actInfo = rlNumericSpec([1 1]);
actInfo.Name = "thrust";

% Create the environment object.
env = rlSimulinkEnv("draft","draft/RL Agent",...
    obsInfo,actInfo);
% Set a custom reset function that randomizes the reference values for the model.
env.ResetFcn = @(in)localResetFcn(in);

% Specify the simulation time Tf and the agent sample time Ts in seconds.
Ts = 1.0;
Tf = 200;
% Fix the random generator seed for reproducibility.
rng(0)

Create the Critic

DDPG agents use a parametrized Q-value function approximator to estimate the value of the policy. A Q-value function critic takes the current observation and an action as inputs and returns a single scalar as output (the estimated discounted cumulative long-term reward for which receives the action from the state corresponding to the current observation, and following the policy thereafter).

To model the parametrized Q-value function within the critic, use a neural network with two input layers (one for the observation channel, as specified by obsInfo, and the other for the action channel, as specified by actInfo) and one output layer (which returns the scalar value).

Define each network path as an array of layer objects. Assign names to the input and output layers of each path. These names allow you to connect the paths and then later explicitly associate the network input and output layers with the appropriate environment channel. Obtain the dimension of the observation and action spaces from the obsInfo and actInfo specifications.

% Observation path
obsPath = [
    featureInputLayer(obsInfo.Dimension(1),Name="obsInLyr")
    fullyConnectedLayer(50)
    reluLayer
    fullyConnectedLayer(25,Name="obsPathOutLyr")
    ];

% Action path
actPath = [
    featureInputLayer(actInfo.Dimension(1),Name="actInLyr")
    fullyConnectedLayer(25,Name="actPathOutLyr")
    ];

% Common path
commonPath = [
    additionLayer(2,Name="add")
    reluLayer
    fullyConnectedLayer(1,Name="QValue")
    ];

criticNetwork = layerGraph();
criticNetwork = addLayers(criticNetwork,obsPath);
criticNetwork = addLayers(criticNetwork,actPath);
criticNetwork = addLayers(criticNetwork,commonPath);

criticNetwork = connectLayers(criticNetwork, ...
    "obsPathOutLyr","add/in1");
criticNetwork = connectLayers(criticNetwork, ...
    "actPathOutLyr","add/in2");

% View the critic network configuration.
figure
plot(criticNetwork)

% Convert the network to a dlnetwork object and summarize its properties.
criticNetwork = dlnetwork(criticNetwork);
summary(criticNetwork)

Initialized: true

Number of learnables: 1.5k

Inputs:

1 'obsInLyr' 3 features

2 'actInLyr' 1 features

% Create the critic approximator object using the specified deep neural network, the environment specification objects, and the names if the network inputs to be associated with the observation and action channels. 
critic = rlQValueFunction(criticNetwork, ...
    obsInfo,actInfo, ...
    ObservationInputNames="obsInLyr", ...
    ActionInputNames="actInLyr");

For more information on Q-value function objects, see rlQValueFunction.

% Check the critic with a random input observation and action.
getValue(critic, ...
    {rand(obsInfo.Dimension)}, ...
    {rand(actInfo.Dimension)})

ans = single

-0.1631

For more information on creating critics, see Create Policies and Value Functions.

Create the Actor

DDPG agents use a parametrized deterministic policy over continuous action spaces, which is learned by a continuous deterministic actor.

A continuous deterministic actor implements a parametrized deterministic policy for a continuous action space. This actor takes the current observation as input and returns as output an action that is a deterministic function of the observation.

To model the parametrized policy within the actor, use a neural network with one input layer (which receives the content of the environment observation channel, as specified by obsInfo) and one output layer (which returns the action to the environment action channel, as specified by actInfo).

% Define the network as an array of layer objects.
actorNetwork = [
    featureInputLayer(obsInfo.Dimension(1))
    fullyConnectedLayer(3)
    tanhLayer
    fullyConnectedLayer(actInfo.Dimension(1))
    ];
% Convert the network to a dlnetwork object and summarize its properties.
actorNetwork = dlnetwork(actorNetwork);
summary(actorNetwork)

Initialized: true

Number of learnables: 16

Inputs:

1 'input' 3 features

% Create the actor approximator object using the specified deep neural network, the environment specification objects, and the name if the network input to be associated with the observation channel. 
actor = rlContinuousDeterministicActor(actorNetwork,obsInfo,actInfo);

For more information, see rlContinuousDeterministicActor.

% Check the actor with a random input observation.
getAction(actor,{rand(obsInfo.Dimension)})

ans = 1×1 cell array

{[-0.3408]}

For more information on creating critics, see Create Policies and Value Functions.

Create the DDPG Agent

% Create the DDPG agent using the specified actor and critic approximator objects.
agent = rlDDPGAgent(actor,critic);

For more information, see rlDDPGAgent.

% Specify options for the agent, the actor, and the critic using dot notation.
agent.SampleTime = Ts;

agent.AgentOptions.TargetSmoothFactor = 1e-3;
agent.AgentOptions.DiscountFactor = 1.0;
agent.AgentOptions.MiniBatchSize = 64;
agent.AgentOptions.ExperienceBufferLength = 1e6; 

agent.AgentOptions.NoiseOptions.Variance = 0.3;
agent.AgentOptions.NoiseOptions.VarianceDecayRate = 1e-5;

agent.AgentOptions.CriticOptimizerOptions.LearnRate = 1e-03;
agent.AgentOptions.CriticOptimizerOptions.GradientThreshold = 1;
agent.AgentOptions.ActorOptimizerOptions.LearnRate = 1e-04;
agent.AgentOptions.ActorOptimizerOptions.GradientThreshold = 1;

Alternatively, you can specify the agent options using an rlDDPGAgentOptions object.

% Check the agent with a random input observation.
getAction(agent,{rand(obsInfo.Dimension)})

ans = 1×1 cell array

{[-0.7926]}

Train Agent

To train the agent, first specify the training options. For this example, use the following options:

• Run each training for at most 5000 episodes. Specify that each episode lasts for at most ceil(Tf/Ts) (that is 200) time steps.
• Display the training progress in the Episode Manager dialog box (set the Plots option) and disable the command line display (set the Verbose option to false).
• Stop training when the agent receives an average cumulative reward greater than 800 over 20 consecutive episodes. At this point, the agent can control the level of water in the tank.

For more information, see rlTrainingOptions.

trainOpts = rlTrainingOptions(...
    MaxEpisodes=5000, ...
    MaxStepsPerEpisode=ceil(Tf/Ts), ...
    ScoreAveragingWindowLength=20, ...
    Verbose=false, ...
    Plots="training-progress",...
    StopTrainingCriteria="AverageReward",...
    StopTrainingValue=800);

Train the agent using the train function. Training is a computationally intensive process that takes several minutes to complete. To save time while running this example, load a pretrained agent by setting doTraining to false. To train the agent yourself, set doTraining to true.

doTraining = false;

if doTraining
    % Train the agent.
    trainingStats = train(agent,env,trainOpts);
else
    % Load the pretrained agent for the example.
    load("CPS.mat","agent")
end

Validate Trained Agent

Validate the learned agent against the model by simulation. Since the reset function randomizes the reference values, fix the random generator seed to ensure simulation reproducibility.

rng(1)

Simulate the agent within the environment, and return the experiences as output.

simOpts = rlSimulationOptions(MaxSteps=ceil(Tf/Ts), StopOnError='on');
experiences = sim(env,agent,simOpts);

Dot indexing is not supported for variables of this type.

Error in rl.util.expstruct2timeserstruct (line 7)

observation = {experiences.Observation};

Error in rl.env.AbstractEnv/sim (line 130)

s = rl.util.expstruct2timeserstruct(exp,time,oinfo,ainfo);

Local Reset Function

function in = localResetFcn(in)
% Randomize reference signal
blk = sprintf("draft/Desired \nDrone Height");
h = 3 * randn + 10;
while h <= 0 || h >= 20
    h = 3 * randn + 10;
end
in = setBlockParameter(in, blk, Value=num2str(h));

% Return the updated input structure
end

Copyright 2019 - 2023 The MathWorks, Inc.

Answer 1

[Ronit]

Hi Samiha,

The error you're encountering, “Dot indexing is not supported for variables of this type”, is likely due to a mismatch in the expected structure or type of the variable you're trying to access with dot notation. This error usually occurs when you are trying to use dot notation on a variable that is not a struct or an object that supports such operations.

Here's a structured approach to address this issue:

• First, ensure that "experiences" is indeed the type of variable you expect. Right after the simulation command that generates experiences, add a line to check its type:

experiences = sim(env,agent,simOpts); 
disp(class(experiences)); % Check the variable type 
disp(isstruct(experiences)); % Check if it's a struct 

• If experiences is a struct and supposed to contain fields like Observation, manually inspect it to ensure it has the expected structure:

disp(fieldnames(experiences)); % List all field names 

• If the structure of experiences doesn't match what your processing code expects, you may need to adjust how you're handling the simulation output. The specific handling will depend on what sim returns for your environment and agent setup.

Here is a documentation for further details about dot indexing in MATLAB: https://www.mathworks.com/help/matlab/matlab_prog/indexing-into-function-call-results.html

Hope this helps!