Facing size error while using Reinforcement learning

This is my code :-

% opening rcam_reinforcement_test1.slx simulink model

mdl='rcam_reinforcement_test1';

open_system(mdl)

% creating env

%open_system([mdl '/env+plant'])

obsInfo=rlNumericSpec([12 1]);

obsInfo.Name="observations";

obsInfo.Description='velx ,vely ,velz ,rollrate ,pitchrate ,yawrate ,bankangle ,pitchangle ,yawangle';

numObservations = obsInfo.Dimension();

actInfo=rlNumericSpec([3,1],...

'LowerLimit',[-25*(pi/180) -25*(pi/180) -30*(pi/180)]',...

'UpperLimit',[25*(pi/180) 25*(pi/180) 30*(pi/180)]');

actInfo.Name="controldeflection";

actInfo.Description='aileron ,tail ,ruddder';

numActions = actInfo.Dimension();

env = rlSimulinkEnv('rcam_reinforcement_test1','rcam_reinforcement_test1/RL Agent',obsInfo,actInfo);

%env.ResetFcn = @(in)localResetFcn(in);

Ts = 1.0;

Tf = 20000;

rng(0)

%creating critic layer

actionLayers = [

featureInputLayer(numActions,"Name","Action")

fullyConnectedLayer(15,"Name","hidden_act_1")

reluLayer("Name","relu_3")

fullyConnectedLayer(1,"Name","hidden_act_2")

];

stateLayers = [

featureInputLayer(numObservations,"Name","State")

fullyConnectedLayer(60,"Name","hidden_first")

reluLayer("Name","relu_1")

fullyConnectedLayer(60,"Name","hidden_second")

reluLayer("Name","relu_2")

fullyConnectedLayer(1,"Name","hidden_third")];

commonLayers = [

additionLayer(2,"Name","addition")

reluLayer("Name","relu_4")

fullyConnectedLayer(1,"Name","out_critic")];

criticNetwork = layerGraph();

criticNetwork =addLayers(criticNetwork,actionLayers);

criticNetwork =addLayers(criticNetwork,stateLayers);

criticNetwork =addLayers(criticNetwork,commonLayers);

criticNetwork = connectLayers(criticNetwork,"hidden_act_2","addition/in2");

criticNetwork = connectLayers(criticNetwork,"hidden_third","addition/in1");

% plot(criticNetwork);

% creating critic option

criticOpts = rlRepresentationOptions('LearnRate',1e-03,'GradientThreshold',1);

% creating critic representation

critic = rlQValueRepresentation(criticNetwork,obsInfo,actInfo,'Observation',{'State'},'Action',{'Action'},criticOpts);

% creating action network

actNetwork=[

featureInputLayer(numObservations,"Name","Action")

fullyConnectedLayer(60,"Name","hidden_layer_first")

reluLayer("Name","relu1")

fullyConnectedLayer(36,"Name","hidden_layer_second")

reluLayer("Name","relu2")

fullyConnectedLayer(numActions,"Name","out_action")];

% creating actor option

actorOpts = rlRepresentationOptions('LearnRate',1e-03,'GradientThreshold',1);

% creating critic representation

actor = rlDeterministicActorRepresentation(actNetwork,obsInfo,actInfo,'Observation',{'Action'},'Action',{'out_action'},actorOpts);

agentOpts = rlDDPGAgentOptions(...

'SampleTime',Ts,...

'TargetSmoothFactor',1e-3,...

'DiscountFactor',1.0, ...

'MiniBatchSize',64, ...

'ExperienceBufferLength',1e6);

%agentOpts.ExplorationModel.StandardDeviation = [0.3 0.3 0.3];

%agentOpts.ExplorationModel.StandardDeviationDecayRate = 1e-5;

agent = rlDDPGAgent(actor,critic,agentOpts);

% training conf

maxepisodes = 5000;

maxsteps = ceil(Tf/Ts);

trainOpts = rlTrainingOptions(...

'MaxEpisodes',maxepisodes, ...

'MaxStepsPerEpisode',maxsteps, ...

'ScoreAveragingWindowLength',20, ...

'Verbose',false, ...

'Plots','training-progress',...

'StopTrainingCriteria','AverageReward',...

'StopTrainingValue',9.3123e+09);

doTraining = true;

if doTraining

% Train the agent.

trainingStats = train(agent,env,trainOpts);

else

% Load the pretrained agent for the example.

%load('WaterTankDDPG.mat','agent')

end

% validating by sim in env

simOpts = rlSimulationOptions('MaxSteps',maxsteps,'StopOnError','on');

experiences = sim(env,agent,simOpts);

######################################################################################################################

This the error that i am getting :-

Error using rl.env.AbstractEnv/simWithPolicy (line 83)

Unable to simulate model 'rcam_reinforcement_test1' with the agent 'agent'.

Error in rl.task.SeriesTrainTask/runImpl (line 33)

[varargout{1},varargout{2}] = simWithPolicy(this.Env,this.Agent,simOpts);

Error in rl.task.Task/run (line 21)

[varargout{1:nargout}] = runImpl(this);

Error in rl.task.TaskSpec/internal_run (line 166)

[varargout{1:nargout}] = run(task);

Error in rl.task.TaskSpec/runDirect (line 170)

[this.Outputs{1:getNumOutputs(this)}] = internal_run(this);

Error in rl.task.TaskSpec/runScalarTask (line 194)

runDirect(this);

Error in rl.task.TaskSpec/run (line 69)

runScalarTask(task);

Error in rl.train.SeriesTrainer/run (line 24)

run(seriestaskspec);

Error in rl.train.TrainingManager/train (line 424)

run(trainer);

Error in rl.train.TrainingManager/run (line 215)

train(this);

Error in rl.agent.AbstractAgent/train (line 77)

TrainingStatistics = run(trainMgr);

Error in rlagent_glider (line 111)

trainingStats = train(agent,env,trainOpts);

Caused by:

Error using rl.env.SimulinkEnvWithAgent>localHandleSimoutErrors (line 667)

Invalid input argument type or size such as observation, reward, isdone or loggedSignals.

Error using rl.env.SimulinkEnvWithAgent>localHandleSimoutErrors (line 667)

Unable to compute gradient from representation.

Error using rl.env.SimulinkEnvWithAgent>localHandleSimoutErrors (line 667)

Number of elements must not change. Use [] as one of the size inputs to automatically calculate the appropriate size

for that dimension.

#############################################################################################################

I am not able to figure out what is causing the error