Errror: Undefined function 'getActionInfo' for input arguments of type 'struct'.

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Rajesh Siraskar
Rajesh Siraskar 2021-8-29
评论： Emmanouil Tzorakoleftherakis 2021-9-23
Hi
This would work previously. I now get an error when I try to test a RL agent. Is this an issue of data-type expected?
I have provided the error message as well as the properties of the agent Action and Observation objects.
Error message -> Undefined function 'getActionInfo' for input arguments of type 'struct'.
Agent and Observation objects:
>> DDPG_agent.agent.getActionInfo
rlNumericSpec with properties:
LowerLimit: 0
UpperLimit: 100
Name: [0×0 string]
Description: [0×0 string]
Dimension: [1 1]
DataType: "double"
>> DDPG_agent.agent.getObservationInfo
rlNumericSpec with properties:
LowerLimit: [3×1 double]
UpperLimit: [3×1 double]
Name: "observations"
Description: "controlled flow, error, integral of error"
Dimension: [3 1]
DataType: "double"
  2 个评论
Rajesh Siraskar
Rajesh Siraskar 2021-9-13
编辑：Rajesh Siraskar 2021-9-13
Hi -- Can someone please help?
Dear Emmanouil (Tzorakoleftherakis) - you've helped me in the past - do you know if there is something wrong I am doing?
Rajesh
Emmanouil Tzorakoleftherakis
Emmanouil Tzorakoleftherakis 2021-9-13
Where is the error in the code above? I don't see an error when you call getActionInfo. Can you attach a mat file with the required variables to reproduce this? Also, which release are you using?

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回答（2 个）

Rajesh Siraskar
Rajesh Siraskar 2021-9-22
Hello Emmanouil
Thank you for your help. The error is not in the code as that runs fine. It is the Simulation run that generates errors. I have tried to add the block diagram and original Simulation output below.
=== Simulation (Elapsed: 2 sec) ===
Error:Error in 'sm_DDPG_PPO_Experimental_Setup/DDPG_Sub_System/DDPG_Agent': Failed to evaluate mask initialization commands.
Caused by:
MATLAB System block 'sm_DDPG_PPO_Experimental_Setup/DDPG_Sub_System/DDPG_Agent/AgentWrapper' error occurred when invoking 'getSampleTime' method of 'rl.simulink.blocks.AgentWrapper'. The error was thrown from '
'C:\Program Files\MATLAB\R2020b\toolbox\rl\rl\simulink\+rl\+simulink\+blocks\AgentWrapper.m' at line 152
'C:\Program Files\MATLAB\R2020b\toolbox\rl\rl\simulink\+rl\+simulink\+blocks\AgentWrapper.m' at line 202
'C:\Program Files\MATLAB\R2020b\toolbox\rl\rl\simulink\+rl\+simulink\+blocks\AgentWrapper.m' at line 257
'C:\Program Files\MATLAB\R2020b\toolbox\rl\rl\simulink\+rl\+simulink\+blocks\agentmaskinit.m' at line 11'.
Undefined function 'getActionInfo' for input arguments of type 'struct'.
Error:Error in 'sm_DDPG_PPO_Experimental_Setup/PPO_Sub_System/PPO_Agent': Failed to evaluate mask initialization commands.
Caused by:
MATLAB System block 'sm_DDPG_PPO_Experimental_Setup/PPO_Sub_System/PPO_Agent/AgentWrapper' error occurred when invoking 'getSampleTime' method of 'rl.simulink.blocks.AgentWrapper'. The error was thrown from '
'C:\Program Files\MATLAB\R2020b\toolbox\rl\rl\simulink\+rl\+simulink\+blocks\AgentWrapper.m' at line 152
'C:\Program Files\MATLAB\R2020b\toolbox\rl\rl\simulink\+rl\+simulink\+blocks\AgentWrapper.m' at line 202
'C:\Program Files\MATLAB\R2020b\toolbox\rl\rl\simulink\+rl\+simulink\+blocks\AgentWrapper.m' at line 257
'C:\Program Files\MATLAB\R2020b\toolbox\rl\rl\simulink\+rl\+simulink\+blocks\agentmaskinit.m' at line 11'.
Undefined function 'getActionInfo' for input arguments of type 'struct'.
  1 个评论
Emmanouil Tzorakoleftherakis
Emmanouil Tzorakoleftherakis 2021-9-22
thanks. It is hard to pinpoint the error without a reproduction model, but it seems like you are using a struct somewhere you are not supposed to. Can you doublecheck?

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Rajesh Siraskar
Rajesh Siraskar 2021-9-23
Hi Emmanouil - Here is below my full "simulation" code.
Basically I have trained two models using PPO and DDPG and am trying to run them in parallel to compare their "trajectories".
[Just in case someone else is interested my paper and Git code location.]
Thanks a lot Emmanouil - you are always very helpful
%--------------------------------------------------------------------------
% Reinforcement Learning for Valve Control. V.5.4: 11-Mar. 11pm
% Author: Rajesh Siraskar
% e-mail: rajeshsiraskar@gmail.com; siraskar@coventry.ac.uk
% University: Coventry University, UK, MTech Automotive Engineering
%
% Code: Experiment and validate a trained RL controller. Compare
% against PID control.
% This code accompanies the paper titled "Reinforcement Learning for Control of Valves"
% https://arxiv.org/abs/2012.14668
% -------------------------------------------------------------------------
%
% To experiment with a trained RL controller/agent, launch the Simulink model `sm_Experimental_Setup.slx` and then ensure
% variables are correctly set in the code file `code_Experimental_Setup.m` and excute the code.
% Variables to be set:
% 1. `MODELS_PATH`: Points to your base path for storing the models. Default 'models/'
% 2. `VALVE_SIMULATION_MODEL = sm_Experimental_Setup`: Points to Simulink model used for validation against PID and experimenting with different noise sources etc.
% 3. `PRE_TRAINED_MODEL_FILE = 'Grade_V.mat'`: Pre-trained model (RL controller) to be tested or validated. Example shows a model called `Grade_V.mat`
% 4. `TIME_DELAY`, `fS` (stiction) and `fD` (dynamic friction): Variables that represent the physical parameters. Set these to the values that you want
% test the RL controller against.
% Suggested Graded Learning stages:
% - GRADE_I: TIME_DELAY=0.1; fS = 8.4/10; fD = 3.5243/10
% - GRADE_II: TIME_DELAY=0.5; fS = 8.4/5; fD = 3.5243/5
% - GRADE_III: TIME_DELAY=1.5; fS = 8.4/2; fD = 3.5243/2
% - GRADE_IV: TIME_DELAY=1.5; fS = 8.4/1.5; fD = 3.5243/1.5
% - GRADE_V: TIME_DELAY=2.0, fS = 8.4/1.5; fD = 3.5243/1.5
% - GRADE_VI: TIME_DELAY=2.5, fS = 8.4/1.0; fD = 3.5243/1.0
%--------------------------------------------------------------------------
%clear all;
warning('off', 'all');
%% Set paths
MODELS_PATH = "models\";
VALVE_SIMULATION_MODEL = "sm_DDPG_PPO_Experimental_Setup"; % Simulink experimentation circuit
DDPG_AGENT = "/DDPG_Sub_System/DDPG_Agent";
PPO_AGENT = "/PPO_Sub_System/PPO_Agent";
%% GRADED LEARNING models
PRE_TRAINED_DDPG_MODEL_FILE = "DDPG_TEST.mat";
PRE_TRAINED_PPO_MODEL_FILE = "PPO_TEST.mat";
% Physical system parameters. Use iteratively. Suceessively increase
% difficulty of training task and apply Graded Learning to train the agent
TIME_DELAY = 2.5; % Time delay for process controlled by valve
fS = 8.4000; % Valve dynamic friction
fD = 3.5243; % Valve static friction
% Agent stage to be tested
DDPG_MODEL_FILE = strcat(MODELS_PATH, PRE_TRAINED_DDPG_MODEL_FILE);
PPO_MODEL_FILE = strcat(MODELS_PATH, PRE_TRAINED_PPO_MODEL_FILE);
% Time step. Tf/Ts gives Simulink's simulation time
Ts = 1.0; % Ts: Sample time (secs)
Tf = 200; % Tf: Simulation length (secs)
ACCEPTABLE_DELTA = 0.05;
% Load experiences from pre-trained agent
sprintf('- Load DDPG model: %s', DDPG_MODEL_FILE)
sprintf('- Load PPO model: %s', PPO_MODEL_FILE)
DDPG_agent = load(DDPG_MODEL_FILE,"agent");
PPO_agent = load(PPO_MODEL_FILE,"agent");
% ----------------------------------------------------------------
% Validate the learned agent against the model by simulation
% ----------------------------------------------------------------
% Define observation and action space
NUMBER_OBSERVATIONS = 3;
% Observation Vector
% (1) U(k)
% (2) Error signal
% (3) Error integral
obsInfo = rlNumericSpec([3 1],...
'LowerLimit',[-inf -inf 0]',...
'UpperLimit',[ inf inf inf]');
obsInfo.Name = "observations";
obsInfo.Description = "controlled flow, error, integral of error";
numObservations = obsInfo.Dimension(1);
actionInfo_DDPG = rlNumericSpec([1 1],'LowerLimit', 0,'UpperLimit', 100);
actionInfo_PPO = rlNumericSpec([2 1],'LowerLimit', 0,'UpperLimit', 100);
actionInfo_DDPG.Name = "flow";
actionInfo_PPO.Name = "flow";
% Intialise the environment with the serialised agent and run the test
sprintf ('\n\n ==== RL for control of valves V.5.1 ====================')
sprintf (' ---- Testing model: %s, %s', DDPG_MODEL_FILE, PPO_MODEL_FILE)
sprintf (' ---- Parameters: Time-Delay: %3.2f, fS: %3.2f, fD: %3.2f', TIME_DELAY, fS, fD)
ObservationInfo = [obsInfo, obsInfo];
ActionInfo = [actionInfo_DDPG, actionInfo_PPO];
arObservationInfo = num2cell(ObservationInfo, 1);
arActionInfo = num2cell(ActionInfo, 1);
% open_system(VALVE_SIMULATION_MODEL);
AgentBlocks = VALVE_SIMULATION_MODEL + [DDPG_AGENT, PPO_AGENT];
env = rlSimulinkEnv(VALVE_SIMULATION_MODEL, AgentBlocks, arObservationInfo, arActionInfo);
simOpts = rlSimulationOptions('MaxSteps', 2000);
expr = sim(env, [DDPG_agent.agent, PPO_agent.agent]);
% ------------------------------------------------------------------------
% Environment Reset function
% Randomize Reference_Signal between 0 and 100
% Reset if the controlled speed drops below zero or exceeds 100
% ------------------------------------------------------------------------
function in = localResetFcn(in, RL_System)
block_Reference_Signal = strcat (RL_System, '/Reference_Signal');
Reference_Signal = 20+randi(80) + rand;
in = setBlockParameter(in, block_Reference_Signal, ...
'Value', num2str(Reference_Signal));
% Randomize initial condition of the flow (0 and 100)
block_Actual_Flow = strcat (RL_System, '/Plant/Process/FLOW');
Actual_Flow = 20+randi(80) + rand;
in = setBlockParameter(in, block_Actual_Flow, 'Bias', num2str(Actual_Flow));
end
  1 个评论
Emmanouil Tzorakoleftherakis
Emmanouil Tzorakoleftherakis 2021-9-23
A couple of suggestions:
1) Make sure DDPG_agent.agent and PPO_agent.agent are the actual agent objects and not structs
2) In the Simulink model, make sure to change the 'Agent object' field in the RL Agent block to be PPO_agent.agent or DDPG_agent.agent as needed (I suspect you may have forgotten to do this)

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