Water Tank Reinforcement Learning Environment Model

Actions and Observations

A reinforcement learning environment receives action signals from the agent and generates observation signals in response to these actions. To create and train an agent, you must create action and observation specification objects.

The action signal for this environment is the flow rate control signal that is sent to the plant. To create a specification object for an action channel carrying a continuous signal, use rlNumericSpec.

actionInfo = rlNumericSpec([1 1]);
actionInfo.Name = "flow";

If the action signal takes one of a discrete set of possible values, create the specification using the rlFiniteSetSpec function.

For this environment, there are three observation signals sent to the agent, specified as a vector signal. The observation vector is ${\left[\begin{array}{ccc}\int \mathit{e}\mathrm{dt}& \mathit{e}& \mathit{h}\end{array}\right]}^{\mathit{T}}$, where:

Compute the observation signals in the generate observations subsystem.

open_system(mdl + "/generate observations")

Create a three-element vector of observation specifications. Specify a lower bound of 0 for the water height, leaving the other observation signals unbounded.

observationInfo = rlNumericSpec([3 1],...
    LowerLimit=[-inf -inf 0  ]',...
    UpperLimit=[ inf  inf inf]');
observationInfo.Name = "observations";
observationInfo.Description = "integrated error, error, and measured height";

If the actions or observations are represented by bus signals, create specifications using the bus2RLSpec function.

Reward Signal

Construct a scalar reward signal. For this example, specify the following reward.

The reward is positive when the error is below 0.1 and negative otherwise. Also, there is a large reward penalty when the water height is outside the 0 to 20 range.

Construct this reward in the calculate reward subsystem.

open_system(mdl + "/calculate reward")

Stop Signal

To terminate training episodes and simulations, specify a logical signal to the isdone input port of the block. For this example, terminate the episode if $\mathit{h}\le 0$ or $\mathit{h}\ge 20$.

Compute this signal in the stop simulation subsystem.

open_system(mdl + "/stop simulation")

Create Environment Object

Create an environment object for the Simulink model.

env = rlSimulinkEnv(mdl,mdl + "/RL Agent",observationInfo,actionInfo);

Reset Function

You can also create a custom reset function that randomizes parameters, variables, or states of the model. In this example, the reset function randomizes the reference signal and the initial water height and sets the corresponding block parameters.

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

To make sure that the reset function always returns the same random initial value, fix the random stream generator before calling the reset function. For example fix the random number stream with the seed 0 and random number algorithm Mersenne Twister. For more information on random number generation see rng.

previousRngState = rng(0,"twister");

Local Function

function in = localResetFcn(in)

% Randomize reference signal
h = 3*randn + 10;
while h <= 0 || h >= 20
    h = 3*randn + 10;
end
in = setBlockParameter(in, ...
    "rlwatertank/Desired \nWater Level", ...
    Value=num2str(h));

% Randomize initial height
h = 3*randn + 10;
while h <= 0 || h >= 20
    h = 3*randn + 10;
end
in = setBlockParameter(in, ...
    "rlwatertank/Water-Tank System/H", ...
    InitialCondition=num2str(h));

end