Hi Borel Merveil,
To create and define all parameters in a custom step function using the Bellman equation in a Deep Q-Network (DQN) learning algorithm in MATLAB, you need to follow these steps:
- Create a function that represents your environment.
- Set up the DQN agent with the necessary parameters.
- Implement the Bellman equation in the custom step function.
Below is a concise example to illustrate these steps:
Step 1: Define the Environment:
Create a function that simulates the environment. This function should return the next state, reward, and a flag indicating whether the episode is done.
function [nextState, reward, isDone] = myEnvironment(state, action)
% Define your environment dynamics here
% Example: simple linear system
nextState = state + action;
% Define reward function
reward = -abs(nextState); % Example reward
% Define termination condition
isDone = abs(nextState) > 10; % Example termination condition
end
Step 2: Define the DQN Agent:
Set up the DQN agent with the necessary parameters.
% Define the state and action spaces
stateSize = 1; % Example state size
actionSize = 1; % Example action size
% Create the critic network
criticNetwork = [
featureInputLayer(stateSize, 'Normalization', 'none', 'Name', 'state')
fullyConnectedLayer(24, 'Name', 'fc1')
reluLayer('Name', 'relu1')
fullyConnectedLayer(24, 'Name', 'fc2')
reluLayer('Name', 'relu2')
fullyConnectedLayer(actionSize, 'Name', 'fc3')];
% Define the critic options
criticOptions = rlRepresentationOptions('LearnRate', 1e-3, 'GradientThreshold', 1);
% Create the critic
critic = rlQValueRepresentation(criticNetwork, ...
rlNumericSpec([stateSize 1]), ...
rlFiniteSetSpec([-1 1]), ... % Example action space
'Observation', {'state'}, ...
'Action', {'action'}, ...
criticOptions);
% Define the DQN agent options
agentOptions = rlDQNAgentOptions(...
'SampleTime', 1, ...
'DiscountFactor', 0.99, ...
'ExperienceBufferLength', 1e6, ...
'MiniBatchSize', 64, ...
'TargetUpdateFrequency', 4, ...
'TargetSmoothFactor', 1e-3);
% Create the DQN agent
agent = rlDQNAgent(critic, agentOptions);
Step 3: Define the Custom Step Function
Implement the Bellman equation in the custom step function.
function [nextState, reward, isDone, loggedSignals] = myStepFunction(state, action, loggedSignals)
% Define the environment dynamics
[nextState, reward, isDone] = myEnvironment(state, action);
% Bellman equation parameters
gamma = 0.99; % Discount factor
% Get the Q-value for the current state-action pair
qValue = getValue(agent.getCritic(), {state, action});
% Get the maximum Q-value for the next state
maxQValueNext = max(getValue(agent.getCritic(), {nextState, action}));
% Update the Q-value using the Bellman equation
qValueUpdated = reward + gamma * maxQValueNext;
% Update the critic network with the new Q-value
setValue(agent.getCritic(), {state, action}, qValueUpdated);
end
Training the Agent
Finally, train the agent using the custom step function.
% Define the training options
trainOpts = rlTrainingOptions(...
'MaxEpisodes', 1000, ...
'MaxStepsPerEpisode', 200, ...
'Verbose', false, ...
'Plots', 'training-progress');
% Train the agent
trainingStats = train(agent, myStepFunction, trainOpts);
This example provides a basic framework for creating a custom step function using the Bellman equation in a DQN learning algorithm in MATLAB. Adjust the state, action spaces, and environment dynamics according to your specific problem.
You can follow this documentation: https://in.mathworks.com/help/reinforcement-learning/ref/rl.agent.rldqnagent.html
I hope this helps!
