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How can I extract a trained RL Agent's network's weights and biases?

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How can I extract a trained RL Agent's network's weights and biases?
My network is:
statePath = [
imageInputLayer([numObservations 1 1], 'Normalization', 'none', 'Name', 'state')
fullyConnectedLayer(NumNeuron, 'Name', 'CriticStateFC1')
reluLayer('Name', 'CriticRelu1')
fullyConnectedLayer(NumNeuron, 'Name', 'CriticStateFC2')];
actionPath = [
imageInputLayer([1 1 1], 'Normalization', 'none', 'Name', 'action')
fullyConnectedLayer(NumNeuron, 'Name', 'CriticActionFC1')
reluLayer('Name', 'ActorRelu1')
fullyConnectedLayer(NumNeuron, 'Name', 'CriticActionFC2')];
commonPath = [
additionLayer(2,'Name', 'add')
reluLayer('Name','CriticCommonRelu')
fullyConnectedLayer(1, 'Name', 'output')];
criticNetwork = layerGraph(statePath);
criticNetwork = addLayers(criticNetwork, actionPath);
criticNetwork = addLayers(criticNetwork, commonPath);
criticNetwork = connectLayers(criticNetwork,'CriticStateFC2','add/in1');
criticNetwork = connectLayers(criticNetwork,'CriticActionFC2','add/in2');
% set some options for the critic
criticOpts = rlRepresentationOptions('LearnRate',learing_rate,...
'GradientThreshold',1);
% create the critic based on the network approximator
critic = rlQValueRepresentation(criticNetwork,obsInfo,actInfo,...
'Observation',{'state'},'Action',{'action'},criticOpts);
agent = rlDQNAgent(critic,agentOpts)
trainingStats = train(agent,env,trainOpts);
After training, I'd like to get the network's trained weights and biases.

Accepted Answer

Anh Tran
Anh Tran on 27 Mar 2020
Edited: Anh Tran on 27 Mar 2020
You can get the parameters from the trained's critic representation for DQN agent. In MATLAB R2020a, see getLearnableParameters and getCritic functions (function name changes a bit since R2019b). You can follow similar steps to get the actor's parameters from actor-based agent like DDPG or PPO.
critic = getCritic(agent);
criticParams = getLearnableParameters(critic);
  6 Comments
轩
on 5 Jan 2024
@Francisco Serra I have the same need. I find a silly method: save the agent after each episode and use "getLearnableParameters" to print the parameter of each agent.

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