How to save and use the pre-trained DQN agent in the reinforcement learning tool box
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I would like to use the DQN agent pre-trained in the following neural network to solve other problems. How can I save the pre-trained DQN agent. Also, how can I use this trained agent to solve other problems.
lgraph = layerGraph();
tempLayers = [
imageInputLayer([1 1 1],"Name","Block Actions","Normalization","none")
fullyConnectedLayer(50,"Name","fc_2")
reluLayer("Name","relu_2")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
imageInputLayer([20 1 1],"Name","Block Observations","Normalization","none")
scalingLayer("Name","scaling","Scale",12)
fullyConnectedLayer(50,"Name","fc_1")
reluLayer("Name","relu_1")];
lgraph = addLayers(lgraph,tempLayers);
tempLayers = [
additionLayer(2,"Name","addition")
fullyConnectedLayer(10,"Name","fc_3")
reluLayer("Name","relu_3")
fullyConnectedLayer(1,"Name","stateValue")];
lgraph = addLayers(lgraph,tempLayers);
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回答(1 个)
Emmanouil Tzorakoleftherakis
2021-1-16
Hello,
if doTraining
% Train the agent.
trainingStats = train(agent,env,trainOpts);
% Save the agent
save myagent.mat agent
else
% Load the pretrained agent for the example.
load('MATLABCartpoleDQNMulti.mat','agent')
end
3 个评论
Maha Mosalam
2022-3-22
So when I use the pretrained agent after that to get actions, I should defined the whole algorithm , I mean(the environment action space , actor and critic layers , expect the trainig line , just
use load(myagent.mat, agent)
Maha Mosalam
2022-3-23
Warning: Cannot load an object of class 'rlDLNetworkModel':
Its class cannot be found.
Warning: Cannot load an object of class 'rlADAMOptimizer':
Its class cannot be found.
Warning: While loading an object of class 'rl.representation.rlQValueRepresentation':
Undefined function 'getLoss' for input arguments of type 'double'.
Warning: Cannot load an object of class 'rlDLNetworkModel':
Its class cannot be found.
.....
I got these mesege when just
load(myagent.mat, agent)
then get action from that, not redefined the actor and critic, and environment
另请参阅
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