Discussion of article "Neural networks made easy (Part 35): Intrinsic Curiosity Module"
Super
Do you have the model file? it seems not in the zip file.
Do you have more information about how to create the model by the NetCreator as well or at least share this file? the EA can't start run withtout those file.
as said below:
To train the EA, all models were created using the NetCreator tool. It should be added that to enable EA operation in the strategy tester, the model files must be located in the terminal common directory 'Terminal\Common\Files', since each agent operates in its own sandbox, so they can exchange data only via the common terminals folder.
Neural networks made easy (Part 25): Practicing Transfer Learning
- www.mql5.com
In the last two articles, we developed a tool for creating and editing neural network models. Now it is time to evaluate the potential use of Transfer Learning technology using practical examples.
Can I second the request for more details on how exactly the model should be created? I would really like to experiment with this EA but this is blocking me!
francobritannique #:
Can I second the request for more details on how exactly the model should be created? I would really like to experiment with this EA but this is blocking me!
Can I second the request for more details on how exactly the model should be created? I would really like to experiment with this EA but this is blocking me!
Hi, You can use model from next article.
Neural networks made easy (Part 36): Relational Reinforcement Learning
- www.mql5.com
In the reinforcement learning models we discussed in previous article, we used various variants of convolutional networks that are able to identify various objects in the original data. The main advantage of convolutional networks is the ability to identify objects regardless of their location. At the same time, convolutional networks do not always perform well when there are various deformations of objects and noise. These are the issues which the relational model can solve.
yuk ping wong #:
Do you have the model file? it seems not in the zip file.
Do you have more information about how to create the model by the NetCreator as well or at least share this file? the EA can't start run withtout those file.
as said below:
To train the EA, all models were created using the NetCreator tool. It should be added that to enable EA operation in the strategy tester, the model files must be located in the terminal common directory 'Terminal\Common\Files', since each agent operates in its own sandbox, so they can exchange data only via the common terminals folder.Hi, about creating model with NetCreator you can read at "Neural networks made easy (Part 31): Evolutionary algorithms"
Neural networks made easy (Part 31): Evolutionary algorithms
- www.mql5.com
In the previous article, we started exploring non-gradient optimization methods. We got acquainted with the genetic algorithm. Today, we will continue this topic and will consider another class of evolutionary algorithms.
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New article Neural networks made easy (Part 35): Intrinsic Curiosity Module has been published:
We continue to study reinforcement learning algorithms. All the algorithms we have considered so far required the creation of a reward policy to enable the agent to evaluate each of its actions at each transition from one system state to another. However, this approach is rather artificial. In practice, there is some time lag between an action and a reward. In this article, we will get acquainted with a model training algorithm which can work with various time delays from the action to the reward.
To train the EA, all models were created using the NetCreator tool. It should be added that to enable EA operation in the strategy tester, the model files must be located in the terminal common directory 'Terminal\Common\Files', since each agent operates in its own sandbox, so they can exchange data only via the common terminals folder.
Training in the strategy tester takes a little longer than the previous virtual training approach. For this reason, I reduced the model training period to 10 months. The rest of the test parameters remained unchanged. Again, I used EURUSD on the H1 timeframe. Indicators were used with default parameters.
To be honest, I expected that the learning process would begin with the deposit loss. But during the first pass, the model showed a result close to 0. Then it even received some profit in the second pass. The model performed 330 trades with more than 98% of operations being profitable.
Author: Dmitriy Gizlyk