How do you optimize /is there a technique for getting the best feature's parameters with reinforced learning? For example moving average lookback period.
Thanks in advance.
Any books or papers would be appreciated.
have you make it work?
Mine Discord is: johanB#6530
Plx need some help.
Hi Jane,
Multiple approaches can be used to optimize feature parameters using reinforced learning.
One such approach is to use a form of reinforcement learning called policy gradient methods, which directly optimize a parameterized policy that maps states to actions. In this approach, the feature parameters are treated as the policy's parameters, and the objective is to maximize the expected reward obtained by following the policy. The policy is updated using gradient ascent on the expected reward, which requires computing the gradient of the policy with respect to the feature parameters.
Another approach is to use model-based reinforcement learning, which involves building a model of the environment and using it to simulate the effects of different feature parameter values. The model can then be used to optimize the feature parameters using various search or optimization methods.
There are many books on reinforcement learning, some specifically on feature parameter optimization. One book that provides an excellent introduction to reinforcement learning is "Reinforcement Learning: An Introduction" by Richard Sutton and Andrew Barto. Another book focusing on feature engineering is "Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists" by Alice Zheng and Amanda Casari.
Hope this helps!
Thanks,
Akshay
Thank you brother Akshay Choudhary, you have harnessed the power of the great Ganesh, the remover of obstacles.
I thank you deepy.
Namaste.