View RSS Feed

mql5

Neural networks made easy (Part 52): Research with optimism and distribution correction

Rate this Entry
by , 12-29-2023 at 07:48 AM (511 Views)
      
   
One of the basic elements for increasing the stability of Q-function learning is the use of an experience replay buffer. Increasing the buffer makes it possible to collect more diverse examples of interaction with the environment. This allows our model to better study and reproduce the Q-function of the environment. This technique is widely used in various reinforcement learning algorithms, including algorithms of the Actor-Critic family.
more...

Submit "Neural networks made easy (Part 52): Research with optimism and distribution correction" to Google Submit "Neural networks made easy (Part 52): Research with optimism and distribution correction" to del.icio.us Submit "Neural networks made easy (Part 52): Research with optimism and distribution correction" to Digg Submit "Neural networks made easy (Part 52): Research with optimism and distribution correction" to reddit

Categories
Uncategorized

Comments