Created Deep Recurrent Q-Network example#85
Open
Douglas-Cho wants to merge 6 commits intorlcode:masterfrom
Open
Created Deep Recurrent Q-Network example#85Douglas-Cho wants to merge 6 commits intorlcode:masterfrom
Douglas-Cho wants to merge 6 commits intorlcode:masterfrom
Conversation
This shows the way to implement Deep Recurrent Q-Network (DRQN) model for the Cartpole case. I had to expand the state input to include a few number of past state data and created a meaningful sequential input stream for Long and Short-Term Memory (LSTM) model. Otherwise, it did not work with just current state information. This sounds like violating the Markov property assumption but this does the job.
Create cartpole-drqn.py
the graph for drqn
saved weights for drqn
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
This shows the way to implement Deep Recurrent Q-Network (DRQN) model for the Cartpole case. I had to expand the state input to include a few number of past state data and created a meaningful sequential input stream for Long and Short-Term Memory (LSTM) model. Otherwise, it did not work with just current state information. This sounds like violating the Markov property assumption but this does the job.