Created Deep Recurrent Q-Network example #85
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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
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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.