samyzaf / tdfmaze Goto Github PK
View Code? Open in Web Editor NEWTour De Flags Maze solved by deep reinforcement learning technique (Q-learning)
Tour De Flags Maze solved by deep reinforcement learning technique (Q-learning)
Hi @samyzaf, do you plan to open source this project? Thanks!
Hello, could you post the code for visualization?
hey @samyzaf can you provide code for visualization (using matplotlib or tkinter canvas).
Hello, and thanks so much for this material. I've got a work problem that seems like an extension of the tour de flags, and I've found your notebooks super helpful as I think through how to tackle it.
I have a bit of an embarrassing questions tho: how do I play a trained model on a new environment? I tried
[ 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1.],
[ 1., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 1.],
[ 1., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 1.],
[ 1., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 1.],
[ 1., 1., 1., 0., 1., 1., 1., 1., 0., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.],
[ 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1.],
])
flags = [(0,4), (0,11), (4,4), (5,9), (11,7), (11,9)]
env_new = Tmaze(maze, flags)
show_env(env_new)
qt_new = Qtraining(
qt.model,
env_new,
#n_epoch = 200,
#max_memory = 500,
#data_size = 100,
#name = 'model_1',
weights_file = 'model_t1.h5'
)
qt_new.play()```
But no luck :/ ('Qtraining' object has no attribute 'env_state')
If you could post an example or two of playing to a new environment from a saved model, I'd appreciate it.
Cheers!
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