def test_interpretation_heatmap():
data = MDPDataBunch.from_env('maze-random-5x5-v0', render='human')
model = DQN(data)
learn = AgentLearner(data, model)
epochs = 10
callbacks = learn.model.callbacks # type: Collection[LearnerCallback]
[c.on_train_begin(learn=learn, n_epochs=epochs) for c in callbacks]
for epoch in range(epochs):
[c.on_epoch_begin(epoch=epoch) for c in callbacks]
learn.model.train()
for element in learn.data.train_dl:
learn.data.train_ds.actions = learn.predict(element)
[c.on_step_end(learn=learn) for c in callbacks]
[c.on_epoch_end() for c in callbacks]
# For now we are going to avoid executing learner_callbacks here.
learn.model.eval()
for element in learn.data.valid_dl:
learn.data.valid_ds.actions = learn.predict(element)
if epoch % 1 == 0:
interp = AgentInterpretationAlpha(learn)
interp.plot_heatmapped_episode(epoch)
[c.on_train_end() for c in callbacks]
def test_interpretation_plot_sequence():
data = MDPDataBunch.from_env('maze-random-5x5-v0',
render='human',
max_steps=1000)
model = DQN(data)
learn = AgentLearner(data, model)
epochs = 20
callbacks = learn.model.callbacks # type: Collection[LearnerCallback]
[c.on_train_begin(learn=learn, n_epochs=epochs) for c in callbacks]
for epoch in range(epochs):
[c.on_epoch_begin(epoch=epoch) for c in callbacks]
learn.model.train()
counter = 0
for element in learn.data.train_dl:
learn.data.train_ds.actions = learn.predict(element)
[c.on_step_end(learn=learn) for c in callbacks]
counter += 1
# if counter % 100 == 0:# or counter == 0:
interp = AgentInterpretationAlpha(learn, ds_type=DatasetType.Train)
interp.plot_heatmapped_episode(epoch)
[c.on_epoch_end() for c in callbacks]
[c.on_train_end() for c in callbacks]