def test_interpretation_plot_q_dqn_returns():
data = MDPDataBunch.from_env('maze-random-5x5-v0', render='human')
model = DQN(data)
learn = AgentLearner(data, model)
learn.fit(5)
interp = AgentInterpretationAlpha(learn)
interp.plot_heatmapped_episode(2)
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]
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_fit_function_dqn():
data = MDPDataBunch.from_env('maze-random-5x5-v0',
render='human',
max_steps=1000)
model = DQN(data, memory=PriorityExperienceReplay(1000))
learn = AgentLearner(data, model)
learn.fit(5)
def test_priority_experience_replay():
data = MDPDataBunch.from_env('maze-random-5x5-v0',
render='human',
max_steps=1000)
model = FixedTargetDQN(data, memory=PriorityExperienceReplay(1000))
learn = AgentLearner(data, model)
learn.fit(5)
def test_double_dueling_dqn_model_maze():
data = MDPDataBunch.from_env('maze-random-5x5-v0',
render='human',
max_steps=1000)
model = DoubleDuelingDQN(data)
learn = AgentLearner(data, model)
learn.fit(5)
def test_basic_dqn_model_maze():
data = MDPDataBunch.from_env('maze-random-5x5-v0',
render='human',
max_steps=200)
model = DQN(data)
learn = AgentLearner(data, model)
learn.fit(5)
def test_fixed_target_dqn_model_maze():
print('\n')
data = MDPDataBunch.from_env('maze-random-5x5-v0',
render='human',
max_steps=1000)
model = FixedTargetDQN(data)
learn = AgentLearner(data, model)
learn.fit(5)
def test_interpretation_plot_q_ddpg_returns():
data = MDPDataBunch.from_env('Pendulum-v0', render='human')
# data = MDPDataBunch.from_env('MountainCarContinuous-v0', render='human')
model = DDPG(data, batch=8)
learn = AgentLearner(data, model)
learn.fit(5)
interp = AgentInterpretationAlpha(learn)
interp.plot_heatmapped_episode(2)
def test_epsilon():
data = MDPDataBunch.from_env('maze-random-5x5-v0',
render='human',
max_steps=100,
add_valid=False)
model = FixedTargetDQN(data,
batch_size=64,
max_episodes=100,
copy_over_frequency=4)
learn = AgentLearner(data, model)
learn.fit(20)
def test_ddpg():
data = MDPDataBunch.from_env('Pendulum-v0', render='human')
# data = MDPDataBunch.from_env('MountainCarContinuous-v0', render='human')
model = DDPG(data, batch=8)
learn = AgentLearner(data, model)
learn.fit(450)
def test_fit_function_ddpg():
data = MDPDataBunch.from_env('Pendulum-v0', render='human', max_steps=1000)
model = DDPG(data, memory=PriorityExperienceReplay(1000))
learn = AgentLearner(data, model)
learn.fit(5)