def test_discrete(self):
passed = 0
for _ in xrange(5):
environment = MinimalTest(definition=False)
config = Configuration(batch_size=8,
learning_rate=0.0005,
memory_capacity=800,
first_update=80,
target_update_frequency=20,
memory=dict(type='replay',
random_sampling=True),
states=environment.states,
actions=environment.actions,
network=layered_network_builder([
dict(type='dense', size=32),
dict(type='dense', size=32)
]))
agent = CategoricalDQNAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 100 or not all(
x / l >= reward_threshold for x, l in zip(
r.episode_rewards[-100:], r.episode_lengths[-100:]))
runner.run(episodes=1000, episode_finished=episode_finished)
print('Categorical DQN agent: ' + str(runner.episode))
if runner.episode < 1000:
passed += 1
print('Categorical DQN agent passed = {}'.format(passed))
self.assertTrue(passed >= 4)
def test_multi(self):
passed = 0
def network_builder(inputs, **kwargs):
layer = layers['dense']
state0 = layer(x=layer(x=inputs['state0'], size=32), size=32)
state1 = layer(x=layer(x=inputs['state1'], size=32), size=32)
return state0 * state1
for _ in xrange(5):
environment = MinimalTest(definition=[False, (False, 2)])
config = Configuration(
batch_size=8,
learning_rate=0.001,
memory_capacity=800,
first_update=80,
target_update_frequency=20,
states=environment.states,
actions=environment.actions,
network=network_builder
)
agent = CategoricalDQNAgent(config=config)
runner = Runner(agent=agent, environment=environment)
def episode_finished(r):
return r.episode < 15 or not all(x / l >= reward_threshold for x, l in zip(r.episode_rewards[-15:], r.episode_lengths[-15:]))
runner.run(episodes=2000, episode_finished=episode_finished)
print('Categorical DQN agent (multi-state/action): ' + str(runner.episode))
if runner.episode < 2000:
passed += 1
print('Categorical DQN agent (multi-state/action) passed = {}'.format(passed))
self.assertTrue(passed >= 2)