def test_len(self):
buf = experience.ExperienceReplayBuffer(self.source, buffer_size=2)
self.assertEqual(0, len(buf))
self.assertEqual([], list(buf))
buf.populate(1)
self.assertEqual(1, len(buf))
buf.populate(2)
self.assertEqual(2, len(buf))
def test_sample(self):
buf = experience.ExperienceReplayBuffer(self.source, buffer_size=10)
buf.populate(10)
b = buf.sample(4)
self.assertEqual(4, len(b))
buf_ids = list(map(id, buf))
check = list(map(lambda v: id(v) in buf_ids, b))
self.assertTrue(all(check))
b = buf.sample(20)
self.assertEqual(10, len(b))
def test_batches(self):
buf = experience.ExperienceReplayBuffer(self.source)
buf.populate(10)
b = list(buf.batches(batch_size=2))
self.assertEqual(5, len(b))
self.assertEqual(2, len(b[0]))
buf.populate(1)
b = list(buf.batches(batch_size=2))
self.assertEqual(5, len(b))
buf.populate(1)
b = list(buf.batches(batch_size=2))
self.assertEqual(6, len(b))
pass
loss_fn = nn.MSELoss(size_average=False)
optimizer = optim.Adam(model.parameters(),
lr=run.getfloat("learning", "lr"))
action_selector = ActionSelectorEpsilonGreedy(epsilon=run.getfloat(
"defaults", "epsilon"),
params=params)
target_net = agent.TargetNet(model)
dqn_agent = agent.DQNAgent(dqn_model=model,
action_selector=action_selector)
exp_source = experience.ExperienceSource(env=env_pool,
agent=dqn_agent,
steps_count=run.getint(
"defaults", "n_steps"))
exp_replay = experience.ExperienceReplayBuffer(exp_source,
buffer_size=run.getint(
"exp_buffer", "size"))
use_target_dqn = run.getboolean("dqn", "target_dqn", fallback=False)
use_double_dqn = run.getboolean("dqn", "double_dqn", fallback=False)
if use_target_dqn:
target_model = target_net.target_model
else:
target_model = model
def batch_to_train(batch):
"""
Convert batch into training data using bellman's equation
:param batch: list of tuples with Experience instances
:return: