class TestExperienceReplayBuffer(unittest.TestCase):
def setUp(self):
np.random.seed(1)
random.seed(1)
torch.manual_seed(1)
self.replay_buffer = ExperienceReplayBuffer(5)
def test_run(self):
states = torch.arange(0, 20)
actions = torch.arange(0, 20)
rewards = torch.arange(0, 20)
expected_samples = torch.tensor([[0, 0, 0], [1, 1, 0], [0, 1, 1],
[3, 0, 0], [1, 4, 4], [1, 2, 4],
[2, 4, 3], [4, 7, 4], [7, 4, 6],
[6, 5, 6]])
expected_weights = np.ones((10, 3))
actual_samples = []
actual_weights = []
for i in range(10):
state = State(states[i].unsqueeze(0), torch.tensor([1]))
next_state = State(states[i + 1].unsqueeze(0), torch.tensor([1]))
self.replay_buffer.store(state, actions[i], rewards[i], next_state)
sample = self.replay_buffer.sample(3)
actual_samples.append(sample[0].features)
actual_weights.append(sample[-1])
tt.assert_equal(
torch.cat(actual_samples).view(expected_samples.shape),
expected_samples)
np.testing.assert_array_equal(expected_weights,
np.vstack(actual_weights))
def agent(self, writer=DummyWriter(), train_steps=float('inf')):
optimizer = Adam(self.model.parameters(),
lr=self.hyperparameters['lr'])
q = QDist(
self.model,
optimizer,
self.n_actions,
self.hyperparameters['atoms'],
v_min=self.hyperparameters['v_min'],
v_max=self.hyperparameters['v_max'],
target=FixedTarget(
self.hyperparameters['target_update_frequency']),
writer=writer,
)
replay_buffer = ExperienceReplayBuffer(
self.hyperparameters['replay_buffer_size'], device=self.device)
return C51(q,
replay_buffer,
exploration=LinearScheduler(
self.hyperparameters['initial_exploration'],
self.hyperparameters['final_exploration'],
0,
self.hyperparameters["final_exploration_step"] -
self.hyperparameters["replay_start_size"],
name="epsilon",
writer=writer,
),
discount_factor=self.hyperparameters["discount_factor"],
minibatch_size=self.hyperparameters["minibatch_size"],
replay_start_size=self.hyperparameters["replay_start_size"],
update_frequency=self.hyperparameters["update_frequency"],
writer=writer)
def _dqn(env, writer=DummyWriter()):
_model = nature_dqn(env).to(device)
_optimizer = Adam(_model.parameters(), lr=lr, eps=eps)
q = QNetwork(_model,
_optimizer,
env.action_space.n,
target=FixedTarget(target_update_frequency),
loss=smooth_l1_loss,
writer=writer)
policy = GreedyPolicy(q,
env.action_space.n,
epsilon=LinearScheduler(initial_exploration,
final_exploration,
replay_start_size,
final_exploration_frame,
name="epsilon",
writer=writer))
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return DeepmindAtariBody(
DQN(
q,
policy,
replay_buffer,
discount_factor=discount_factor,
minibatch_size=minibatch_size,
replay_start_size=replay_start_size,
update_frequency=update_frequency,
), )
def _dqn(env, writer=DummyWriter()):
model = fc_relu_q(env).to(device)
optimizer = Adam(model.parameters(), lr=lr)
q = QNetwork(model,
optimizer,
env.action_space.n,
target=FixedTarget(target_update_frequency),
loss=mse_loss,
writer=writer)
policy = GreedyPolicy(q,
env.action_space.n,
epsilon=LinearScheduler(initial_exploration,
final_exploration,
replay_start_size,
final_exploration_frame,
name="epsilon",
writer=writer))
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return DQN(q,
policy,
replay_buffer,
discount_factor=discount_factor,
replay_start_size=replay_start_size,
update_frequency=update_frequency,
minibatch_size=minibatch_size)
def _c51(env, writer=DummyWriter()):
model = fc_relu_dist_q(env, atoms=atoms).to(device)
optimizer = Adam(model.parameters(), lr=lr)
q = QDist(
model,
optimizer,
env.action_space.n,
atoms,
v_min=v_min,
v_max=v_max,
writer=writer,
)
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return C51(q,
replay_buffer,
exploration=LinearScheduler(
initial_exploration,
final_exploration,
replay_start_size,
final_exploration_frame,
name="epsilon",
writer=writer,
),
discount_factor=discount_factor,
minibatch_size=minibatch_size,
replay_start_size=replay_start_size,
update_frequency=update_frequency,
writer=writer)
def test_store_device(self):
if torch.cuda.is_available():
self.replay_buffer = ExperienceReplayBuffer(5,
device='cuda',
store_device='cpu')
states = torch.arange(0, 20).to('cuda')
actions = torch.arange(0, 20).view((-1, 1)).to('cuda')
rewards = torch.arange(0, 20).to('cuda')
state = State(states[0])
next_state = State(states[1], reward=rewards[1])
self.replay_buffer.store(state, actions[0], next_state)
sample = self.replay_buffer.sample(3)
self.assertEqual(sample[0].device, torch.device('cuda'))
self.assertEqual(self.replay_buffer.buffer[0][0].device,
torch.device('cpu'))
def _dqn(env, writer=DummyWriter()):
_model = model
_optimizer = optimizer
if _model is None:
_model = conv_net(env, frames=agent_history_length).to(device)
if _optimizer is None:
_optimizer = Adam(_model.parameters(), lr=lr, eps=eps)
q = QNetwork(_model,
_optimizer,
env.action_space.n,
target_update_frequency=target_update_frequency,
loss=smooth_l1_loss,
writer=writer)
policy = GreedyPolicy(q,
env.action_space.n,
annealing_start=replay_start_size,
annealing_time=final_exploration_frame -
replay_start_size,
initial_epsilon=initial_exploration,
final_epsilon=final_exploration)
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return DeepmindAtariBody(DQN(
q,
policy,
replay_buffer,
discount_factor=discount_factor,
minibatch_size=minibatch_size,
replay_start_size=replay_start_size,
update_frequency=update_frequency,
),
env,
action_repeat=action_repeat,
frame_stack=agent_history_length,
noop_max=noop_max)
def _ddqn(env, writer=DummyWriter()):
action_repeat = 1
last_timestep = last_frame / action_repeat
last_update = (last_timestep - replay_start_size) / update_frequency
final_exploration_step = final_exploration_frame / action_repeat
model = model_constructor(env).to(device)
optimizer = Adam(model.parameters(), lr=lr, eps=eps)
q = QNetwork(model,
optimizer,
scheduler=CosineAnnealingLR(optimizer, last_update),
target=FixedTarget(target_update_frequency),
writer=writer)
policy = SharedAutonomyPolicy(q,
env.action_space.n,
epsilon=0,
pilot_tol=pilot_tol)
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(replay_buffer_size,
alpha=alpha,
beta=beta,
device=device)
else:
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return co_DDQN(q,
policy,
replay_buffer,
loss=weighted_smooth_l1_loss,
discount_factor=discount_factor,
minibatch_size=minibatch_size,
replay_start_size=replay_start_size,
update_frequency=update_frequency)
def agent(self, writer=DummyWriter(), train_steps=float('inf')):
optimizer = Adam(self.model.parameters(),
lr=self.hyperparameters['lr'])
q = QNetwork(self.model,
optimizer,
target=FixedTarget(
self.hyperparameters['target_update_frequency']),
writer=writer)
policy = GreedyPolicy(
q,
self.n_actions,
epsilon=LinearScheduler(
self.hyperparameters['initial_exploration'],
self.hyperparameters['final_exploration'],
self.hyperparameters['replay_start_size'],
self.hyperparameters['final_exploration_step'] -
self.hyperparameters['replay_start_size'],
name="exploration",
writer=writer))
replay_buffer = ExperienceReplayBuffer(
self.hyperparameters['replay_buffer_size'], device=self.device)
return DQN(
q,
policy,
replay_buffer,
discount_factor=self.hyperparameters['discount_factor'],
minibatch_size=self.hyperparameters['minibatch_size'],
replay_start_size=self.hyperparameters['replay_start_size'],
update_frequency=self.hyperparameters['update_frequency'],
)
def _model_predictive_dqn(env, writer=None):
# models
feature_model = shared_feature_layers().to(device)
value_model = value_head().to(device)
reward_model = reward_head(env).to(device)
generator_model = Generator(env).to(device)
# optimizers
feature_optimizer = Adam(feature_model.parameters(), lr=lr, eps=eps)
value_optimizer = Adam(value_model.parameters(), lr=lr, eps=eps)
reward_optimizer = Adam(reward_model.parameters(), lr=lr, eps=eps)
generator_optimizer = Adam(generator_model.parameters(), lr=lr, eps=eps)
# approximators
f = FeatureNetwork(feature_model, feature_optimizer, writer=writer)
v = VNetwork(value_model, value_optimizer, writer=writer)
r = QNetwork(reward_model, reward_optimizer, name='reward', writer=writer)
g = Approximation(generator_model, generator_optimizer, name='generator', writer=writer)
# replay buffer
replay_buffer = ExperienceReplayBuffer(replay_buffer_size, device=device)
# create agent
agent = ModelPredictiveDQN(f, v, r, g, replay_buffer,
minibatch_size=minibatch_size,
replay_start_size=replay_start_size
)
# apply agent wrappers for better atari performance
return DeepmindAtariBody(agent, lazy_frames=True)
def _ddpg(env, writer=DummyWriter()):
value_model = fc_value(env).to(device)
value_optimizer = Adam(value_model.parameters(), lr=lr_q)
q = QContinuous(value_model,
value_optimizer,
target=PolyakTarget(polyak_rate),
writer=writer)
policy_model = fc_policy(env).to(device)
policy_optimizer = Adam(policy_model.parameters(), lr=lr_pi)
policy = DeterministicPolicy(policy_model,
policy_optimizer,
env.action_space,
noise,
target=PolyakTarget(polyak_rate),
writer=writer)
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return DDPG(q,
policy,
replay_buffer,
replay_start_size=replay_start_size,
discount_factor=discount_factor,
update_frequency=update_frequency,
minibatch_size=minibatch_size)
def _dqn(env, writer=DummyWriter()):
action_repeat = 4
last_timestep = last_frame / action_repeat
last_update = (last_timestep - replay_start_size) / update_frequency
final_exploration_step = final_exploration_frame / action_repeat
model = nature_dqn(env).to(device)
optimizer = Adam(
model.parameters(),
lr=lr,
eps=eps
)
q = QNetwork(
model,
optimizer,
scheduler=CosineAnnealingLR(optimizer, last_update),
target=FixedTarget(target_update_frequency),
writer=writer
)
policy = GreedyPolicy(
q,
env.action_space.n,
epsilon=LinearScheduler(
initial_exploration,
final_exploration,
replay_start_size,
final_exploration_step - replay_start_size,
name="epsilon",
writer=writer
)
)
replay_buffer = ExperienceReplayBuffer(
replay_buffer_size,
device=device
)
return DeepmindAtariBody(
DQN(
q,
policy,
replay_buffer,
discount_factor=discount_factor,
loss=smooth_l1_loss,
minibatch_size=minibatch_size,
replay_start_size=replay_start_size,
update_frequency=update_frequency,
),
lazy_frames=True
)
def test_run(self):
np.random.seed(1)
random.seed(1)
torch.manual_seed(1)
self.replay_buffer = ExperienceReplayBuffer(5)
states = torch.arange(0, 20)
actions = torch.arange(0, 20).view((-1, 1))
rewards = torch.arange(0, 20)
expected_samples = torch.tensor([
[0, 0, 0],
[1, 1, 0],
[0, 1, 1],
[3, 0, 0],
[1, 4, 4],
[1, 2, 4],
[2, 4, 3],
[4, 7, 4],
[7, 4, 6],
[6, 5, 6],
])
expected_weights = np.ones((10, 3))
actual_samples = []
actual_weights = []
for i in range(10):
state = State(states[i])
next_state = State(states[i + 1], reward=rewards[i])
self.replay_buffer.store(state, actions[i], next_state)
sample = self.replay_buffer.sample(3)
actual_samples.append(sample[0].observation)
actual_weights.append(sample[-1])
tt.assert_equal(
torch.cat(actual_samples).view(expected_samples.shape),
expected_samples)
np.testing.assert_array_equal(expected_weights,
np.vstack(actual_weights))
def agent(self, writer=DummyWriter(), train_steps=float('inf')):
n_updates = (train_steps - self.hyperparameters['replay_start_size']) / self.hyperparameters['update_frequency']
optimizer = Adam(
self.model.parameters(),
lr=self.hyperparameters['lr'],
eps=self.hyperparameters['eps']
)
q = QNetwork(
self.model,
optimizer,
scheduler=CosineAnnealingLR(optimizer, n_updates),
target=FixedTarget(self.hyperparameters['target_update_frequency']),
writer=writer
)
policy = GreedyPolicy(
q,
self.n_actions,
epsilon=LinearScheduler(
self.hyperparameters['initial_exploration'],
self.hyperparameters['final_exploration'],
self.hyperparameters['replay_start_size'],
self.hyperparameters['final_exploration_step'] - self.hyperparameters['replay_start_size'],
name="exploration",
writer=writer
)
)
replay_buffer = ExperienceReplayBuffer(
self.hyperparameters['replay_buffer_size'],
device=self.device
)
return DeepmindAtariBody(
DQN(
q,
policy,
replay_buffer,
discount_factor=self.hyperparameters['discount_factor'],
loss=smooth_l1_loss,
minibatch_size=self.hyperparameters['minibatch_size'],
replay_start_size=self.hyperparameters['replay_start_size'],
update_frequency=self.hyperparameters['update_frequency'],
),
lazy_frames=True
)
def agent(self, writer=DummyWriter(), train_steps=float('inf')):
n_updates = (train_steps - self.hyperparameters['replay_start_size']) / self.hyperparameters['update_frequency']
optimizer = Adam(
self.model.parameters(),
lr=self.hyperparameters['lr'],
eps=self.hyperparameters['eps']
)
q = QDist(
self.model,
optimizer,
self.n_actions,
self.hyperparameters['atoms'],
v_min=self.hyperparameters['v_min'],
v_max=self.hyperparameters['v_max'],
target=FixedTarget(self.hyperparameters['target_update_frequency']),
scheduler=CosineAnnealingLR(optimizer, n_updates),
writer=writer,
)
replay_buffer = ExperienceReplayBuffer(
self.hyperparameters['replay_buffer_size'],
device=self.device
)
return DeepmindAtariBody(
C51(
q,
replay_buffer,
exploration=LinearScheduler(
self.hyperparameters['initial_exploration'],
self.hyperparameters['final_exploration'],
0,
self.hyperparameters["final_exploration_step"] - self.hyperparameters["replay_start_size"],
name="epsilon",
writer=writer,
),
discount_factor=self.hyperparameters["discount_factor"],
minibatch_size=self.hyperparameters["minibatch_size"],
replay_start_size=self.hyperparameters["replay_start_size"],
update_frequency=self.hyperparameters["update_frequency"],
writer=writer
),
lazy_frames=True,
episodic_lives=True
)
class TestExperienceReplayBuffer(unittest.TestCase):
def test_run(self):
np.random.seed(1)
random.seed(1)
torch.manual_seed(1)
self.replay_buffer = ExperienceReplayBuffer(5)
states = torch.arange(0, 20)
actions = torch.arange(0, 20).view((-1, 1))
rewards = torch.arange(0, 20)
expected_samples = torch.tensor([
[0, 0, 0],
[1, 1, 0],
[0, 1, 1],
[3, 0, 0],
[1, 4, 4],
[1, 2, 4],
[2, 4, 3],
[4, 7, 4],
[7, 4, 6],
[6, 5, 6],
])
expected_weights = np.ones((10, 3))
actual_samples = []
actual_weights = []
for i in range(10):
state = State(states[i])
next_state = State(states[i + 1], reward=rewards[i])
self.replay_buffer.store(state, actions[i], next_state)
sample = self.replay_buffer.sample(3)
actual_samples.append(sample[0].observation)
actual_weights.append(sample[-1])
tt.assert_equal(
torch.cat(actual_samples).view(expected_samples.shape),
expected_samples)
np.testing.assert_array_equal(expected_weights,
np.vstack(actual_weights))
def test_store_device(self):
if torch.cuda.is_available():
self.replay_buffer = ExperienceReplayBuffer(5,
device='cuda',
store_device='cpu')
states = torch.arange(0, 20).to('cuda')
actions = torch.arange(0, 20).view((-1, 1)).to('cuda')
rewards = torch.arange(0, 20).to('cuda')
state = State(states[0])
next_state = State(states[1], reward=rewards[1])
self.replay_buffer.store(state, actions[0], next_state)
sample = self.replay_buffer.sample(3)
self.assertEqual(sample[0].device, torch.device('cuda'))
self.assertEqual(self.replay_buffer.buffer[0][0].device,
torch.device('cpu'))
def _c51(env, writer=DummyWriter()):
action_repeat = 4
last_timestep = last_frame / action_repeat
last_update = (last_timestep - replay_start_size) / update_frequency
model = nature_c51(env, atoms=atoms).to(device)
optimizer = Adam(
model.parameters(),
lr=lr,
eps=eps
)
q = QDist(
model,
optimizer,
env.action_space.n,
atoms,
v_min=v_min,
v_max=v_max,
target=FixedTarget(target_update_frequency),
scheduler=CosineAnnealingLR(optimizer, last_update),
writer=writer,
)
replay_buffer = ExperienceReplayBuffer(
replay_buffer_size,
device=device
)
return DeepmindAtariBody(
C51(
q,
replay_buffer,
exploration=LinearScheduler(
initial_exploration,
final_exploration,
0,
last_timestep,
name="epsilon",
writer=writer,
),
discount_factor=discount_factor,
minibatch_size=minibatch_size,
replay_start_size=replay_start_size,
update_frequency=update_frequency,
writer=writer
),
lazy_frames=True
)
def _online_cacla(env, writer=DummyWriter()):
value_model = models.critic(env, hidden1=hidden1,
hidden2=hidden2).to(device)
policy_model = models.actor(env, hidden1=hidden1,
hidden2=hidden2).to(device)
# feature_model = models.features(env.state_space.shape[0]).to(device)
value_optimizer = Adam(value_model.parameters(), lr=lr_v, eps=eps)
policy_optimizer = Adam(policy_model.parameters(), lr=lr_pi, eps=eps)
# feature_optimizer = Adam(feature_model.parameters(), lr=lr_pi, eps=eps)
# feature_optimizer = SGD(feature_model.parameters(), lr=lr_pi, momentum=0.9)
policy = DeterministicPolicy(
policy_model,
policy_optimizer,
env.action_space,
quiet=not log,
clip_grad=1.0,
writer=writer,
normalise_inputs=True,
box=env.state_space,
)
v = VNetwork(
value_model,
value_optimizer,
quiet=not log,
writer=writer,
normalise_inputs=True,
box=env.state_space,
)
features = None # FeatureNetwork(feature_model, feature_optimizer, writer=writer, normalize_input=False)
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
# TODO - reintroduce TimeFeature wrapper
return OnlineCACLA(features,
v,
policy,
replay_buffer,
env.action_space,
log=log,
writer=writer,
discount_factor=discount_factor)
def _sac(env, writer=DummyWriter()):
q_1_model = fc_q(env).to(device)
q_1_optimizer = Adam(q_1_model.parameters(), lr=lr_q)
q_1 = QContinuous(q_1_model, q_1_optimizer, writer=writer, name='q_1')
q_2_model = fc_q(env).to(device)
q_2_optimizer = Adam(q_2_model.parameters(), lr=lr_q)
q_2 = QContinuous(q_2_model, q_2_optimizer, writer=writer, name='q_2')
v_model = fc_v(env).to(device)
v_optimizer = Adam(v_model.parameters(), lr=lr_v)
v = VNetwork(
v_model,
v_optimizer,
target=PolyakTarget(polyak_rate),
writer=writer,
name='v',
)
policy_model = fc_policy(env).to(device)
policy_optimizer = Adam(policy_model.parameters(), lr=lr_pi)
policy = SoftDeterministicPolicy(policy_model,
policy_optimizer,
env.action_space,
writer=writer)
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return SAC(policy,
q_1,
q_2,
v,
replay_buffer,
entropy_target=(-env.action_space.shape[0] *
entropy_target_scaling),
lr_temperature=lr_temperature,
replay_start_size=replay_start_size,
discount_factor=discount_factor,
update_frequency=update_frequency,
minibatch_size=minibatch_size,
writer=writer)
def _fac(env, writer=DummyWriter()):
value_model = models.critic(env, hidden1=hidden1,
hidden2=hidden2).to(device)
policy_model = models.actor(env, hidden1=hidden1,
hidden2=hidden2).to(device)
value_optimizer = Adam(value_model.parameters(), lr=lr_v, eps=eps)
policy_optimizer = Adam(policy_model.parameters(), lr=lr_pi, eps=eps)
policy = DeterministicPolicy(
policy_model,
policy_optimizer,
env.action_space,
quiet=not log,
clip_grad=1.0,
writer=writer,
normalise_inputs=True,
box=env.state_space,
)
v = VNetwork(
value_model,
value_optimizer,
quiet=not log,
writer=writer,
normalise_inputs=True,
box=env.state_space,
)
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
# TODO - reintroduce TimeFeature wrapper
return ForwardAC(v,
policy,
replay_buffer,
env.action_space,
log=log,
trace_decay=trace_decay,
writer=writer,
discount_factor=discount_factor)
def _c51(env, writer=DummyWriter()):
model = nature_c51(env, atoms=51).to(device)
optimizer = Adam(
model.parameters(),
lr=lr,
eps=eps
)
q = QDist(
model,
optimizer,
env.action_space.n,
atoms,
v_min=v_min,
v_max=v_max,
target=FixedTarget(target_update_frequency),
writer=writer,
)
replay_buffer = ExperienceReplayBuffer(
replay_buffer_size,
device=device
)
return DeepmindAtariBody(
C51(
q,
replay_buffer,
exploration=LinearScheduler(
initial_exploration,
final_exploration,
replay_start_size,
final_exploration_frame,
name="epsilon",
writer=writer,
),
discount_factor=discount_factor,
minibatch_size=minibatch_size,
replay_start_size=replay_start_size,
update_frequency=update_frequency,
writer=writer
)
)
def agent(self, writer=DummyWriter(), train_steps=float('inf')):
n_updates = (train_steps - self.hyperparameters["replay_start_size"]
) / self.hyperparameters["update_frequency"]
q_optimizer = Adam(self.q_model.parameters(),
lr=self.hyperparameters["lr_q"])
q = QContinuous(self.q_model,
q_optimizer,
target=PolyakTarget(
self.hyperparameters["polyak_rate"]),
scheduler=CosineAnnealingLR(q_optimizer, n_updates),
writer=writer)
policy_optimizer = Adam(self.policy_model.parameters(),
lr=self.hyperparameters["lr_pi"])
policy = DeterministicPolicy(
self.policy_model,
policy_optimizer,
self.action_space,
target=PolyakTarget(self.hyperparameters["polyak_rate"]),
scheduler=CosineAnnealingLR(policy_optimizer, n_updates),
writer=writer)
replay_buffer = ExperienceReplayBuffer(
self.hyperparameters["replay_buffer_size"], device=self.device)
return TimeFeature(
DDPG(
q,
policy,
replay_buffer,
self.action_space,
noise=self.hyperparameters["noise"],
replay_start_size=self.hyperparameters["replay_start_size"],
discount_factor=self.hyperparameters["discount_factor"],
update_frequency=self.hyperparameters["update_frequency"],
minibatch_size=self.hyperparameters["minibatch_size"],
))
def _ddpg(env, writer=DummyWriter()):
final_anneal_step = (last_frame -
replay_start_size) // update_frequency
q_model = fc_q(env).to(device)
q_optimizer = Adam(q_model.parameters(), lr=lr_q)
q = QContinuous(q_model,
q_optimizer,
target=PolyakTarget(polyak_rate),
scheduler=CosineAnnealingLR(q_optimizer,
final_anneal_step),
writer=writer)
policy_model = fc_deterministic_policy(env).to(device)
policy_optimizer = Adam(policy_model.parameters(), lr=lr_pi)
policy = DeterministicPolicy(policy_model,
policy_optimizer,
env.action_space,
target=PolyakTarget(polyak_rate),
scheduler=CosineAnnealingLR(
policy_optimizer, final_anneal_step),
writer=writer)
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return TimeFeature(
DDPG(
q,
policy,
replay_buffer,
env.action_space,
noise=noise,
replay_start_size=replay_start_size,
discount_factor=discount_factor,
update_frequency=update_frequency,
minibatch_size=minibatch_size,
))
def _dqn(env, writer=DummyWriter()):
model = build_model(env).to(device)
optimizer = Adam(model.parameters(), lr=lr)
q = QNetwork(model,
optimizer,
env.action_space.n,
target_update_frequency=target_update_frequency,
loss=mse_loss,
writer=writer)
policy = GreedyPolicy(q,
env.action_space.n,
initial_epsilon=initial_exploration,
final_epsilon=final_exploration,
annealing_time=final_exploration_frame)
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return DQN(q,
policy,
replay_buffer,
discount_factor=discount_factor,
replay_start_size=replay_start_size,
update_frequency=update_frequency,
minibatch_size=minibatch_size)
def agent(self, writer=DummyWriter(), train_steps=float("inf")):
# optimizers
feature_optimizer = Adam(self.feature_model.parameters(), lr=self.hyperparameters["lr"], eps=self.hyperparameters["eps"])
value_optimizer = Adam(self.value_model.parameters(), lr=self.hyperparameters["lr"], eps=self.hyperparameters["eps"])
reward_optimizer = Adam(self.reward_model.parameters(), lr=self.hyperparameters["lr"], eps=self.hyperparameters["eps"])
generator_optimizer = Adam(self.generator_model.parameters(), lr=self.hyperparameters["lr"], eps=self.hyperparameters["eps"])
# approximators
f = FeatureNetwork(self.feature_model, feature_optimizer, writer=writer)
v = VNetwork(self.value_model, value_optimizer, writer=writer)
r = QNetwork(self.reward_model, reward_optimizer, name="reward", writer=writer)
g = Approximation(self.generator_model, generator_optimizer, name="generator", writer=writer)
# replay buffer
replay_buffer = ExperienceReplayBuffer(self.hyperparameters["replay_buffer_size"], device=self.device)
# create agent
agent = ModelBasedDQN(f, v, r, g, replay_buffer,
minibatch_size=self.hyperparameters["minibatch_size"],
replay_start_size=self.hyperparameters["replay_start_size"]
)
# apply atari wrappers for better performance
return DeepmindAtariBody(agent, lazy_frames=True)
def _dqn(env, writers=None):
action_repeat = 4
last_timestep = last_frame / action_repeat
last_update = (last_timestep - replay_start_size) / update_frequency
final_exploration_step = final_exploration_frame / action_repeat
n_agents = len(env.agents)
n_actions = env.action_spaces['first_0'].n
model = model_constructor(env).to(device)
optimizer = Adam(
model.parameters(),
lr=lr,
eps=eps
)
q = Approximation(
model,
optimizer,
scheduler=CosineAnnealingLR(optimizer, last_update),
target=FixedTarget(target_update_frequency),
writer=writers['first_0']
)
replay_buffer = ExperienceReplayBuffer(
replay_buffer_size,
store_device=device,
device=device
)
def agent_constructor(writer):
policy = GreedyPolicy(
q,
n_actions,
epsilon=LinearScheduler(
initial_exploration,
final_exploration,
replay_start_size,
final_exploration_step - replay_start_size,
name="epsilon",
writer=writer
)
)
return DeepmindAtariBody(
DQN(
q,
policy,
replay_buffer,
discount_factor=discount_factor,
loss=smooth_l1_loss,
minibatch_size=minibatch_size,
replay_start_size=replay_start_size,
update_frequency=update_frequency,
),
lazy_frames=True
)
return MultiagentEncoder(IndependentMultiagent({
agent : agent_constructor(writers[agent])
for agent in env.agents
}), env.agents, device)
def _sac(env, writer=DummyWriter()):
final_anneal_step = (last_frame -
replay_start_size) // update_frequency
v_model = v_model_constructor(env).to(device)
q_1_model = q1_model_constructor(env).to(device)
q_2_model = q2_model_constructor(env).to(device)
#quick and dirty implementation of parallel branch un/freeze
policy_model = policy_model_constructor(
env=env, train_parallel=train_parallel).to(device)
if pretrained_models is not None:
q_1_model = pretrained_models.q_1.model.to(device)
q_2_model = pretrained_models.q_2.model.to(device)
v_model = pretrained_models.v.model.to(device)
policy_model = pretrained_models.policy.model.to(device)
q_1_optimizer = Adam(q_1_model.parameters(), lr=lr_q)
q_1 = QContinuousCtrlRep(q_1_model,
q_1_optimizer,
scheduler=CosineAnnealingLR(
q_1_optimizer, final_anneal_step),
target=FixedTarget(1000),
writer=writer,
name='q_1')
q_2_optimizer = Adam(q_2_model.parameters(), lr=lr_q)
q_2 = QContinuousCtrlRep(q_2_model,
q_2_optimizer,
scheduler=CosineAnnealingLR(
q_2_optimizer, final_anneal_step),
target=FixedTarget(1000),
writer=writer,
name='q_2')
v_optimizer = Adam(v_model.parameters(), lr=lr_v)
v = VNetworkCtrlRep(
v_model,
v_optimizer,
scheduler=CosineAnnealingLR(v_optimizer, final_anneal_step),
target=PolyakTarget(polyak_rate),
writer=writer,
name='v',
)
policy_optimizer = Adam(filter(lambda p: p.requires_grad,
policy_model.parameters()),
lr=lr_pi)
policy = SoftDeterministicPolicyCtrlRep(policy_model,
policy_optimizer,
env.action_space,
scheduler=CosineAnnealingLR(
policy_optimizer,
final_anneal_step),
target=FixedTarget(1000),
writer=writer)
replay_buffer = ExperienceReplayBuffer(replay_buffer_size,
device=device)
return TimeFeature(
SACCtrlRep(policy=policy,
q_1=q_1,
q_2=q_2,
v=v,
replay_buffer=replay_buffer,
temperature_initial=temperature_initial,
entropy_target=(-env.action_space.shape[0] *
entropy_target_scaling),
lr_temperature=lr_temperature,
replay_start_size=replay_start_size,
discount_factor=discount_factor,
update_frequency=update_frequency,
minibatch_size=minibatch_size,
writer=writer))
def agent(self, writer=DummyWriter(), train_steps=float('inf')):
n_updates = (train_steps - self.hyperparameters["replay_start_size"]
) / self.hyperparameters["update_frequency"]
q_1_optimizer = Adam(self.q_1_model.parameters(),
lr=self.hyperparameters["lr_q"])
q_1 = QContinuous(self.q_1_model,
q_1_optimizer,
scheduler=CosineAnnealingLR(q_1_optimizer,
n_updates),
writer=writer,
name='q_1')
q_2_optimizer = Adam(self.q_2_model.parameters(),
lr=self.hyperparameters["lr_q"])
q_2 = QContinuous(self.q_2_model,
q_2_optimizer,
scheduler=CosineAnnealingLR(q_2_optimizer,
n_updates),
writer=writer,
name='q_2')
v_optimizer = Adam(self.v_model.parameters(),
lr=self.hyperparameters["lr_v"])
v = VNetwork(
self.v_model,
v_optimizer,
scheduler=CosineAnnealingLR(v_optimizer, n_updates),
target=PolyakTarget(self.hyperparameters["polyak_rate"]),
writer=writer,
name='v',
)
policy_optimizer = Adam(self.policy_model.parameters(),
lr=self.hyperparameters["lr_pi"])
policy = SoftDeterministicPolicy(self.policy_model,
policy_optimizer,
self.action_space,
scheduler=CosineAnnealingLR(
policy_optimizer, n_updates),
writer=writer)
replay_buffer = ExperienceReplayBuffer(
self.hyperparameters["replay_buffer_size"], device=self.device)
return TimeFeature(
SAC(policy,
q_1,
q_2,
v,
replay_buffer,
temperature_initial=self.
hyperparameters["temperature_initial"],
entropy_target=(
-self.action_space.shape[0] *
self.hyperparameters["entropy_target_scaling"]),
lr_temperature=self.hyperparameters["lr_temperature"],
replay_start_size=self.hyperparameters["replay_start_size"],
discount_factor=self.hyperparameters["discount_factor"],
update_frequency=self.hyperparameters["update_frequency"],
minibatch_size=self.hyperparameters["minibatch_size"],
writer=writer))
def setUp(self):
np.random.seed(1)
random.seed(1)
torch.manual_seed(1)
self.replay_buffer = ExperienceReplayBuffer(5)
def setUp(self):
np.random.seed(1)
random.seed(1)
torch.manual_seed(1)
self.replay_buffer = NStepReplayBuffer(4, 0.5,
ExperienceReplayBuffer(100))