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
)
def test_agent(self):
q_dist = QDist(
copy.deepcopy(self.model),
None,
self.n_actions,
self.hyperparameters['atoms'],
v_min=self.hyperparameters['v_min'],
v_max=self.hyperparameters['v_max'],
)
return DeepmindAtariBody(C51TestAgent(q_dist, self.n_actions, self.hyperparameters["test_exploration"]))
c51 = PresetBuilder('c51', default_hyperparameters, C51AtariPreset)
clip_grad=self.hyperparameters["clip_grad"],
writer=writer)
v = VNetwork(self.value_model,
value_optimizer,
loss_scaling=self.hyperparameters["value_loss_scaling"],
clip_grad=self.hyperparameters["clip_grad"],
writer=writer)
policy = SoftmaxPolicy(self.policy_model,
policy_optimizer,
clip_grad=self.hyperparameters["clip_grad"],
writer=writer)
return VPG(features,
v,
policy,
discount_factor=self.hyperparameters["discount_factor"],
min_batch_size=self.hyperparameters["min_batch_size"])
def test_agent(self):
features = FeatureNetwork(copy.deepcopy(self.feature_model))
policy = SoftmaxPolicy(copy.deepcopy(self.policy_model))
return VPGTestAgent(features, policy)
def parallel_test_agent(self):
return self.test_agent()
vpg = PresetBuilder('vpg', default_hyperparameters, VPGClassicControlPreset)
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 test_agent(self):
q = QNetwork(copy.deepcopy(self.model))
policy = GreedyPolicy(q,
self.n_actions,
epsilon=self.hyperparameters['test_exploration'])
return DQNTestAgent(policy)
dqn = PresetBuilder('dqn', default_hyperparameters, DQNClassicControlPreset)
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 test_agent(self):
policy = SoftDeterministicPolicy(copy.deepcopy(self.policy_model),
space=self.action_space)
return TimeFeature(SACTestAgent(policy))
sac = PresetBuilder('sac', default_hyperparameters, SACContinuousPreset)
name="exploration",
writer=writer))
replay_buffer = PrioritizedReplayBuffer(
self.hyperparameters['replay_buffer_size'],
alpha=self.hyperparameters['alpha'],
beta=self.hyperparameters['beta'],
device=self.device)
return DeepmindAtariBody(DDQN(
q,
policy,
replay_buffer,
loss=weighted_smooth_l1_loss,
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"],
),
lazy_frames=True)
def test_agent(self):
q = QNetwork(copy.deepcopy(self.model))
policy = GreedyPolicy(q,
self.n_actions,
epsilon=self.hyperparameters['test_exploration'])
return DeepmindAtariBody(DDQNTestAgent(policy))
ddqn = PresetBuilder('ddqn', default_hyperparameters, DDQNAtariPreset)
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 test_agent(self):
policy = DeterministicPolicy(
copy.deepcopy(self.policy_model),
None,
self.action_space,
)
return TimeFeature(DDPGTestAgent(policy))
ddpg = PresetBuilder('ddpg', default_hyperparameters, DDPGContinuousPreset)
)
v = VNetwork(
self.value_model,
value_optimizer,
scheduler=CosineAnnealingLR(value_optimizer, n_updates),
loss_scaling=self.hyperparameters["value_loss_scaling"],
clip_grad=self.hyperparameters["clip_grad"],
writer=writer
)
policy = SoftmaxPolicy(
self.policy_model,
policy_optimizer,
scheduler=CosineAnnealingLR(policy_optimizer, n_updates),
clip_grad=self.hyperparameters["clip_grad"],
writer=writer
)
return DeepmindAtariBody(
VPG(features, v, policy, discount_factor=self.hyperparameters["discount_factor"], min_batch_size=self.hyperparameters["min_batch_size"]),
)
def test_agent(self):
features = FeatureNetwork(copy.deepcopy(self.feature_model))
policy = SoftmaxPolicy(copy.deepcopy(self.policy_model))
return DeepmindAtariBody(VPGTestAgent(features, policy))
vpg = PresetBuilder('vpg', default_hyperparameters, VPGAtariPreset)
q_dist,
replay_buffer,
exploration=LinearScheduler(
self.hyperparameters['initial_exploration'],
self.hyperparameters['final_exploration'],
0,
train_steps - self.hyperparameters['replay_start_size'],
name="exploration",
writer=writer
),
discount_factor=self.hyperparameters['discount_factor'] ** self.hyperparameters["n_steps"],
minibatch_size=self.hyperparameters['minibatch_size'],
replay_start_size=self.hyperparameters['replay_start_size'],
update_frequency=self.hyperparameters['update_frequency'],
writer=writer,
)
def test_agent(self):
q_dist = QDist(
copy.deepcopy(self.model),
None,
self.n_actions,
self.hyperparameters['atoms'],
v_min=self.hyperparameters['v_min'],
v_max=self.hyperparameters['v_max'],
)
return RainbowTestAgent(q_dist, self.n_actions, self.hyperparameters["test_exploration"])
rainbow = PresetBuilder('rainbow', default_hyperparameters, RainbowClassicControlPreset)
self.hyperparameters['final_exploration'],
0,
train_steps - self.hyperparameters['replay_start_size'],
name="exploration",
writer=writer),
discount_factor=self.hyperparameters['discount_factor']**
self.hyperparameters["n_steps"],
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)
def test_agent(self):
q_dist = QDist(
copy.deepcopy(self.model),
None,
self.n_actions,
self.hyperparameters['atoms'],
v_min=self.hyperparameters['v_min'],
v_max=self.hyperparameters['v_max'],
)
return DeepmindAtariBody(
RainbowTestAgent(q_dist, self.n_actions,
self.hyperparameters["test_exploration"]))
rainbow = PresetBuilder('rainbow', default_hyperparameters, RainbowAtariPreset)
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 test_agent(self):
q_dist = QDist(
copy.deepcopy(self.model),
None,
self.n_actions,
self.hyperparameters['atoms'],
v_min=self.hyperparameters['v_min'],
v_max=self.hyperparameters['v_max'],
)
return C51TestAgent(q_dist, self.n_actions,
self.hyperparameters["test_exploration"])
c51 = PresetBuilder('c51', default_hyperparameters, C51ClassicControlPreset)
