def agent(self, writer=DummyWriter(), train_steps=float('inf')):
feature_optimizer = Adam(self.feature_model.parameters(),
lr=self.hyperparameters["lr_pi"],
eps=self.hyperparameters["eps"])
value_optimizer = Adam(self.value_model.parameters(),
lr=self.hyperparameters["lr_v"],
eps=self.hyperparameters["eps"])
policy_optimizer = Adam(self.policy_model.parameters(),
lr=self.hyperparameters["lr_pi"],
eps=self.hyperparameters["eps"])
features = FeatureNetwork(self.feature_model,
feature_optimizer,
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 agent(self, writer=DummyWriter(), train_steps=float('inf')):
n_updates = train_steps / self.hyperparameters["min_batch_size"]
feature_optimizer = Adam(self.feature_model.parameters(), lr=self.hyperparameters["lr_pi"], eps=self.hyperparameters["eps"])
value_optimizer = Adam(self.value_model.parameters(), lr=self.hyperparameters["lr_v"], eps=self.hyperparameters["eps"])
policy_optimizer = Adam(self.policy_model.parameters(), lr=self.hyperparameters["lr_pi"], eps=self.hyperparameters["eps"])
features = FeatureNetwork(
self.feature_model,
feature_optimizer,
scheduler=CosineAnnealingLR(feature_optimizer, n_updates),
clip_grad=self.hyperparameters["clip_grad"],
writer=writer
)
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 agent(self, writer=DummyWriter(), train_steps=float('inf')):
feature_optimizer = Adam(self.feature_model.parameters(),
lr=self.hyperparameters["lr"])
value_optimizer = Adam(self.value_model.parameters(),
lr=self.hyperparameters["lr"])
policy_optimizer = Adam(self.policy_model.parameters(),
lr=self.hyperparameters["lr"])
features = FeatureNetwork(self.feature_model,
feature_optimizer,
clip_grad=self.hyperparameters["clip_grad"])
v = VNetwork(self.value_model,
value_optimizer,
clip_grad=self.hyperparameters["clip_grad"],
writer=writer)
policy = SoftmaxPolicy(self.policy_model,
policy_optimizer,
clip_grad=self.hyperparameters["clip_grad"],
writer=writer)
return A2C(
features,
v,
policy,
n_envs=self.hyperparameters["n_envs"],
n_steps=self.hyperparameters["n_steps"],
discount_factor=self.hyperparameters["discount_factor"],
entropy_loss_scaling=self.hyperparameters["entropy_loss_scaling"],
writer=writer)
def _vpg(env, writer=DummyWriter()):
feature_model = feature_model_constructor(env).to(device)
value_model = value_model_constructor().to(device)
policy_model = policy_model_constructor(env).to(device)
feature_optimizer = Adam(feature_model.parameters(), lr=lr)
value_optimizer = Adam(value_model.parameters(), lr=lr)
policy_optimizer = Adam(policy_model.parameters(), lr=lr)
features = FeatureNetwork(
feature_model,
feature_optimizer,
writer=writer
)
v = VNetwork(
value_model,
value_optimizer,
writer=writer
)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
writer=writer
)
return VPG(features, v, policy, discount_factor=discount_factor, min_batch_size=min_batch_size)
def _a2c(envs, writer=DummyWriter()):
env = envs[0]
feature_model = feature_model_constructor(env).to(device)
value_model = value_model_constructor().to(device)
policy_model = policy_model_constructor(env).to(device)
feature_optimizer = Adam(feature_model.parameters(), lr=lr)
value_optimizer = Adam(value_model.parameters(), lr=lr)
policy_optimizer = Adam(policy_model.parameters(), lr=lr)
features = FeatureNetwork(
feature_model, feature_optimizer, clip_grad=clip_grad)
v = VNetwork(
value_model,
value_optimizer,
clip_grad=clip_grad,
writer=writer
)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
clip_grad=clip_grad,
writer=writer
)
return A2C(
features,
v,
policy,
n_envs=n_envs,
n_steps=n_steps,
discount_factor=discount_factor,
entropy_loss_scaling=entropy_loss_scaling,
writer=writer
)
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 _vac(envs, writer=DummyWriter()):
value_model = value_model_constructor().to(device)
policy_model = policy_model_constructor(envs[0]).to(device)
feature_model = feature_model_constructor().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)
v = VNetwork(
value_model,
value_optimizer,
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer,
)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
clip_grad=clip_grad,
writer=writer,
)
features = FeatureNetwork(feature_model,
feature_optimizer,
clip_grad=clip_grad,
writer=writer)
return DeepmindAtariBody(
VAC(features, v, policy, discount_factor=discount_factor), )
def _vpg(env, writer=DummyWriter()):
feature_model = fc_relu_features(env).to(device)
value_model = fc_value_head().to(device)
policy_model = fc_policy_head(env).to(device)
feature_optimizer = Adam(feature_model.parameters(), lr=lr)
value_optimizer = Adam(value_model.parameters(), lr=lr)
policy_optimizer = Adam(policy_model.parameters(), lr=lr)
features = FeatureNetwork(feature_model,
feature_optimizer,
clip_grad=clip_grad,
writer=writer)
v = VNetwork(value_model,
value_optimizer,
clip_grad=clip_grad,
writer=writer)
policy = SoftmaxPolicy(policy_model,
policy_optimizer,
env.action_space.n,
entropy_loss_scaling=entropy_loss_scaling,
clip_grad=clip_grad,
writer=writer)
return VPG(features,
v,
policy,
gamma=gamma,
min_batch_size=min_batch_size)
def _ppo(envs, writer=DummyWriter()):
env = envs[0]
feature_model = fc_relu_features(env).to(device)
value_model = fc_value_head().to(device)
policy_model = fc_policy_head(env).to(device)
feature_optimizer = Adam(feature_model.parameters(), lr=lr)
value_optimizer = Adam(value_model.parameters(), lr=lr)
policy_optimizer = Adam(policy_model.parameters(), lr=lr)
features = FeatureNetwork(feature_model,
feature_optimizer,
clip_grad=clip_grad)
v = VNetwork(value_model,
value_optimizer,
clip_grad=clip_grad,
writer=writer)
policy = SoftmaxPolicy(policy_model,
policy_optimizer,
clip_grad=clip_grad,
writer=writer)
return PPO(features,
v,
policy,
epsilon=epsilon,
epochs=epochs,
lam=lam,
minibatches=minibatches,
n_envs=n_envs,
n_steps=n_steps,
discount_factor=discount_factor,
entropy_loss_scaling=entropy_loss_scaling,
writer=writer)
def _ppo(envs, writer=DummyWriter()):
env = envs[0]
# Update epoch * minibatches times per update,
# but we only update once per n_steps,
# with n_envs and 4 frames per step
final_anneal_step = last_frame * epochs * minibatches / (n_steps *
n_envs * 4)
value_model = value_model_constructor().to(device)
policy_model = policy_model_constructor(env).to(device)
feature_model = feature_model_constructor().to(device)
feature_optimizer = Adam(feature_model.parameters(), lr=lr, eps=eps)
value_optimizer = Adam(value_model.parameters(), lr=lr, eps=eps)
policy_optimizer = Adam(policy_model.parameters(), lr=lr, eps=eps)
features = FeatureNetwork(feature_model,
feature_optimizer,
clip_grad=clip_grad,
scheduler=CosineAnnealingLR(
feature_optimizer, final_anneal_step),
writer=writer)
v = VNetwork(
value_model,
value_optimizer,
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer,
scheduler=CosineAnnealingLR(value_optimizer, final_anneal_step),
)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
clip_grad=clip_grad,
writer=writer,
scheduler=CosineAnnealingLR(policy_optimizer, final_anneal_step),
)
return DeepmindAtariBody(
PPO(
features,
v,
policy,
epsilon=LinearScheduler(clip_initial,
clip_final,
0,
final_anneal_step,
name='clip',
writer=writer),
epochs=epochs,
minibatches=minibatches,
n_envs=n_envs,
n_steps=n_steps,
discount_factor=discount_factor,
lam=lam,
entropy_loss_scaling=entropy_loss_scaling,
writer=writer,
))
def agent(self, writer=DummyWriter(), train_steps=float('inf')):
n_updates = train_steps * self.hyperparameters[
'epochs'] * self.hyperparameters['minibatches'] / (
self.hyperparameters['n_steps'] *
self.hyperparameters['n_envs'])
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"])
policy_optimizer = Adam(self.policy_model.parameters(),
lr=self.hyperparameters["lr"],
eps=self.hyperparameters["eps"])
features = FeatureNetwork(self.feature_model,
feature_optimizer,
scheduler=CosineAnnealingLR(
feature_optimizer, n_updates),
clip_grad=self.hyperparameters["clip_grad"],
writer=writer)
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(
PPO(
features,
v,
policy,
epsilon=LinearScheduler(self.hyperparameters["clip_initial"],
self.hyperparameters["clip_final"],
0,
n_updates,
name='clip',
writer=writer),
epochs=self.hyperparameters["epochs"],
minibatches=self.hyperparameters["minibatches"],
n_envs=self.hyperparameters["n_envs"],
n_steps=self.hyperparameters["n_steps"],
discount_factor=self.hyperparameters["discount_factor"],
lam=self.hyperparameters["lam"],
entropy_loss_scaling=self.
hyperparameters["entropy_loss_scaling"],
writer=writer,
))
def _a2c(envs, writer=DummyWriter()):
env = envs[0]
feature_model = conv_features().to(device)
value_model = value_net().to(device)
policy_model = policy_net(env).to(device)
feature_optimizer = RMSprop(
feature_model.parameters(),
alpha=alpha,
lr=lr * feature_lr_scaling,
eps=eps
)
value_optimizer = RMSprop(
value_model.parameters(),
alpha=alpha,
lr=lr,
eps=eps
)
policy_optimizer = RMSprop(
policy_model.parameters(),
alpha=alpha,
lr=lr,
eps=eps
)
features = FeatureNetwork(
feature_model,
feature_optimizer,
clip_grad=clip_grad
)
v = ValueNetwork(
value_model,
value_optimizer,
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer
)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
env.action_space.n,
entropy_loss_scaling=entropy_loss_scaling,
clip_grad=clip_grad,
writer=writer,
)
return ParallelAtariBody(
A2C(
features,
v,
policy,
n_envs=n_envs,
n_steps=n_steps,
discount_factor=discount_factor,
),
envs,
)
def _ppo(envs, writer=DummyWriter()):
final_anneal_step = last_frame * epochs * minibatches / (n_steps *
n_envs)
env = envs[0]
feature_model, value_model, policy_model = fc_actor_critic(env)
feature_model.to(device)
value_model.to(device)
policy_model.to(device)
feature_optimizer = Adam(feature_model.parameters(), lr=lr, eps=eps)
value_optimizer = Adam(value_model.parameters(), lr=lr, eps=eps)
policy_optimizer = Adam(policy_model.parameters(), lr=lr, eps=eps)
features = FeatureNetwork(feature_model,
feature_optimizer,
clip_grad=clip_grad,
scheduler=CosineAnnealingLR(
feature_optimizer, final_anneal_step),
writer=writer)
v = VNetwork(
value_model,
value_optimizer,
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer,
scheduler=CosineAnnealingLR(value_optimizer, final_anneal_step),
)
policy = GaussianPolicy(
policy_model,
policy_optimizer,
env.action_space,
clip_grad=clip_grad,
writer=writer,
scheduler=CosineAnnealingLR(policy_optimizer, final_anneal_step),
)
return TimeFeature(
PPO(
features,
v,
policy,
epsilon=LinearScheduler(clip_initial,
clip_final,
0,
final_anneal_step,
name='clip',
writer=writer),
epochs=epochs,
minibatches=minibatches,
n_envs=n_envs,
n_steps=n_steps,
discount_factor=discount_factor,
lam=lam,
entropy_loss_scaling=entropy_loss_scaling,
writer=writer,
))
def _a2c(envs, writer=DummyWriter()):
env = envs[0]
final_anneal_step = last_frame / (n_steps * n_envs)
value_model = value_head().to(device)
policy_model = policy_head(env).to(device)
feature_model = conv_features().to(device)
feature_optimizer = Adam(feature_model.parameters(), lr=lr, eps=eps)
value_optimizer = Adam(value_model.parameters(), lr=lr, eps=eps)
policy_optimizer = Adam(policy_model.parameters(), lr=lr, eps=eps)
features = FeatureNetwork(
feature_model,
feature_optimizer,
scheduler=CosineAnnealingLR(
feature_optimizer,
final_anneal_step,
),
clip_grad=clip_grad,
writer=writer
)
v = VNetwork(
value_model,
value_optimizer,
scheduler=CosineAnnealingLR(
value_optimizer,
final_anneal_step,
),
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer
)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
scheduler=CosineAnnealingLR(
policy_optimizer,
final_anneal_step,
),
clip_grad=clip_grad,
writer=writer
)
return FrameStack(
A2C(
features,
v,
policy,
n_envs=n_envs,
n_steps=n_steps,
discount_factor=discount_factor,
entropy_loss_scaling=LinearScheduler(entropy_loss_scaling, 0., 0, final_anneal_step, name="entropy_loss_scaling", writer=writer),
writer=writer
),
size=4
)
def _a2c(envs, writer=DummyWriter()):
env = envs[0]
final_anneal_step = last_frame / (n_steps * n_envs * 4)
value_model = nature_value_head().to(device)
policy_model = nature_policy_head(env).to(device)
feature_model = nature_features().to(device)
feature_optimizer = Adam(feature_model.parameters(), lr=lr, eps=eps)
value_optimizer = Adam(value_model.parameters(), lr=lr, eps=eps)
policy_optimizer = Adam(policy_model.parameters(), lr=lr, eps=eps)
features = FeatureNetwork(
feature_model,
feature_optimizer,
scheduler=CosineAnnealingLR(
feature_optimizer,
final_anneal_step,
),
clip_grad=clip_grad,
writer=writer
)
v = VNetwork(
value_model,
value_optimizer,
scheduler=CosineAnnealingLR(
value_optimizer,
final_anneal_step,
),
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer
)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
scheduler=CosineAnnealingLR(
policy_optimizer,
final_anneal_step,
),
clip_grad=clip_grad,
writer=writer
)
return DeepmindAtariBody(
A2C(
features,
v,
policy,
n_envs=n_envs,
n_steps=n_steps,
discount_factor=discount_factor,
entropy_loss_scaling=entropy_loss_scaling,
writer=writer
),
)
def _vac(env, writer=DummyWriter()):
value_model = fc_value_head().to(device)
policy_model = fc_policy_head(env).to(device)
feature_model = fc_relu_features(env).to(device)
value_optimizer = RMSprop(value_model.parameters(), lr=lr_v, alpha=alpha, eps=eps)
policy_optimizer = RMSprop(policy_model.parameters(), lr=lr_pi, alpha=alpha, eps=eps)
feature_optimizer = RMSprop(feature_model.parameters(), lr=lr_pi, alpha=alpha, eps=eps)
v = VNetwork(value_model, value_optimizer, writer=writer)
policy = SoftmaxPolicy(policy_model, policy_optimizer, env.action_space.n, writer=writer)
features = FeatureNetwork(feature_model, feature_optimizer)
return VAC(features, v, policy, gamma=discount_factor)
def _vac(env, writer=DummyWriter()):
value_model = value_model_constructor().to(device)
policy_model = policy_model_constructor(env).to(device)
feature_model = feature_model_constructor(env).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)
v = VNetwork(value_model, value_optimizer, writer=writer)
policy = SoftmaxPolicy(policy_model, policy_optimizer, writer=writer)
features = FeatureNetwork(feature_model, feature_optimizer)
return VAC(features, v, policy, discount_factor=discount_factor)
def _a2c(envs, writer=DummyWriter()):
env = envs[0]
value_model = nature_value_head().to(device)
policy_model = nature_policy_head(envs[0]).to(device)
feature_model = nature_features().to(device)
feature_optimizer = RMSprop(
feature_model.parameters(), alpha=alpha, lr=lr, eps=eps
)
value_optimizer = RMSprop(value_model.parameters(), alpha=alpha, lr=lr, eps=eps)
policy_optimizer = RMSprop(
policy_model.parameters(), alpha=alpha, lr=lr, eps=eps
)
features = FeatureNetwork(
feature_model,
feature_optimizer,
clip_grad=clip_grad,
writer=writer
)
v = VNetwork(
value_model,
value_optimizer,
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer
)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
env.action_space.n,
entropy_loss_scaling=entropy_loss_scaling,
clip_grad=clip_grad,
writer=writer
)
return DeepmindAtariBody(
A2C(
features,
v,
policy,
n_envs=n_envs,
n_steps=n_steps,
discount_factor=discount_factor,
),
)
def _vpg_atari(env, writer=DummyWriter()):
feature_model = nature_features().to(device)
value_model = nature_value_head().to(device)
policy_model = nature_policy_head(env).to(device)
feature_optimizer = RMSprop(feature_model.parameters(),
alpha=alpha,
lr=lr * feature_lr_scaling,
eps=eps)
value_optimizer = RMSprop(value_model.parameters(),
alpha=alpha,
lr=lr,
eps=eps)
policy_optimizer = RMSprop(policy_model.parameters(),
alpha=alpha,
lr=lr,
eps=eps)
features = FeatureNetwork(feature_model,
feature_optimizer,
clip_grad=clip_grad,
writer=writer)
v = VNetwork(value_model,
value_optimizer,
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
env.action_space.n,
entropy_loss_scaling=entropy_loss_scaling,
clip_grad=clip_grad,
writer=writer,
)
return DeepmindAtariBody(
VPG(features,
v,
policy,
gamma=discount_factor,
min_batch_size=min_batch_size), )
def _vpg_atari(env, writer=DummyWriter()):
value_model = nature_value_head().to(device)
policy_model = nature_policy_head(env).to(device)
feature_model = nature_features().to(device)
feature_optimizer = Adam(feature_model.parameters(), lr=lr, eps=eps)
value_optimizer = Adam(value_model.parameters(), lr=lr, eps=eps)
policy_optimizer = Adam(policy_model.parameters(), lr=lr, eps=eps)
features = FeatureNetwork(feature_model,
feature_optimizer,
scheduler=CosineAnnealingLR(
feature_optimizer,
final_anneal_step,
),
clip_grad=clip_grad,
writer=writer)
v = VNetwork(value_model,
value_optimizer,
scheduler=CosineAnnealingLR(
value_optimizer,
final_anneal_step,
),
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer)
policy = SoftmaxPolicy(policy_model,
policy_optimizer,
scheduler=CosineAnnealingLR(
policy_optimizer,
final_anneal_step,
),
clip_grad=clip_grad,
writer=writer)
return DeepmindAtariBody(VPG(features,
v,
policy,
discount_factor=discount_factor,
min_batch_size=min_batch_size),
episodic_lives=True)
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 test_agent(self):
features = FeatureNetwork(copy.deepcopy(self.feature_model))
policy = SoftmaxPolicy(copy.deepcopy(self.policy_model))
return DeepmindAtariBody(VACTestAgent(features, policy))
def __init__(
self,
model_fn,
model_features,
logger,
device,
num_targets,
max_learn_steps,
num_actions,
obs_preproc,
discount_factor=0.99,
entropy_target=-2,
lr_value=1e-3,
lr_pi=1e-4,
# Training settings
polyak_rate=0.005,
# Replay Buffer settings
replay_start_size=5000,
replay_buffer_size=1e6,
# Exploration settings
temperature_initial=0.1,
lr_temperature=1e-5,
entropy_target_scaling=1.,
):
self.writer = writer = DummyWriter()
eps = 1e-5
self.discount_factor = discount_factor
self.entropy_target = entropy_target
self.temperature = temperature_initial
self.lr_temperature = lr_temperature
self.logger = logger
self.device = device
self.num_targets = num_targets
self.max_learn_steps = max_learn_steps
self.num_actions = num_actions
final_anneal_step = (max_learn_steps)
self.policy = DiversityPolicy(model_fn, model_features, num_actions,
num_targets, obs_preproc, device)
self.policy = self.policy.to(device)
self.obs_preproc = obs_preproc
policy_optimizer = Adam(self.policy.parameters(), lr=lr_pi, eps=eps)
self.policy_learner = SoftmaxPolicy(self.policy,
policy_optimizer,
scheduler=CosineAnnealingLR(
policy_optimizer,
final_anneal_step),
writer=writer)
value_feature_model = model_fn().to(device)
q_models = [
DuelingQValueLayer(model_features, num_targets,
num_actions).to(device) for i in range(2)
]
v_model = ValueLayer(model_features, num_targets,
num_actions).to(device)
feature_optimizer = Adam(value_feature_model.parameters(),
lr=lr_value,
eps=eps)
q_optimizers = [
Adam(q_models[i].parameters(), lr=lr_value, eps=eps)
for i in range(2)
]
v_optimizer = Adam(v_model.parameters(), lr=lr_value, eps=eps)
self.features = FeatureNetwork(
value_feature_model,
feature_optimizer,
scheduler=CosineAnnealingLR(
feature_optimizer,
final_anneal_step,
),
# clip_grad=clip_grad,
writer=writer)
self.qs = [
QContinuous(q_models[i],
q_optimizers[i],
scheduler=CosineAnnealingLR(q_optimizers[i],
final_anneal_step),
writer=writer,
name=f'q_{i}') for i in range(2)
]
self.v = VNetwork(
v_model,
v_optimizer,
scheduler=CosineAnnealingLR(v_optimizer, final_anneal_step),
target=PolyakTarget(polyak_rate),
writer=writer,
name='v',
)
def setUp(self):
torch.manual_seed(1)
self.features = FeatureNetwork(nn.Linear(1, 2), None)
self.v = VNetwork(nn.Linear(2, 1), None)
def test_agent(self):
f = FeatureNetwork(self.feature_model, None)
v = VNetwork(self.value_model, None)
r = QNetwork(self.reward_model, None)
g = Approximation(self.generator_model, None)
return DeepmindAtariBody(ModelBasedTestAgent(f, v, r, g, self.hyperparameters["discount_factor"]))
def test_agent(self):
features = FeatureNetwork(copy.deepcopy(self.feature_model))
policy = SoftmaxPolicy(copy.deepcopy(self.policy_model))
return VPGTestAgent(features, policy)
def _ppo(envs, writer=DummyWriter()):
env = envs[0]
value_model = nature_value_head().to(device)
policy_model = nature_policy_head(envs[0]).to(device)
feature_model = nature_features().to(device)
feature_optimizer = Adam(
feature_model.parameters(), lr=lr, eps=eps
)
value_optimizer = Adam(value_model.parameters(), lr=lr, eps=eps)
policy_optimizer = Adam(policy_model.parameters(), lr=lr, eps=eps)
features = FeatureNetwork(
feature_model,
feature_optimizer,
clip_grad=clip_grad,
scheduler=CosineAnnealingLR(
feature_optimizer,
final_anneal_step,
eta_min=lr * min_lr_scale
),
writer=writer
)
v = VNetwork(
value_model,
value_optimizer,
loss_scaling=value_loss_scaling,
clip_grad=clip_grad,
writer=writer,
scheduler=CosineAnnealingLR(
value_optimizer,
final_anneal_step,
eta_min=lr * min_lr_scale
),
)
policy = SoftmaxPolicy(
policy_model,
policy_optimizer,
env.action_space.n,
entropy_loss_scaling=entropy_loss_scaling,
clip_grad=clip_grad,
writer=writer,
scheduler=CosineAnnealingLR(
policy_optimizer,
final_anneal_step,
eta_min=lr * min_lr_scale
),
)
return DeepmindAtariBody(
PPO(
features,
v,
policy,
epsilon=LinearScheduler(
clip_initial,
clip_final,
0,
final_anneal_step,
name='clip',
writer=writer
),
epochs=epochs,
minibatches=minibatches,
n_envs=n_envs,
n_steps=n_steps,
discount_factor=discount_factor,
lam=lam,
)
)