def _vsarsa(envs, writer=DummyWriter()):
env = envs[0]
model = fc_relu_q(env).to(device)
optimizer = Adam(model.parameters(), lr=lr, eps=eps)
q = QNetwork(model, optimizer, writer=writer)
policy = GreedyPolicy(q, env.action_space.n, epsilon=epsilon)
return VSarsa(q, policy, discount_factor=discount_factor)
def agent(self, writer=DummyWriter(), train_steps=float('inf')):
n_updates = train_steps / self.hyperparameters['n_envs']
optimizer = Adam(
self.model.parameters(),
lr=self.hyperparameters['lr'],
eps=self.hyperparameters['eps']
)
q = QNetwork(
self.model,
optimizer,
scheduler=CosineAnnealingLR(optimizer, n_updates),
writer=writer
)
policy = ParallelGreedyPolicy(
q,
self.n_actions,
epsilon=LinearScheduler(
self.hyperparameters['initial_exploration'],
self.hyperparameters['final_exploration'],
0,
self.hyperparameters["final_exploration_step"] / self.hyperparameters["n_envs"],
name="exploration",
writer=writer
)
)
return DeepmindAtariBody(
VSarsa(q, policy, discount_factor=self.hyperparameters['discount_factor']),
)
def _vsarsa(envs, writer=DummyWriter()):
env = envs[0]
model = nature_ddqn(env).to(device)
optimizer = RMSprop(model.parameters(), lr=lr, alpha=alpha, eps=eps)
q = QNetwork(model,
optimizer,
env.action_space.n,
loss=smooth_l1_loss,
writer=writer)
policy = GreedyPolicy(q,
env.action_space.n,
epsilon=LinearScheduler(initial_exploration,
final_exploration,
0,
final_exploration_frame,
name="epsilon",
writer=writer))
return DeepmindAtariBody(VSarsa(q, policy, gamma=discount_factor), )