def act(self, state, explore=True):
state = copy.deepcopy(state)
state["low_dim_states"] = np.float32(
np.append(state["low_dim_states"], self.prev_action))
state["social_vehicles"] = (torch.from_numpy(
state["social_vehicles"]).unsqueeze(0).to(self.device))
state["low_dim_states"] = (torch.from_numpy(
state["low_dim_states"]).unsqueeze(0).to(self.device))
self.actor.eval()
action = self.actor(state).cpu().data.numpy().flatten()
noise = [self.noise[0].sample(), self.noise[1].sample()]
if explore:
action[0] += noise[0]
action[1] += noise[1]
self.actor.train()
action_low, action_high = (
self.action_low.data.cpu().numpy(),
self.action_high.data.cpu().numpy(),
)
action = np.clip(action, action_low, action_high)[0]
return to_3d_action(action)
def act(self, state, explore=True):
self.actor.eval()
state = self.state_preprocessor(
state=state,
normalize=True,
unsqueeze=True,
device=self.device,
social_capacity=self.social_capacity,
observation_num_lookahead=self.observation_num_lookahead,
social_vehicle_config=self.social_vehicle_config,
prev_action=self.prev_action,
)
# print(state)
action = self.actor(state).cpu().data.numpy().flatten()
noise = [self.noise[0].sample(), self.noise[1].sample()]
if explore:
action[0] += noise[0]
action[1] += noise[1]
self.actor.train()
action_low, action_high = (
self.action_low.data.cpu().numpy(),
self.action_high.data.cpu().numpy(),
)
action = np.clip(action, action_low, action_high)[0]
return to_3d_action(action)
def act(self, state, explore=True):
state = self.state_preprocessor(
state=state,
normalize=True,
unsqueeze=True,
device=self.device,
social_capacity=self.social_capacity,
observation_num_lookahead=self.observation_num_lookahead,
social_vehicle_config=self.social_vehicle_config,
prev_action=self.prev_action,
)
with torch.no_grad():
dist, value = self.ppo_net(state)
if explore: # training mode
action = dist.sample()
log_prob = dist.log_prob(action)
self.current_log_prob = log_prob
self.current_value = value
action = torch.squeeze(action)
action = action.data.cpu().numpy()
else: # testing mode
mean = torch.squeeze(dist.loc)
action = mean.data.cpu().numpy()
self.step_count += 1
return to_3d_action(action)
def act(self, state, explore=True):
state = copy.deepcopy(state)
state["low_dim_states"] = np.float32(
np.append(state["low_dim_states"], self.prev_action))
state["social_vehicles"] = (torch.from_numpy(
state["social_vehicles"]).unsqueeze(0).to(self.device))
state["low_dim_states"] = (torch.from_numpy(
state["low_dim_states"]).unsqueeze(0).to(self.device))
action, _, mean = self.sac_net.sample(state)
if explore: # training mode
action = torch.squeeze(action, 0)
action = action.detach().cpu().numpy()
else: # testing mode
mean = torch.squeeze(mean, 0)
action = mean.detach().cpu().numpy()
return to_3d_action(action)
def act(self, state, explore=True):
state = self.state_preprocessor(
state=state,
normalize=True,
unsqueeze=True,
device=self.device_name,
social_capacity=self.social_capacity,
observation_num_lookahead=self.observation_num_lookahead,
social_vehicle_config=self.social_vehicle_config,
prev_action=self.prev_action,
)
action, _, mean = self.sac_net.sample(state)
if explore: # training mode
action = torch.squeeze(action, 0)
action = action.detach().cpu().numpy()
else: # testing mode
mean = torch.squeeze(mean, 0)
action = mean.detach().cpu().numpy()
return to_3d_action(action)
def act(self, state, explore=True):
state["low_dim_states"] = np.float32(
np.append(state["low_dim_states"], self.prev_action))
state["social_vehicles"] = (torch.from_numpy(
state["social_vehicles"]).unsqueeze(0).to(self.device))
state["low_dim_states"] = (torch.from_numpy(
state["low_dim_states"]).unsqueeze(0).to(self.device))
with torch.no_grad():
dist, value = self.ppo_net(x=state)
if explore: # training mode
action = dist.sample()
log_prob = dist.log_prob(action)
self.current_log_prob = log_prob
self.current_value = value
action = torch.squeeze(action)
action = action.data.cpu().numpy()
else: # testing mode
mean = torch.squeeze(dist.loc)
action = mean.data.cpu().numpy()
self.step_count += 1
return to_3d_action(action)