def display_vehicles_attention(cls, agent, sim_surface):
import pygame
try:
state = agent.previous_state
if (not hasattr(cls, "state")) or (cls.state != state).any():
cls.v_attention = cls.compute_vehicles_attention(agent, state)
cls.state = state
for head in range(list(cls.v_attention.values())[0].shape[0]):
attention_surface = pygame.Surface(sim_surface.get_size(), pygame.SRCALPHA)
for vehicle, attention in cls.v_attention.items():
if attention[head] < cls.MIN_ATTENTION:
continue
width = attention[head] * 5
desat = remap(attention[head], (0, 0.5), (0.7, 1), clip=True)
colors = sns.color_palette("dark", desat=desat)
color = np.array(colors[2-2*head]) * 255
color = (*color, remap(attention[head], (0, 0.5), (100, 200), clip=True))
if vehicle is agent.env.vehicle:
pygame.draw.circle(attention_surface, color,
sim_surface.vec2pix(agent.env.vehicle.position),
max(sim_surface.pix(width / 2), 1))
else:
pygame.draw.line(attention_surface, color,
sim_surface.vec2pix(agent.env.vehicle.position),
sim_surface.vec2pix(vehicle.position),
max(sim_surface.pix(width), 1))
sim_surface.blit(attention_surface, (0, 0))
except ValueError as e:
print("Unable to display vehicles attention", e)
def compute_vehicles_attention(cls, agent, state):
import torch
state_t = torch.tensor([state], dtype=torch.float).to(agent.device)
attention = agent.value_net.get_attention_matrix(state_t).squeeze(
0).squeeze(1).detach().cpu().numpy()
ego, others, mask = agent.value_net.split_input(state_t)
mask = mask.squeeze()
v_attention = {}
for v_index in range(state.shape[0]):
if mask[v_index]:
continue
v_position = {}
for feature in ["x", "y"]:
v_feature = state[
v_index,
agent.env.observation.features.index(feature)]
v_feature = remap(
v_feature, [-1, 1],
agent.env.observation.features_range[feature])
v_position[feature] = v_feature
v_position = np.array([v_position["x"], v_position["y"]])
if not agent.env.observation.absolute and v_index > 0:
v_position += agent.env.unwrapped.vehicle.position
vehicle = min(
agent.env.road.vehicles,
key=lambda v: np.linalg.norm(v.position - v_position))
v_attention[vehicle] = attention[:, v_index]
return v_attention
def _plot_node(self, node, pos, ax, depth=0):
if depth > self.max_depth:
return
for a in range(self.actions):
if a in node.children:
child = node.children[a]
if not child.count:
continue
d = 1 / self.actions**depth
pos_child = [pos[0] - d/2 + a/(self.actions - 1)*d, pos[1] - 1/self.max_depth]
width = constrain(remap(child.count, (1, self.total_count), (0.5, 4)), 0.5, 4)
ax.plot([pos[0], pos_child[0]], [pos[1], pos_child[1]], 'k', linewidth=width, solid_capstyle='round')
self._plot_node(child, pos_child, ax, depth+1)
def display(cls, agent, surface, sim_surface=None, display_text=True):
"""
Display the action-values for the current state
:param agent: the DQNAgent to be displayed
:param surface: the pygame surface on which the agent is displayed
:param sim_surface: the pygame surface on which the env is rendered
:param display_text: whether to display the action values as text
"""
import pygame
action_values = agent.get_state_action_values(agent.previous_state)
action_distribution = agent.action_distribution(agent.previous_state)
cell_size = (surface.get_width() // len(action_values),
surface.get_height())
pygame.draw.rect(surface, cls.BLACK,
(0, 0, surface.get_width(), surface.get_height()), 0)
# Display node value
for action, value in enumerate(action_values):
cmap = cm.jet_r
norm = mpl.colors.Normalize(vmin=0,
vmax=1 / (1 - agent.config["gamma"]))
color = cmap(norm(value), bytes=True)
pygame.draw.rect(
surface, color,
(cell_size[0] * action, 0, cell_size[0], cell_size[1]), 0)
if display_text:
font = pygame.font.Font(None, 15)
text = "v={:.2f} / p={:.2f}".format(
value, action_distribution[action])
text = font.render(text, 1, (10, 10, 10), (255, 255, 255))
surface.blit(text, (cell_size[0] * action, 0))
if sim_surface:
try:
state = agent.previous_state
if (not hasattr(cls, "state")) or (cls.state != state).any():
cls.v_attention = cls.compute_vehicles_attention(
agent, state)
cls.state = state
for head in range(list(cls.v_attention.values())[0].shape[0]):
attention_surface = pygame.Surface(sim_surface.get_size(),
pygame.SRCALPHA)
for vehicle, attention in cls.v_attention.items():
if attention[head] < cls.MIN_ATTENTION:
continue
width = attention[head] * 5
desat = remap(attention[head], (0, 0.5), (0.7, 1),
clip=True)
colors = sns.color_palette("dark", desat=desat)
color = np.array(colors[2 - 2 * head]) * 255
color = (*color,
remap(attention[head], (0, 0.5), (100, 200),
clip=True))
if vehicle is agent.env.vehicle:
pygame.draw.circle(
attention_surface, color,
sim_surface.vec2pix(
agent.env.vehicle.position),
max(sim_surface.pix(width / 2), 1))
else:
pygame.draw.line(
attention_surface, color,
sim_surface.vec2pix(
agent.env.vehicle.position),
sim_surface.vec2pix(vehicle.position),
max(sim_surface.pix(width), 1))
sim_surface.blit(attention_surface, (0, 0))
except ValueError as e:
print("Unable to display vehicles attention", e)