def __init__(self, name, agent, all_locations):
"""
Creates a new reward feature.
:param Agent agent: the PsychSim agent capable of retrieving the feature's value given a state.
:param str name: the label for this reward feature.
:param list[str] all_locations: all the world's locations.
"""
super().__init__(name)
self.agent = agent
self.world = agent.world
self.all_locations = all_locations
self.location_feat = get_location_key(agent)
def __init__(self, name, agent, all_locations, inverse=True, max_frequency=1):
"""
Creates a new reward feature.
:param Agent agent: the PsychSim agent capable of retrieving the feature's value given a state.
:param str name: the label for this reward feature.
:param list[str] all_locations: all the world's locations.
:param bool inverse: whether to take the inverse frequency, i.e., `time - freq`.
:param int max_frequency: the maximum frequency that the agent can achieve (either for any or all locations).
"""
super().__init__(name, 1. / max_frequency) # use max frequency as normalization factor to keep var in [0,1]
self.agent = agent
self.world = agent.world
self.inverse = inverse
self.all_locations = all_locations
self.location_feat = get_location_key(agent)
self.time_feat = get_mission_seconds_key()
def plot_trajectories(agents,
trajectories,
locations,
neighbors,
output_img,
coordinates=None,
state=None,
title='Trajectories',
draw_victims=True,
show=False):
"""
Plots the given set of trajectories over a graph representation of the environment.
:param Agent or list[Agent] agents: the agent or agents (one per trajectory) whose trajectories we want to plot.
:param list[list[tuple[World, Distribution]]] trajectories: the set of trajectories to save, containing
several sequences of state-action pairs.
:param list[str] locations: the list of possible world locations.
:param dict[str,dict[int, str]] neighbors: the locations' neighbors in each direction.
:param str output_img: the path to the file in which to save the plot.
:param dict[str, tuple[float,float]] coordinates: the coordinates of each world location.
:param VectorDistributionSet state: the state used to fetch the graph information to be plotted.
:param str title: the plot's title.
:param bool draw_victims: whether to include the number of victims of each color in each location of the graph.
:param bool show: whether to show the plot to the screen.
:return:
"""
multiagent = True
if isinstance(agents, Agent):
multiagent = False
agents = [agents] * len(trajectories)
# get base world plot
g, g_plot = _plot(agents[0].world, locations, neighbors, output_img,
coordinates, state, title, draw_victims)
if len(trajectories) == 0 or len(trajectories[0]) == 0:
return
# draw each trajectory
t_colors = distinct_colors(len(trajectories))
for i, trajectory in enumerate(trajectories):
loc_feat = get_location_key(agents[i])
state = copy.deepcopy(trajectory[0][0].state)
state.select(True) # select most likely state
source = trajectory[0][0].getFeature(loc_feat, state, True)
for t in range(1, len(trajectory)):
state = copy.deepcopy(trajectory[t][0].state)
state.select(True)
target = trajectory[t][0].getFeature(loc_feat, state, True)
label = (agents[i].name if multiagent else
'T{:02d}'.format(i)) if t == len(trajectory) - 1 else None
g.add_edge(source,
target,
color=color_to_html_format(t_colors[i]),
arrow_size=EDGE_ARROW_SIZE,
arrow_width=EDGE_ARROW_WIDTH,
label_size=EDGE_LABEL_SIZE,
label_color=EDGE_LABEL_COLOR,
width=TRAJ_EDGE_WIDTH,
label=label)
source = target
g_plot.redraw()
_draw_title(title, g_plot.surface, g_plot.width, g_plot.height)
_save_plot(g_plot, output_img)
if show:
g_plot.show()