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Render all spawnpoints that are matched with a target dirt pile + Fixed arrow placement

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Chanumask 2024-05-12 11:48:05 +02:00 committed by Julian Schönberger
parent defbaf6f93
commit 98113ea849
1 changed files with 35 additions and 14 deletions
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35
marl_factory_grid/utils/plotting/plot_single_runs.py
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@ -1,3 +1,5 @@
import ast
import os
import pickle import pickle
from os import PathLike from os import PathLike
from pathlib import Path from pathlib import Path
@ -147,25 +149,44 @@ def plot_action_maps(factory, agents, result_path):
target_dirt_pos = factory.state.entities[d.DIRT][action_map_index].pos target_dirt_pos = factory.state.entities[d.DIRT][action_map_index].pos
action_entities.append( action_entities.append(
RenderEntity(name='target_dirt', probability=0, pos=swap_coordinates(target_dirt_pos))) RenderEntity(name='target_dirt', probability=0, pos=swap_coordinates(target_dirt_pos)))
action_entities.append(RenderEntity(name='spawn_pos', probability=0, pos=swap_coordinates(
factory.state.agent_spawn_positions[agent_index])))
# Render all spawnpoints assigned to current target dirt pile
spawnpoints = list(factory.state.agents_conf.values())[agent_index]['positions']
all_target_dirts = []
if 'DirtPiles' in factory.conf['Entities']:
tuples = ast.literal_eval(factory.conf['Entities']['DirtPiles']['coords_or_quantity'])
for t in tuples:
all_target_dirts.append(t)
assigned_spawn_positions = []
for j in range(len(spawnpoints) // len(all_target_dirts)):
assigned_spawn_positions.append(spawnpoints[j * len(all_target_dirts) + all_target_dirts.index(target_dirt_pos)])
for spawn_pos in assigned_spawn_positions:
action_entities.append(RenderEntity(name='spawn_pos', probability=0, pos=swap_coordinates(spawn_pos)))
render_arrows = []
for position, probabilities in probabilities_map.items(): for position, probabilities in probabilities_map.items():
if position not in wall_positions: if position not in wall_positions:
if np.any(probabilities) > 0: # Ensure it's not all zeros which would indicate a wall if np.any(probabilities) > 0: # Ensure it's not all zeros which would indicate a wall
sorted_indices = sorted(range(len(probabilities)), key=lambda i: -probabilities[i]) sorted_indices = np.argsort(np.argsort(-probabilities))
colors = ['green_arrow', 'yellow_arrow', 'red_arrow', 'grey_arrow'] colors = ['green_arrow', 'yellow_arrow', 'red_arrow', 'grey_arrow']
render_arrows.append([])
for rank, direction_index in enumerate(sorted_indices): for rank, direction_index in enumerate(sorted_indices):
action = directions[direction_index] action = directions[direction_index]
probability = probabilities[direction_index] probability = probabilities[rank]
arrow_color = colors[rank] arrow_color = colors[direction_index]
render_arrows[-1].append((probability, arrow_color, position))
# Swap west and east
for l in render_arrows:
l[1], l[3] = l[3], l[1]
for l in render_arrows:
for rank, (probability, arrow_color, position) in enumerate(l):
if probability > 0: if probability > 0:
action_entity = RenderEntity( action_entity = RenderEntity(
name=arrow_color, name=arrow_color,
pos=position, pos=position,
probability=probability, probability=probability,
rotation=direction_index * 90 rotation=rank * 90
) )
action_entities.append(action_entity) action_entities.append(action_entity)