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Fix marginal score calculation to use path-dependent arrival time #7

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Fix marginal score calculation to use path-dependent arrival time #7

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21 changes: 6 additions & 15 deletions lib/CBBA.py
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Original file line number Diff line number Diff line change
Expand Up @@ -619,8 +619,7 @@ def compute_bid(self, idx_agent: int, feasibility: list):
if score > best_bid:
best_bid = score
best_index = j
# Select min start time as optimal
best_time = 0.0
best_time = min_start

# save best bid information
if best_bid > 0:
Expand Down Expand Up @@ -666,19 +665,11 @@ def scoring_compute_score(self, idx_agent: int, task_current: Task, task_prev: T
# i have to have time to do task m and fly to task at j+1
max_start = min(task_current.end_time, time_next - task_current.duration - dt)

# Compute score
if self.time_window_flag:
# if tasks have time window
reward = task_current.task_value * \
math.exp((-task_current.discount) * (min_start-task_current.start_time))
else:
# no time window for tasks
dt_current = math.sqrt((self.AgentList[idx_agent].x-task_current.x)**2 +
(self.AgentList[idx_agent].y-task_current.y)**2 +
(self.AgentList[idx_agent].z-task_current.z)**2) / \
self.AgentList[idx_agent].nom_velocity

reward = task_current.task_value * math.exp((-task_current.discount) * dt_current)
# Compute score using path-dependent arrival time (paper eq. 11).
# min_start already accounts for preceding tasks in the path (via time_prev + duration + dt),
# so this formula is correct for both single- and multi-task assignments.
reward = task_current.task_value * \
math.exp((-task_current.discount) * (min_start - task_current.start_time))

# # Subtract fuel cost. Implement constant fuel to ensure DMG (diminishing marginal gain).
# # This is a fake score since it double-counts fuel. Should not be used when comparing to optimal score.
Expand Down