File size: 2,772 Bytes
77da5ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import pytest
import copy
from core.reward import compute_task_reward
from core.action_space import AgentAction, PrimaryAction
from core.task import TaskGenerator
from core.life_state import LifeMetrics
def test_reward_milestone_bonus():
"""Verify that hitting a milestone results in a positive reward component."""
gen = TaskGenerator()
task = gen.get_random_task()
milestone = task.milestones[0]
# State before: empty world
state_before = LifeMetrics()
# State after: satisfied the milestone condition
state_after = copy.deepcopy(state_before)
# Most milestones in TaskGenerator are boolean flags in mutable_world
# We must simulate the world mutation that matches the task logic
# Note: Task metrics are actually in the flat 'mutable_world' or specific LifeMetrics domains
# For smoke test, we simulate the 'milestones_achieved' list directly as returned by Env
reward, breakdown = compute_task_reward(
state_before=state_before,
state_after=state_after,
resources_used={"time": 1.0, "energy": 10.0},
actions_taken=1,
milestones_achieved=[milestone.id],
success_conditions_met=[False],
exo_events_seen=0,
milestones_after_event=0,
routes_remaining=1,
rollback_used=False,
cascade_collapse=False,
task=task
)
assert breakdown["components"]["milestone"] > 0
assert reward >= 0
def test_reward_scaling_with_impact():
"""Verify that improving metrics results in higher outcome reward than stationary state."""
gen = TaskGenerator()
task = gen.get_random_task()
state_before = LifeMetrics()
# Positive case: metrics improve
state_good = copy.deepcopy(state_before)
state_good.career.stability = 90.0 # Started at 70
# Neutral case: no change
state_neutral = copy.deepcopy(state_before)
reward_good, break_good = compute_task_reward(
state_before=state_before, state_after=state_good,
resources_used={"time": 1.0}, actions_taken=1, milestones_achieved=[],
success_conditions_met=[False], exo_events_seen=0, milestones_after_event=0,
routes_remaining=1, rollback_used=False, cascade_collapse=False, task=task
)
reward_neutral, break_neutral = compute_task_reward(
state_before=state_before, state_after=state_neutral,
resources_used={"time": 1.0}, actions_taken=1, milestones_achieved=[],
success_conditions_met=[False], exo_events_seen=0, milestones_after_event=0,
routes_remaining=1, rollback_used=False, cascade_collapse=False, task=task
)
assert break_good["components"]["local_metric_delta"] > break_neutral["components"]["local_metric_delta"]
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