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ff293b1 ee21104 ff293b1 ee21104 ff293b1 | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | """Phase 4: reward sub-scores, aggregation, logging, schema drift."""
import json
import random
import statistics
from pathlib import Path
import pytest
from ghostexec.models import GhostexecAction
from ghostexec.server import reward as reward_mod
from ghostexec.server.reward import aggregate_scores
from ghostexec.server.ghostexec_environment import GhostexecEnvironment
ROOT = Path(__file__).resolve().parents[1]
SCENARIO = ROOT / "scenarios" / "phase2_core.json"
DRIFT = ROOT / "scenarios" / "schema_drift_test.json"
def test_reward_weights_and_aggregator_helpers():
w = GhostexecEnvironment.load_world_from_json(SCENARIO)
c, r, t = 1.0, -1.0, 2.5
weighted_inner = reward_mod.W_CONFLICT * c + reward_mod.W_REL * r + reward_mod.W_TASK * t
bd = aggregate_scores(
c,
r,
t,
conflict_raw=c,
critical_queue_bonus=0.0,
weighted_inner=weighted_inner,
weighted_base_only=weighted_inner,
shaping_synergy=0.0,
shaping_tradeoff=0.0,
shaping_potential=0.0,
shaping_scaffold=0.0,
shaping_quality=0.0,
action_ok=True,
episode_done=False,
world_after=w,
)
assert bd.weighted_base == pytest.approx(reward_mod.WEIGHTED_OUTPUT_SCALE * weighted_inner)
def test_catastrophic_and_completion_bonuses_only_when_episode_done():
w0 = GhostexecEnvironment.load_world_from_json(SCENARIO)
w1 = w0.model_copy(deep=True)
w1.stress = 30
w2 = w1.model_copy(deep=True)
action = GhostexecAction(action_type="do_nothing")
mid = reward_mod.compute_step_reward(w1, w2, action, action_ok=True, episode_done=False)
assert mid.episode_completion_bonus == 0.0
assert mid.catastrophic_penalty == 0.0
w_bad = w1.model_copy(deep=True)
for i, c in enumerate(w_bad.contacts):
if c.name == "Marcus Webb":
w_bad.contacts[i] = c.model_copy(update={"mood": "furious"})
break
end = reward_mod.compute_step_reward(w1, w_bad, action, action_ok=True, episode_done=True)
assert end.episode_completion_bonus == pytest.approx(10.0)
assert end.catastrophic_penalty == pytest.approx(-15.0)
def test_invalid_step_matches_do_nothing_subscores_plus_invalid_addon():
w = GhostexecEnvironment.load_world_from_json(SCENARIO)
noop = GhostexecAction(action_type="do_nothing")
bad = GhostexecAction(action_type="reply_email", email_id="missing", message_body="x")
bd_ok = reward_mod.compute_step_reward(w, w, noop, action_ok=True, episode_done=False)
bd_bad = reward_mod.compute_step_reward(w, w, bad, action_ok=False, episode_done=False)
assert bd_bad.invalid_step_adjustment == pytest.approx(-0.25)
# do_nothing carries an additional strict additive floor (-0.15) not applied to invalid non-idle actions.
assert bd_bad.final == pytest.approx(bd_ok.final - (0.25 - 0.15))
def test_scripted_episode_reward_direction_and_log(tmp_path, monkeypatch):
logf = tmp_path / "rewards.jsonl"
env = GhostexecEnvironment(SCENARIO)
env.reset()
monkeypatch.setattr(env, "_reward_log_path", logf)
r_resolve = env.step(
GhostexecAction(
action_type="reschedule_meeting",
meeting_id="m02",
new_time="2026-04-21T18:00:00",
)
)
r_bad = env.step(GhostexecAction(action_type="do_nothing"))
assert r_resolve.metadata.get("step_ok") is True
assert r_bad.metadata.get("step_ok") is True
assert (r_resolve.reward or 0) > (r_bad.reward or 0)
assert logf.is_file()
lines = logf.read_text(encoding="utf-8").strip().splitlines()
assert len(lines) >= 2
row = json.loads(lines[0])
assert "reward" in row and "episode_id" in row
assert row.get("action_type") == "reschedule_meeting"
assert "conflict_raw" in row and "step_ok" in row
assert "shaping_total" in row and "shaping_to_base_ratio" in row
assert "shaping_scaffold" in row
assert row.get("reward_mode") == "full"
def test_reward_mode_base_turns_off_shaping_terms():
env = GhostexecEnvironment(SCENARIO, reward_mode="base")
env.reset()
obs = env.step(
GhostexecAction(
action_type="reschedule_meeting",
meeting_id="m02",
new_time="2026-04-21T18:00:00",
)
)
bd = (obs.metadata or {}).get("reward_breakdown") or {}
assert float(bd.get("shaping_synergy") or 0.0) == pytest.approx(0.0)
assert float(bd.get("shaping_tradeoff") or 0.0) == pytest.approx(0.0)
assert float(bd.get("shaping_potential") or 0.0) == pytest.approx(0.0)
def test_schema_drift_events_mutate_world():
env = GhostexecEnvironment(SCENARIO, schema_drift_events_path=DRIFT)
env.reset()
assert env.step(GhostexecAction(action_type="do_nothing")).metadata.get("step_ok") is True
assert any("schema drift: shifted" in x for x in env.world.action_log)
env.step(GhostexecAction(action_type="do_nothing"))
sarah = env.get_contact("Sarah Chen")
assert sarah is not None
assert sarah.communication_preference == "text"
env.step(GhostexecAction(action_type="do_nothing"))
t02 = next(t for t in env.world.tasks if t.id == "t02")
assert t02.deadline == "2026-04-21T07:00:00"
assert "Marcus Webb" in env._reply_relationship_suppressed # noqa: SLF001
def test_rewards_differ_between_helpful_and_idle_steps():
env = GhostexecEnvironment(SCENARIO)
env.reset()
r_help = env.step(
GhostexecAction(
action_type="reschedule_meeting",
meeting_id="m02",
new_time="2026-04-21T18:00:00",
)
).reward
r_idle = env.step(GhostexecAction(action_type="do_nothing")).reward
assert r_help is not None and r_idle is not None
assert r_help != r_idle
# Whitelisted reschedules (known non-overlapping targets for phase2_core at 08:00).
_SAFE_RESCHEDULES: list[tuple[str, str]] = [
("m02", "2026-04-21T18:00:00"),
("m03", "2026-04-21T18:30:00"),
("m06", "2026-04-21T20:00:00"),
("m09", "2026-04-21T21:00:00"),
]
def test_seeded_stochastic_policy_reward_spread():
random.seed(1234)
K = 80
archive_ids = [f"e{i:02d}" for i in range(1, 31)]
contacts = ["Jordan Lee", "Jamie Liu", "Marcus Webb", "Sarah Chen"]
env = GhostexecEnvironment(SCENARIO)
env.reset()
rewards: list[float] = []
ai = ri = 0
for _ in range(K):
u = random.random()
if u < 0.32:
obs = env.step(GhostexecAction(action_type="do_nothing"))
elif u < 0.58:
eid = archive_ids[ai % len(archive_ids)]
ai += 1
obs = env.step(GhostexecAction(action_type="archive_email", email_id=eid))
elif u < 0.78:
mid, nt = _SAFE_RESCHEDULES[ri % len(_SAFE_RESCHEDULES)]
ri += 1
obs = env.step(
GhostexecAction(action_type="reschedule_meeting", meeting_id=mid, new_time=nt)
)
else:
cname = contacts[ai % len(contacts)]
ai += 1
obs = env.step(
GhostexecAction(
action_type="send_message",
contact_name=cname,
message_body="Quick sync on priorities.",
)
)
assert obs.reward is not None
rewards.append(float(obs.reward))
std = statistics.pstdev(rewards)
sr = sorted(rewards)
p5 = sr[max(0, int(0.05 * (len(sr) - 1)))]
p95 = sr[min(len(sr) - 1, int(0.95 * (len(sr) - 1)))]
assert std > 0.06
assert (p95 - p5) > 0.09
def test_good_script_beats_do_nothing_spam_on_mean_reward():
good = GhostexecEnvironment(SCENARIO)
good.reset()
good_actions = [
GhostexecAction(
action_type="reschedule_meeting",
meeting_id="m02",
new_time="2026-04-21T18:00:00",
),
GhostexecAction(action_type="reply_email", email_id="e01", message_body="Drafting revised figures now."),
GhostexecAction(action_type="archive_email", email_id="e09"),
GhostexecAction(
action_type="send_message",
contact_name="Jordan Lee",
message_body="Standup notes attached.",
),
GhostexecAction(action_type="complete_task", task_id="t06"),
]
g_rewards = [good.step(a).reward for a in good_actions]
g_mean = sum(float(x) for x in g_rewards) / len(g_rewards)
bad = GhostexecEnvironment(SCENARIO)
bad.reset()
b_rewards = [bad.step(GhostexecAction(action_type="do_nothing")).reward for _ in range(5)]
b_mean = sum(float(x) for x in b_rewards) / len(b_rewards)
assert g_mean > b_mean + 0.2
|