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12263fa dfc5996 | 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 | # Cloud Arena Evaluation — Mathematical Model
# Extracted from cloud_arena_final.py (Cells 4-5)
import os
import numpy as np
import torch
from typing import List
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from sb3_contrib import MaskablePPO
from sb3_contrib.common.wrappers import ActionMasker
from cloud_arena.environment import (
CloudArenaEnv, get_action_masks, MAX_RESOURCES, MAX_STEPS, A_NOOP,
)
def _get_inner(vec_env):
inner = vec_env.envs[0]
while hasattr(inner, "env"):
inner = inner.env
return inner
def evaluate_model(model_path="./models/cloud_arena_final",
vecnorm_path="./models/cloud_arena_vecnorm.pkl",
level=0, n_eval=30):
results = {k: [] for k in ["win","cost_score","security_score",
"reliability_score","savings_pct","veto_rate",
"cascade_count","steps"]}
def make_eval_env():
env = CloudArenaEnv(curriculum_ref=[level], global_step_ref=[500000])
return ActionMasker(env, get_action_masks)
raw = DummyVecEnv([make_eval_env])
eval_env = VecNormalize.load(vecnorm_path, raw)
eval_env.training = False
eval_env.norm_reward = False
model = MaskablePPO.load(model_path, env=eval_env)
for ep in range(n_eval):
obs = eval_env.reset()
done = False
steps = 0
while not done:
masks = [_get_inner(eval_env).action_masks()]
act, _ = model.predict(obs, deterministic=True, action_masks=masks)
obs, rew, done_arr, info_arr = eval_env.step(act)
done = bool(done_arr[0])
steps += 1
info = info_arr[0] if info_arr else {}
for k in results:
results[k].append(info.get(k, 0) if k != "steps" else steps)
return results
BOSS_NAMES = {
1: "Cost Crisis",
2: "Security Breach",
3: "Infrastructure Failure",
4: "Traffic Surge",
5: "Final Boss",
}
def run_boss_fights(model_path="./models/cloud_arena_final",
vecnorm_path="./models/cloud_arena_vecnorm.pkl",
level=0, n_runs=10):
model = MaskablePPO.load(model_path)
boss_scores = {}
for s_id, name in BOSS_NAMES.items():
runs = []
for seed in range(100, 100 + n_runs):
def _init():
env = CloudArenaEnv(curriculum_ref=[level], global_step_ref=[0])
return ActionMasker(env, get_action_masks)
raw = DummyVecEnv([_init])
vec = VecNormalize.load(vecnorm_path, raw)
vec.training = False
vec.norm_reward = False
inner = _get_inner(vec)
raw_obs, _ = inner.reset(seed=seed, options={"scenario": s_id})
obs = vec.normalize_obs(np.array([raw_obs]))
done = False
steps = 0
noops_chaos = 0
chaos_steps_total = 0
while not done:
masks = [inner.action_masks()]
act, _ = model.predict(obs, deterministic=True, action_masks=masks)
a_type = int(act[0]) // MAX_RESOURCES
if inner.chaos_active:
chaos_steps_total += 1
if a_type == A_NOOP:
noops_chaos += 1
obs, _, done_arr, info_arr = vec.step(act)
done = bool(done_arr[0])
steps += 1
info = info_arr[0] if info_arr else {}
info.update({"steps": steps, "noops_chaos": noops_chaos, "chaos_steps": chaos_steps_total})
runs.append(info)
vec.close()
wins = [r.get("win", 0) for r in runs]
costs = [r.get("cost_score", 0) for r in runs]
secs = [r.get("security_score", 0) for r in runs]
rels = [r.get("reliability_score", 0) for r in runs]
if s_id == 3:
noop_r = [r["noops_chaos"] / max(r["chaos_steps"], 1) for r in runs]
score = (0.4 * np.mean(noop_r) + 0.6 * np.mean(rels)) * 100
else:
score = (0.4 * np.mean(wins) + 0.3 * np.mean(costs) + 0.3 * np.mean(secs)) * 100
boss_scores[s_id] = score
return boss_scores
def evaluate_llm_grpo(model, tokenizer, n_eval=20, steps_per_episode=15, seed=123):
"""
Evaluate LLM policy quality on the FinOps environment using the same
ACTION parser logic as training.
"""
import random
import torch
from cloud_arena.llm_environment import SB3Adapter
from cloud_arena.llm_training import extract_action_and_reasoning, format_prompt
random.seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
env = SB3Adapter()
metrics = {
"episodes": n_eval,
"win_rate": 0.0,
"avg_savings_pct": 0.0,
"avg_episode_len": 0.0,
"safety_violation_rate": 0.0,
"action_distribution": {str(i): 0 for i in range(5)},
"avg_reward_components": {},
}
wins = 0
total_savings = 0.0
total_steps = 0
total_safety_violations = 0
reward_components_sum = {}
total_component_steps = 0
for _ in range(n_eval):
_, _ = env.reset()
done = False
step_count = 0
last_info = {}
while not done and step_count < steps_per_episode:
state_dict = env.core._get_internal_state()
prompt = format_prompt(state_dict)
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
input_ids = inputs["input_ids"].to(model.device)
attn_mask = inputs["attention_mask"].to(model.device)
with torch.no_grad():
out = model.generate(
input_ids=input_ids,
attention_mask=attn_mask,
max_new_tokens=80,
do_sample=False,
pad_token_id=tokenizer.pad_token_id,
)
response = tokenizer.decode(out[0][input_ids.shape[1] :], skip_special_tokens=True)
action, _ = extract_action_and_reasoning(response)
metrics["action_distribution"][str(action)] += 1
_, _, terminated, truncated, info = env.step(action)
done = bool(terminated or truncated)
step_count += 1
last_info = info
total_safety_violations += int(info.get("safety_violation", 0))
rc = info.get("reward_components", {})
for k, v in rc.items():
reward_components_sum[k] = reward_components_sum.get(k, 0.0) + float(v)
total_component_steps += 1
wins += int(last_info.get("win", False))
total_savings += float(last_info.get("savings_pct", 0.0))
total_steps += step_count
total_actions = max(sum(metrics["action_distribution"].values()), 1)
metrics["action_distribution"] = {
k: round(v / total_actions, 4) for k, v in metrics["action_distribution"].items()
}
metrics["win_rate"] = round(wins / max(n_eval, 1), 4)
metrics["avg_savings_pct"] = round(total_savings / max(n_eval, 1), 3)
metrics["avg_episode_len"] = round(total_steps / max(n_eval, 1), 3)
metrics["safety_violation_rate"] = round(total_safety_violations / max(total_steps, 1), 4)
metrics["avg_reward_components"] = {
k: round(v / max(total_component_steps, 1), 4) for k, v in reward_components_sum.items()
}
return metrics
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