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1704ce2 | 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 | import random
from env import CloudEnv
from agent import get_action # your LLM agent
# ---------------------------
# BASELINE AGENTS
# ---------------------------
def random_agent(state):
return random.choice([0, 1, 2])
def rule_based_agent(state):
if state.cpu > 80:
return 0 # add server
elif state.cpu < 40:
return 1 # remove server
else:
return 2 # do nothing
# ---------------------------
# RUN EPISODE
# ---------------------------
def run_episode(env, agent_fn):
state = env.reset()
history = []
done = False
while not done:
action = agent_fn(state)
next_state, reward, done, _ = env.step(action)
history.append({
"cpu": next_state.cpu,
"servers": next_state.servers,
"cost": next_state.cost,
"reward": reward
})
state = next_state
return history
# ---------------------------
# VERIFIER (CORE LOGIC)
# ---------------------------
def evaluate_episode(history):
total_reward = sum(h["reward"] for h in history)
avg_cpu = sum(h["cpu"] for h in history) / len(history)
avg_cost = sum(h["cost"] for h in history) / len(history)
overloads = sum(1 for h in history if h["cpu"] > 100)
optimal_cpu = sum(1 for h in history if 50 <= h["cpu"] <= 75)
# FINAL SCORE (you can tweak this)
score = (
total_reward
+ optimal_cpu * 2
- overloads * 3
- avg_cost * 0.1
)
return {
"score": score,
"total_reward": total_reward,
"avg_cpu": avg_cpu,
"avg_cost": avg_cost,
"overloads": overloads,
"optimal_steps": optimal_cpu
}
# ---------------------------
# TEST ALL AGENTS
# ---------------------------
def evaluate_agent(agent_fn, name):
env = CloudEnv("medium")
history = run_episode(env, agent_fn)
result = evaluate_episode(history)
print(f"\n=== {name} ===")
for k, v in result.items():
print(f"{k}: {round(v, 2)}")
# ---------------------------
# MAIN
# ---------------------------
if __name__ == "__main__":
evaluate_agent(random_agent, "Random Agent")
evaluate_agent(rule_based_agent, "Rule-Based Agent")
# LLM agent wrapper
def llm_agent(state):
return get_action(state)
evaluate_agent(llm_agent, "LLM Agent") |