Files
ai-agent-book/tests/test_ch1_learning_agents_empty_eval_windows.py
T
liqiang b119135836
Build latest book artifacts / build (push) Canceled after 0s
dependency resolution / resolve (3.11) (push) Canceled after 0s
dependency resolution / resolve (3.13) (push) Canceled after 0s
deploy-pages / build (push) Canceled after 0s
deploy-pages / deploy (push) Canceled after 0s
i18n consistency check / check (push) Canceled after 0s
provider adoption tests / test (chapter2/context-compression) (push) Canceled after 0s
provider adoption tests / test (chapter2/prompt-injection) (push) Canceled after 0s
provider adoption tests / test (chapter2/system-hint) (push) Canceled after 0s
provider adoption tests / test (chapter3/log-sanitization) (push) Canceled after 0s
web-search-agent tests / test (push) Canceled after 0s
web-search-agent tests / agentbook (push) Canceled after 0s
ai-agent-book 精选快照(<2MB 代码与文档,来自 github.com/bojieli/ai-agent-book)
2026-08-20 13:12:50 +00:00

36 lines
1.1 KiB
Python

"""Regression test for empty evaluation windows in chapter1 RL and LLM learning agents."""
import sys
from pathlib import Path
import pytest
pytest.importorskip("openai")
pytest.importorskip("numpy")
# Add chapter1/learning-from-experience to sys.path
ch1_dir = (Path(__file__).resolve().parent.parent / "chapter1" / "learning-from-experience").resolve()
if str(ch1_dir) not in sys.path:
sys.path.insert(0, str(ch1_dir))
from llm_agent import LLMAgent
from rl_agent import QLearningAgent
def test_rl_agent_evaluate_zero_episodes():
agent = QLearningAgent()
results = agent.evaluate(num_episodes=0)
assert results["num_episodes"] == 0
assert results["victory_rate"] == 0.0
assert results["avg_reward"] == 0.0
assert results["avg_length"] == 0.0
assert results["std_reward"] == 0.0
assert results["std_length"] == 0.0
def test_llm_agent_evaluate_zero_episodes():
agent = LLMAgent(api_key="dummy-key")
results = agent.evaluate(num_episodes=0)
assert results["num_episodes"] == 0
assert results["victory_rate"] == 0.0
assert results["avg_reward"] == 0.0
assert results["avg_length"] == 0.0