pengxiao
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以讲义内容为骨架迁移到标准目录格式： - officefile/ 主内容（12章 + 附录 + CC4SI补充） - dofile/ 代码示例（11个Python脚本） - data/ 图片资源 - output/ 生成输出（忽略） - Archive/ 归档旧目录（忽略） - .claude/skills/ 保留markdown-to-docx工具链 - .pandoc/ 保留CSL和本地化配置 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
38 KiB
38 KiB
03.5 规划与执行
核心问题
Agent如何将复杂目标分解为可执行步骤？ 当环境变化时，如何动态调整计划？ 如何平衡规划深度与执行效率？
概念讲解
规划的基本概念
规划 (Planning) 是寻找从初始状态到目标状态的行动序列的过程：
规划问题的基本要素
状态空间 (State Space)
│
├─── 初始状态: S₀
├─── 目标状态: S_goal
└─── 中间状态: S₁, S₂, ..., Sₙ
│
↓
行动 (Actions)
│
├─── 前置条件: preconditions
├─── 效果: effects
└─── 代价: cost
│
↓
规划 = 行动序列 [a₁, a₂, ..., aₙ]
使得: S₀ ─a₁→ S₁ ─a₂→ S₂ ... ─aₙ→ S_goal
规划与执行的循环
┌─────────────────────────────────────────────────────┐
│ 规划-执行循环 │
│ (Plan-Execute Loop) │
├─────────────────────────────────────────────────────┤
│ │
│ ┌─────────┐ │
│ │ 目标 │ │
│ └────┬────┘ │
│ │ │
│ ↓ │
│ ┌─────────┐ ┌─────────────┐ │
│ │ 规划 │ ───→ │ 执行步骤 │ │
│ │ Planner│ │ Executor │ │
│ └────┬────┘ └──────┬──────┘ │
│ │ │ │
│ │ ↓ │
│ │ ┌─────────────┐ │
│ │ │ 观察结果 │ │
│ │ └──────┬──────┘ │
│ │ │ │
│ │ ↓ │
│ │ ┌─────────────┐ │
│ │ │ 监控状态 │ │
│ │ └──────┬──────┘ │
│ │ │ │
│ ↓ ↓ │
│ ┌─────────────────────────────┐ │
│ │ 是否需要重新规划？ │ │
│ └─────────────┬───────────────┘ │
│ 是 否 │
│ │ │ │
│ └──────────────┘ │
│ │ │
│ ↓ │
│ ┌─────────────┐ │
│ │ 继续执行 │ │
│ └─────────────┘ │
│ │
└─────────────────────────────────────────────────────┘
规划方法的分类
规划方法谱系
┌────────────────────────────────────────────────────────┐
│ │
│ 前向搜索 (Forward Search) │
│ ──────────────────────── │
│ 从初始状态向目标状态搜索 │
│ 适合：目标明确，分支因子较小 │
│ │
│ ↓ │
│ │
│ 后向搜索 (Backward Search) │
│ ─────────────────────── │
│ 从目标状态向初始状态搜索 │
│ 适合：目标状态较少，起始状态较多 │
│ │
│ ↓ │
│ │
│ 双向搜索 (Bidirectional Search) │
│ ────────────────────────────── │
│ 同时从两端搜索，在中间汇合 │
│ 适合：状态空间大，双向都可搜索 │
│ │
│ ↓ │
│ │
│ 分层规划 (Hierarchical Planning) │
│ ───────────────────────────── │
│ 先规划高层抽象，再细化具体步骤 │
│ 适合：复杂任务，有多层抽象 │
│ │
└────────────────────────────────────────────────────────┘
设计原理
前向搜索规划
from typing import Callable, List, Dict, Any, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import heapq
from collections import deque
class PlanningStatus(Enum):
"""规划状态"""
SUCCESS = "success"
FAILURE = "failure"
IN_PROGRESS = "in_progress"
NO_PLAN = "no_plan"
@dataclass
class Action:
"""行动定义"""
name: str
preconditions: Callable[[Any], bool] # 前置条件检查
effects: Callable[[Any], Any] # 状态转换
cost: float = 1.0 # 行动代价
@dataclass
class Plan:
"""计划"""
actions: List[Action]
expected_final_state: Any
total_cost: float
steps: List[str]
class ForwardSearchPlanner:
"""
前向搜索规划器
从初始状态开始，逐步应用行动直到达到目标
"""
def __init__(self,
actions: List[Action],
goal_test: Callable[[Any], bool],
max_depth: int = 100,
heuristic: Callable[[Any], float] = None):
"""
Args:
actions: 可用行动列表
goal_test: 目标测试函数
max_depth: 最大搜索深度
heuristic: 启式函数（估计到目标的距离）
"""
self.actions = actions
self.goal_test = goal_test
self.max_depth = max_depth
self.heuristic = heuristic or (lambda s: 0)
# 搜索统计
self.nodes_expanded = 0
self.nodes_visited = 0
def plan(self, initial_state: Any) -> Optional[Plan]:
"""
执行前向搜索规划
支持的搜索算法：
- BFS（无启发式）
- UCS（uniform cost search，无启发式但有代价）
- A*（有启发式）
"""
# 搜索节点：(f_score, g_score, state, action_sequence)
initial_node = (
self.heuristic(initial_state),
0,
initial_state,
[]
)
open_set = [initial_node]
closed_set = set()
while open_set:
# 获取最优节点
f, g, current_state, action_sequence = heapq.heappop(open_set)
# 检查是否已访问
state_hash = self._hash_state(current_state)
if state_hash in closed_set:
continue
closed_set.add(state_hash)
self.nodes_visited += 1
# 检查是否达到目标
if self.goal_test(current_state):
return Plan(
actions=action_sequence,
expected_final_state=current_state,
total_cost=g,
steps=[a.name for a in action_sequence]
)
# 深度限制
if len(action_sequence) >= self.max_depth:
continue
# 扩展节点
self.nodes_expanded += 1
for action in self.actions:
# 检查前置条件
if action.preconditions(current_state):
# 应用行动
new_state = action.effects(current_state)
# 创建新节点
new_g = g + action.cost
new_f = new_g + self.heuristic(new_state)
new_sequence = action_sequence + [action]
heapq.heappush(open_set, (
new_f,
new_g,
new_state,
new_sequence
))
return None
def _hash_state(self, state: Any) -> int:
"""状态哈希（用于去重）"""
return hash(str(state))
class BackwardSearchPlanner:
"""
后向搜索规划器
从目标状态开始，反向应用行动直到回到初始状态
"""
def __init__(self,
actions: List[Action],
initial_state: Any,
max_depth: int = 100):
"""
Args:
actions: 可用行动列表
initial_state: 初始状态
max_depth: 最大搜索深度
"""
self.actions = actions
self.initial_state = initial_state
self.max_depth = max_depth
def plan(self, goal_state: Any) -> Optional[Plan]:
"""
执行后向搜索规划
反向行动需要能够"撤销"原行动的效果
"""
# 构建反向行动
reverse_actions = self._build_reverse_actions()
# 从目标状态搜索到初始状态
planner = ForwardSearchPlanner(
actions=reverse_actions,
goal_test=lambda s: s == self.initial_state,
max_depth=self.max_depth
)
result = planner.plan(goal_state)
if result:
# 反转行动序列
result.actions = list(reversed(result.actions))
result.steps = list(reversed(result.steps))
return result
def _build_reverse_actions(self) -> List[Action]:
"""构建反向行动（简化实现）"""
# 实际实现需要更复杂的逻辑
# 这里假设行动是可逆的
return self.actions
分层规划
class HierarchicalTask:
"""分层任务"""
def __init__(self, name: str,
is_primitive: bool = False,
subtasks: List['HierarchicalTask'] = None,
implementation: Callable = None):
self.name = name
self.is_primitive = is_primitive
self.subtasks = subtasks or []
self.implementation = implementation
class HierarchicalPlanner:
"""
分层规划器 (HTN - Hierarchical Task Network)
将复杂任务分解为可执行的原子任务
"""
def __init__(self, root_task: HierarchicalTask):
self.root_task = root_task
self.task_hierarchy = self._build_hierarchy(root_task)
def _build_hierarchy(self, task: HierarchicalTask,
level: int = 0) -> Dict:
"""构建任务层次结构"""
return {
'task': task,
'level': level,
'children': [
self._build_hierarchy(t, level + 1)
for t in task.subtasks
]
}
def plan(self, initial_state: Dict) -> List[Action]:
"""
执行分层规划
1. 从根任务开始
2. 递归分解非原子任务
3. 收集所有原子任务
"""
execution_plan = []
self._decompose_task(self.root_task, initial_state, execution_plan)
return execution_plan
def _decompose_task(self, task: HierarchicalTask,
state: Dict, plan: List) -> bool:
"""分解任务"""
if task.is_primitive:
# 原子任务，直接执行
if task.implementation:
result = task.implementation(state)
plan.append(Action(
name=task.name,
preconditions=lambda s: True,
effects=lambda s: result,
cost=1.0
))
return True
return False
# 非原子任务，递归分解子任务
for subtask in task.subtasks:
if not self._decompose_task(subtask, state, plan):
return False
return True
def visualize_hierarchy(self) -> str:
"""可视化任务层次"""
lines = []
def print_node(node, prefix="", is_last=True):
connector = "└── " if is_last else "├── "
lines.append(f"{prefix}{connector}{node['task'].name}")
children = node['children']
for i, child in enumerate(children):
is_last_child = (i == len(children) - 1)
extension = " " if is_last else "│ "
print_node(child, prefix + extension, is_last_child)
print_node(self.task_hierarchy)
return "\n".join(lines)
动态重规划
class ReplanningAgent:
"""
支持动态重规划的Agent
特点：
1. 持续监控执行状态
2. 检测计划失效
3. 触发重新规划
"""
def __init__(self, planner, monitor_interval: float = 1.0):
self.planner = planner
self.monitor_interval = monitor_interval
self.current_plan: Optional[Plan] = None
self.executed_steps: List[str] = []
self.plan_status = PlanningStatus.IN_PROGRESS
def execute_with_monitoring(self,
initial_state: Any,
environment) -> Any:
"""
带监控的执行
Args:
initial_state: 初始状态
environment: 环境接口（支持 step() 和 get_state()）
"""
# 初始规划
self.current_plan = self.planner.plan(initial_state)
if not self.current_plan:
self.plan_status = PlanningStatus.NO_PLAN
return None
current_state = initial_state
# 执行-监控循环
for action in self.current_plan.actions:
print(f"执行: {action.name}")
# 执行行动
try:
result = environment.execute(action.name, current_state)
self.executed_steps.append(action.name)
# 更新状态
current_state = environment.get_state()
# 检查是否需要重新规划
if self._should_replan(current_state, action, result):
print("检测到计划失效，重新规划...")
self._replan(current_state)
# 检查计划是否完成
if self._plan_complete(current_state):
self.plan_status = PlanningStatus.SUCCESS
break
except Exception as e:
print(f"执行失败: {e}")
self._replan(current_state)
return current_state
def _should_replan(self, state: Any,
last_action: Action,
result: Any) -> bool:
"""判断是否需要重新规划"""
# 检查1：行动结果是否符合预期
if not result.get('success', True):
return True
# 检查2：状态是否发生意外变化
if result.get('unexpected_change', False):
return True
# 检查3：目标是否已改变
if result.get('goal_changed', False):
return True
return False
def _replan(self, current_state: Any) -> None:
"""执行重新规划"""
# 保存已执行的步骤
executed = self.executed_steps.copy()
# 重新规划
new_plan = self.planner.plan(current_state)
if new_plan:
self.current_plan = new_plan
print(f"新计划: {' → '.join(new_plan.steps)}")
else:
print("无法找到新计划")
self.plan_status = PlanningStatus.FAILURE
def _plan_complete(self, state: Any) -> bool:
"""检查计划是否完成"""
return self.planner.goal_test(state)
代码示例
空间分析任务规划器
"""
空间分析任务规划器
演示如何为GIS分析任务创建分层规划系统
"""
from typing import List, Dict, Any, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
import json
class TaskType(Enum):
"""任务类型"""
DATA_PREPARATION = "data_preparation"
ANALYSIS = "analysis"
VISUALIZATION = "visualization"
EXPORT = "export"
class TaskStatus(Enum):
"""任务状态"""
PENDING = "pending"
IN_PROGRESS = "in_progress"
COMPLETED = "completed"
FAILED = "failed"
SKIPPED = "skipped"
@dataclass
class Task:
"""任务定义"""
id: str
name: str
type: TaskType
description: str = ""
depends_on: List[str] = field(default_factory=list)
parameters: Dict[str, Any] = field(default_factory=dict)
status: TaskStatus = TaskStatus.PENDING
result: Any = None
error: Optional[str] = None
def is_ready(self, completed_tasks: set) -> bool:
"""检查任务是否准备就绪（依赖已完成）"""
return all(dep in completed_tasks for dep in self.depends_on)
def to_dict(self) -> Dict:
"""转换为字典"""
return {
'id': self.id,
'name': self.name,
'type': self.type.value,
'description': self.description,
'depends_on': self.depends_on,
'parameters': self.parameters,
'status': self.status.value,
'error': self.error
}
class SpatialAnalysisPlanner:
"""
空间分析任务规划器
功能：
1. 定义任务依赖关系
2. 生成执行计划
3. 执行任务序列
4. 处理失败和重试
"""
def __init__(self, name: str = "Spatial Analysis"):
self.name = name
self.tasks: Dict[str, Task] = {}
self.execution_history: List[Dict] = []
def add_task(self,
task_id: str,
name: str,
task_type: TaskType,
description: str = "",
depends_on: List[str] = None,
parameters: Dict = None) -> 'SpatialAnalysisPlanner':
"""添加任务"""
self.tasks[task_id] = Task(
id=task_id,
name=name,
type=task_type,
description=description,
depends_on=depends_on or [],
parameters=parameters or {}
)
return self
def get_execution_plan(self) -> List[List[str]]:
"""
获取执行计划（分层级）
返回每层可并行执行的任务ID列表
"""
plan = []
completed = set()
remaining = set(self.tasks.keys())
while remaining:
# 找出所有准备就绪的任务
ready = [
task_id for task_id in remaining
if self.tasks[task_id].is_ready(completed)
]
if not ready:
# 循环依赖
raise ValueError("检测到循环依赖或无法满足的依赖")
plan.append(ready)
completed.update(ready)
remaining -= set(ready)
return plan
def execute(self,
executor: Callable[[Task], Any],
max_retries: int = 1) -> Dict[str, Any]:
"""
执行计划
Args:
executor: 任务执行器函数
max_retries: 最大重试次数
Returns:
执行结果摘要
"""
plan = self.get_execution_plan()
results = {}
completed = set()
for level, task_ids in enumerate(plan):
print(f"\n=== 执行层级 {level + 1}/{len(plan)} ===")
print(f"任务: {', '.join(task_ids)}")
# 可以并行执行（这里简化为顺序）
for task_id in task_ids:
task = self.tasks[task_id]
for attempt in range(max_retries + 1):
try:
print(f" 执行: {task.name} (尝试 {attempt + 1})")
task.status = TaskStatus.IN_PROGRESS
# 执行任务
result = executor(task)
task.result = result
task.status = TaskStatus.COMPLETED
completed.add(task_id)
results[task_id] = result
# 记录历史
self.execution_history.append({
'task_id': task_id,
'status': 'completed',
'attempt': attempt + 1
})
break
except Exception as e:
error_msg = str(e)
task.error = error_msg
if attempt < max_retries:
print(f" 失败，重试: {error_msg}")
else:
task.status = TaskStatus.FAILED
print(f" 最终失败: {error_msg}")
self.execution_history.append({
'task_id': task_id,
'status': 'failed',
'error': error_msg,
'attempts': attempt + 1
})
# 决定是否继续
if task_id in self._get_critical_tasks():
print("关键任务失败，终止执行")
return results
return results
def visualize_plan(self) -> str:
"""可视化执行计划（DAG）"""
plan = self.get_execution_plan()
lines = [f"\n{self.name} - 执行计划"]
lines.append("=" * 50)
for level, task_ids in enumerate(plan):
lines.append(f"\n层级 {level + 1}:")
for task_id in task_ids:
task = self.tasks[task_id]
deps = f" (依赖: {', '.join(task.depends_on)})" if task.depends_on else ""
lines.append(f" - {task.name}{deps}")
return "\n".join(lines)
def _get_critical_tasks(self) -> set:
"""获取关键任务（失败会终止整个流程）"""
# 简化实现：所有数据准备任务是关键的
return {
t.id for t in self.tasks.values()
if t.type == TaskType.DATA_PREPARATION
}
# ==================== 预定义分析工作流 ====================
class CommonAnalysisWorkflows:
"""常见空间分析工作流模板"""
@staticmethod
def ecological_network_analysis() -> SpatialAnalysisPlanner:
"""
生态网络分析工作流
六阶段：
1. 数据准备
2. 源地识别
3. 阻力面构建
4. MCR分析
5. 廊道提取
6. 结果输出
"""
planner = SpatialAnalysisPlanner("生态网络分析")
# 数据准备阶段
planner.add_task("load_landcover", "加载土地覆盖数据",
TaskType.DATA_PREPARATION,
"加载研究区土地覆盖栅格数据")
planner.add_task("load_elevation", "加载高程数据",
TaskType.DATA_PREPARATION,
"加载DEM高程数据")
# 分析阶段
planner.add_task("identify_sources", "识别生态源地",
TaskType.ANALYSIS,
"基于形态空间格局分析识别核心生境斑块",
depends_on=["load_landcover"])
planner.add_task("build_resistance", "构建生态阻力面",
TaskType.ANALYSIS,
"基于土地覆盖类型赋值构建阻力面",
depends_on=["load_landcover", "load_elevation"])
planner.add_task("mcr_analysis", "最小累积阻力分析",
TaskType.ANALYSIS,
"计算从各源地到空间各点的最小累积阻力",
depends_on=["identify_sources", "build_resistance"])
planner.add_task("extract_corridors", "提取生态廊道",
TaskType.ANALYSIS,
"基于MCR结果提取潜在生态廊道",
depends_on=["mcr_analysis"])
# 输出阶段
planner.add_task("visualize", "结果可视化",
TaskType.VISUALIZATION,
"生成源地、阻力面、廊道的可视化地图",
depends_on=["extract_corridors"])
planner.add_task("export_results", "导出分析结果",
TaskType.EXPORT,
"导出矢量数据和统计报告",
depends_on=["extract_corridors"])
return planner
@staticmethod
def site_selection_analysis() -> SpatialAnalysisPlanner:
"""
选址分析工作流
1. 加载约束数据
2. 加载候选地块
3. 叠加分析
4. 适宜性评价
5. 最优选址
6. 结果导出
"""
planner = SpatialAnalysisPlanner("设施选址分析")
planner.add_task("load_constraints", "加载约束数据",
TaskType.DATA_PREPARATION,
"加载坡度、保护区、道路等约束数据")
planner.add_task("load_candidates", "加载候选地块",
TaskType.DATA_PREPARATION,
"加载候选地块矢量数据")
planner.add_task("overlay_analysis", "叠加分析",
TaskType.ANALYSIS,
"将约束数据叠加到候选地块",
depends_on=["load_constraints", "load_candidates"])
planner.add_task("suitability", "适宜性评价",
TaskType.ANALYSIS,
"基于多准则评价计算适宜性得分",
depends_on=["overlay_analysis"])
planner.add_task("select_optimal", "最优选址",
TaskType.ANALYSIS,
"选择得分最高的地块",
depends_on=["suitability"])
planner.add_task("export", "导出选址方案",
TaskType.EXPORT,
"导出选址结果和评价报告",
depends_on=["select_optimal"])
return planner
# ==================== 演示程序 ====================
def demonstrate_spatial_planning():
"""演示空间分析规划系统"""
print("=" * 70)
print("空间分析任务规划器演示")
print("=" * 70)
# 1. 创建生态网络分析工作流
print("\n1. 创建生态网络分析工作流...")
planner = CommonAnalysisWorkflows.ecological_network_analysis()
# 2. 可视化执行计划
print("\n2. 执行计划DAG:")
print(planner.visualize_plan())
# 3. 获取执行计划
print("\n3. 执行层级:")
execution_plan = planner.get_execution_plan()
for i, level in enumerate(execution_plan, 1):
task_names = [planner.tasks[t].name for t in level]
print(f" 层级 {i}: {', '.join(task_names)}")
# 4. 模拟执行
print("\n4. 模拟执行:")
def mock_executor(task: Task) -> Dict:
"""模拟任务执行"""
import time
import random
time.sleep(0.1) # 模拟执行时间
# 模拟偶尔失败
if random.random() < 0.1:
raise Exception("模拟随机失败")
return {
'task': task.name,
'status': 'success',
'output': f"{task.name}_output"
}
results = planner.execute(mock_executor, max_retries=2)
# 5. 统计结果
print("\n5. 执行统计:")
status_count = {}
for task in planner.tasks.values():
status = task.status.value
status_count[status] = status_count.get(status, 0) + 1
for status, count in status_count.items():
print(f" {status}: {count}")
# 6. 创建自定义工作流
print("\n6. 创建自定义工作流:")
custom_planner = SpatialAnalysisPlanner("自定义分析")
custom_planner.add_task("t1", "数据加载", TaskType.DATA_PREPARATION)
custom_planner.add_task("t2", "数据清洗", TaskType.ANALYSIS, depends_on=["t1"])
custom_planner.add_task("t3", "统计分析", TaskType.ANALYSIS, depends_on=["t2"])
custom_planner.add_task("t4", "并行分析A", TaskType.ANALYSIS, depends_on=["t2"])
custom_planner.add_task("t5", "并行分析B", TaskType.ANALYSIS, depends_on=["t2"])
custom_planner.add_task("t6", "结果汇总", TaskType.ANALYSIS,
depends_on=["t3", "t4", "t5"])
print(custom_planner.visualize_plan())
if __name__ == "__main__":
demonstrate_spatial_planning()
RL风格的规划执行
class ReinforcementLearningPlanner:
"""
基于强化学习思想的规划执行
特点：
1. 从执行中学习
2. 评估行动效果
3. 更新策略
"""
def __init__(self, actions: List[Action],
reward_fn: Callable[[Any, Any, Any], float]):
"""
Args:
actions: 可用行动
reward_fn: 奖励函数 (state, action, next_state) -> reward
"""
self.actions = actions
self.reward_fn = reward_fn
# Q值表：state -> {action: q_value}
self.q_table: Dict[str, Dict[str, float]] = {}
# 学习参数
self.learning_rate = 0.1
self.discount_factor = 0.9
self.epsilon = 0.1 # 探索率
def select_action(self, state: Any, training: bool = True) -> Action:
"""
选择行动（epsilon-greedy策略）
Args:
state: 当前状态
training: 是否在训练模式
"""
state_key = self._state_key(state)
# 初始化Q值
if state_key not in self.q_table:
self.q_table[state_key] = {a.name: 0.0 for a in self.actions}
# epsilon-greedy
import random
if training and random.random() < self.epsilon:
# 探索：随机选择
return random.choice(self.actions)
else:
# 利用：选择Q值最大的
q_values = self.q_table[state_key]
best_action_name = max(q_values, key=q_values.get)
return next(a for a in self.actions if a.name == best_action_name)
def update_q_value(self, state: Any, action: Action,
reward: float, next_state: Any) -> None:
"""
更新Q值（Q-learning更新规则）
Q(s,a) ← Q(s,a) + α[r + γ max Q(s',a') - Q(s,a)]
"""
state_key = self._state_key(state)
next_state_key = self._state_key(next_state)
# 确保初始化
if next_state_key not in self.q_table:
self.q_table[next_state_key] = {
a.name: 0.0 for a in self.actions
}
# 计算新的Q值
current_q = self.q_table[state_key][action.name]
max_next_q = max(self.q_table[next_state_key].values())
new_q = current_q + self.learning_rate * (
reward + self.discount_factor * max_next_q - current_q
)
self.q_table[state_key][action.name] = new_q
def learn_episode(self, environment,
max_steps: int = 100) -> float:
"""
执行一个学习episode
Returns:
总奖励
"""
state = environment.reset()
total_reward = 0
for _ in range(max_steps):
# 选择行动
action = self.select_action(state, training=True)
# 执行行动
next_state, done = environment.step(action)
# 计算奖励
reward = self.reward_fn(state, action, next_state)
total_reward += reward
# 更新Q值
self.update_q_value(state, action, reward, next_state)
state = next_state
if done:
break
return total_reward
def _state_key(self, state: Any) -> str:
"""生成状态键（简化）"""
return str(hash(str(state)))
案例分析
LangGraph的规划执行
"""
LangGraph风格的规划执行
LangGraph使用状态图来表示和执行复杂的Agent工作流
"""
from typing import TypedDict, Annotated, Sequence
import operator
class AgentState(TypedDict):
"""Agent状态定义"""
messages: Annotated[Sequence[str], operator.add]
current_plan: list[str]
executed_steps: list[str]
requires_replan: bool
def should_continue(state: AgentState) -> str:
"""
条件边：决定继续执行还是结束
类似LangGraph的条件边
"""
if not state["current_plan"]:
return "end"
if state["requires_replan"]:
return "replan"
return "continue"
class LangGraphStylePlanner:
"""LangGraph风格的规划器"""
def __init__(self):
self.state = AgentState(
messages=[],
current_plan=[],
executed_steps=[],
requires_replan=False
)
self.nodes = {
"plan": self._plan_node,
"execute": self._execute_node,
"observe": self._observe_node,
"replan": self._replan_node
}
def build_graph(self) -> Dict:
"""构建执行图"""
return {
"nodes": self.nodes,
"edges": {
"plan": "execute",
"execute": "observe",
"observe": should_continue,
"continue": "execute",
"replan": "execute"
}
}
def _plan_node(self, state: AgentState) -> AgentState:
"""规划节点：生成初始计划"""
state["current_plan"] = ["step1", "step2", "step3"]
state["messages"].append("计划已生成")
return state
def _execute_node(self, state: AgentState) -> AgentState:
"""执行节点：执行下一步"""
if state["current_plan"]:
step = state["current_plan"].pop(0)
state["executed_steps"].append(step)
state["messages"].append(f"已执行: {step}")
return state
def _observe_node(self, state: AgentState) -> AgentState:
"""观察节点：检查是否需要重新规划"""
# 检查执行结果
state["requires_replan"] = False # 简化
return state
def _replan_node(self, state: AgentState) -> AgentState:
"""重规划节点：调整计划"""
state["current_plan"] = ["new_step"] + state["current_plan"]
state["requires_replan"] = False
state["messages"].append("计划已更新")
return state
def run(self, initial_goal: str) -> AgentState:
"""运行整个图"""
self.state["messages"].append(f"目标: {initial_goal}")
graph = self.build_graph()
current_node = "plan"
while current_node != "end":
# 执行节点
self.state = self.nodes[current_node](self.state)
# 获取下一个节点
next_node = graph["edges"].get(current_node)
if callable(next_node):
next_node = next_node(self.state)
current_node = next_node
return self.state
反思与延伸
思考问题
-
规划深度：多深的规划是合适的？过度规划会有什么问题？
-
不确定性：如何在不确定环境中进行规划？
-
多Agent协调：多个Agent如何协调各自的规划？
-
规划评估：如何评估一个规划的质量？
延伸阅读
- "Planning Algorithms" (LaValle) - 规划算法权威教材
- "Hierarchical Planning" - 分层规划专题
- "Reinforcement Learning" (Sutton & Barto) - RL中的规划与学习
关键要点
- 规划是寻找从初始状态到目标的行动序列
- 前向搜索从初始状态向目标搜索，适合目标明确的场景
- 后向搜索从目标向初始状态搜索，适合目标较少的场景
- 分层规划将复杂任务分解，适合复杂多阶段任务
- 重规划是应对环境变化的关键机制
- 执行-监控-调整循环是实际系统的核心模式
- 任务依赖管理使用DAG表达和并行化执行