299 lines
11 KiB
Python
299 lines
11 KiB
Python
#!/usr/bin/env python3
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# -*- coding: UTF-8 -*-
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###########################################################################
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# Copyright © 1998 - 2026 Tencent. All Rights Reserved.
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###########################################################################
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"""
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Author: Tencent AI Arena Authors
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Feature preprocessor for Robot Vacuum.
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清扫大作战特征预处理器。
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"""
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import numpy as np
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def _norm(v, v_max, v_min=0.0):
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"""Normalize value to [0, 1].
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将值线性归一化到 [0, 1]。
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"""
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v = float(np.clip(v, v_min, v_max))
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if v_max == v_min:
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return 0.0
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return (v - v_min) / (v_max - v_min)
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class Preprocessor:
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"""Feature preprocessor for Robot Vacuum.
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清扫大作战特征预处理器。
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"""
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GRID_SIZE = 128
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VIEW_HALF = 10 # Full local view radius (21×21) / 完整局部视野半径
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LOCAL_HALF = 3 # Cropped view radius (7×7) / 裁剪后的视野半径
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def __init__(self):
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self.reset()
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def reset(self):
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"""Reset all internal state at episode start.
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对局开始时重置所有状态。
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"""
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self.step_no = 0
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self.battery = 600
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self.battery_max = 600
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self.cur_pos = (0, 0)
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self.prev_pos = None
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self.has_position_history = False
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self.current_visit_count = 0
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self.is_new_cell = False
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self.last_action = -1
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self.dirt_cleaned = 0
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self.last_dirt_cleaned = 0
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self.total_dirt = 1
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# Global passable map (0=obstacle, 1=passable), used for ray computation
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# 维护全局通行地图（0=障碍, 1=可通行），用于射线计算
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self.passable_map = np.ones((self.GRID_SIZE, self.GRID_SIZE), dtype=np.int8)
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# Nearest dirt distance
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# 最近污渍距离
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self.nearest_dirt_dist = 200.0
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self.last_nearest_dirt_dist = 200.0
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self.visit_count_map = np.zeros((self.GRID_SIZE, self.GRID_SIZE), dtype=np.uint16)
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self._view_map = np.zeros((21, 21), dtype=np.float32)
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self._legal_act = [1] * 8
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def pb2struct(self, env_obs, last_action):
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"""Parse and cache essential fields from observation dict.
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从 env_obs 字典中提取并缓存所有需要的状态量。
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"""
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observation = env_obs["observation"]
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frame_state = observation["frame_state"]
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env_info = observation["env_info"]
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hero = frame_state["heroes"]
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self.last_action = int(last_action)
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self.step_no = int(observation["step_no"])
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self.prev_pos = self.cur_pos if self.has_position_history else None
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self.cur_pos = (int(hero["pos"]["x"]), int(hero["pos"]["z"]))
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self.has_position_history = True
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hx, hz = self.cur_pos
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if 0 <= hx < self.GRID_SIZE and 0 <= hz < self.GRID_SIZE:
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self.current_visit_count = int(self.visit_count_map[hx, hz])
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self.is_new_cell = self.current_visit_count == 0
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self.visit_count_map[hx, hz] = min(self.current_visit_count + 1, np.iinfo(np.uint16).max)
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else:
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self.current_visit_count = 0
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self.is_new_cell = False
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# Battery / 电量
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self.battery = int(hero["battery"])
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self.battery_max = max(int(hero["battery_max"]), 1)
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# Cleaning progress / 清扫进度
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self.last_dirt_cleaned = self.dirt_cleaned
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self.dirt_cleaned = int(hero["dirt_cleaned"])
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self.total_dirt = max(int(env_info["total_dirt"]), 1)
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# Legal actions / 合法动作
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self._legal_act = [int(x) for x in (observation.get("legal_action") or [1] * 8)]
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# Local view map (21×21) / 局部视野地图
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map_info = observation.get("map_info")
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if map_info is not None:
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self._view_map = np.array(map_info, dtype=np.float32)
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hx, hz = self.cur_pos
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self._update_passable(hx, hz)
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def _update_passable(self, hx, hz):
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"""Write local view into global passable map.
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将局部视野写入全局通行地图。
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"""
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view = self._view_map
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vsize = view.shape[0]
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half = vsize // 2
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for ri in range(vsize):
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for ci in range(vsize):
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gx = hx - half + ri
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gz = hz - half + ci
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if 0 <= gx < self.GRID_SIZE and 0 <= gz < self.GRID_SIZE:
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# 0 = obstacle, 1/2 = passable
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# 0 = 障碍, 1/2 = 可通行
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self.passable_map[gx, gz] = 1 if view[ri, ci] != 0 else 0
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def _get_local_view_feature(self):
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"""Local view feature (49D): crop center 7×7 from 21×21.
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局部视野特征（49D）：从 21×21 视野中心裁剪 7×7。
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"""
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center = self.VIEW_HALF
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h = self.LOCAL_HALF
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crop = self._view_map[center - h : center + h + 1, center - h : center + h + 1]
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return (crop / 2.0).flatten()
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def _get_global_state_feature(self):
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"""Global state feature (12D).
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全局状态特征（12D）。
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Dimensions / 维度说明：
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[0] step_norm step progress / 步数归一化 [0,1]
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[1] battery_ratio battery level / 电量比 [0,1]
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[2] cleaning_progress cleaned ratio / 已清扫比例 [0,1]
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[3] remaining_dirt remaining dirt ratio / 剩余污渍比例 [0,1]
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[4] pos_x_norm x position / x 坐标归一化 [0,1]
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[5] pos_z_norm z position / z 坐标归一化 [0,1]
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[6] ray_N_dirt north ray distance / 向上（z-）方向最近污渍距离
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[7] ray_E_dirt east ray distance / 向右（x+）方向
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[8] ray_S_dirt south ray distance / 向下（z+）方向
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[9] ray_W_dirt west ray distance / 向左（x-）方向
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[10] nearest_dirt_norm nearest dirt Euclidean distance / 最近污渍欧氏距离归一化
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[11] dirt_delta approaching dirt indicator / 是否在接近污渍（1=是, 0=否）
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"""
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step_norm = _norm(self.step_no, 2000)
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battery_ratio = _norm(self.battery, self.battery_max)
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cleaning_progress = _norm(self.dirt_cleaned, self.total_dirt)
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remaining_dirt = 1.0 - cleaning_progress
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hx, hz = self.cur_pos
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pos_x_norm = _norm(hx, self.GRID_SIZE)
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pos_z_norm = _norm(hz, self.GRID_SIZE)
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# 4-directional ray to find nearest dirt
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# 四方向射线找最近污渍距离
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ray_dirs = [(0, -1), (1, 0), (0, 1), (-1, 0)] # N E S W
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ray_dirt = []
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max_ray = 30
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for dx, dz in ray_dirs:
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x, z = hx, hz
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found = max_ray
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for step in range(1, max_ray + 1):
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x += dx
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z += dz
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if not (0 <= x < self.GRID_SIZE and 0 <= z < self.GRID_SIZE):
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break
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if self._view_map is not None:
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cell = (
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int(
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self._view_map[
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np.clip(x - (hx - self.VIEW_HALF), 0, 20), np.clip(z - (hz - self.VIEW_HALF), 0, 20)
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]
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)
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if (0 <= x - hx + self.VIEW_HALF < 21 and 0 <= z - hz + self.VIEW_HALF < 21)
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else 0
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)
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if cell == 2:
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found = step
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break
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ray_dirt.append(_norm(found, max_ray))
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# Nearest dirt Euclidean distance (estimated from 7×7 crop)
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# 最近污渍欧氏距离（视野内 7×7 粗估）
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self.last_nearest_dirt_dist = self.nearest_dirt_dist
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self.nearest_dirt_dist = self._calc_nearest_dirt_dist()
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nearest_dirt_norm = _norm(self.nearest_dirt_dist, 180)
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dirt_delta = 1.0 if self.nearest_dirt_dist < self.last_nearest_dirt_dist else 0.0
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return np.array(
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[
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step_norm,
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battery_ratio,
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cleaning_progress,
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remaining_dirt,
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pos_x_norm,
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pos_z_norm,
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ray_dirt[0],
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ray_dirt[1],
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ray_dirt[2],
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ray_dirt[3],
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nearest_dirt_norm,
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dirt_delta,
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],
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dtype=np.float32,
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)
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def _calc_nearest_dirt_dist(self):
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"""Find nearest dirt Euclidean distance from local view.
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从局部视野中找最近污渍的欧氏距离。
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"""
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view = self._view_map
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if view is None:
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return 200.0
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dirt_coords = np.argwhere(view == 2)
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if len(dirt_coords) == 0:
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return 200.0
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center = self.VIEW_HALF
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dists = np.sqrt((dirt_coords[:, 0] - center) ** 2 + (dirt_coords[:, 1] - center) ** 2)
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return float(np.min(dists))
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def get_legal_action(self):
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"""Return legal action mask (8D list).
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返回合法动作掩码（8D list）。
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"""
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return list(self._legal_act)
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def feature_process(self, env_obs, last_action):
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"""Generate 69D feature vector, legal action mask, and scalar reward.
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生成 69D 特征向量、合法动作掩码和标量奖励。
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"""
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self.pb2struct(env_obs, last_action)
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local_view = self._get_local_view_feature() # 49D
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global_state = self._get_global_state_feature() # 12D
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legal_action = self.get_legal_action() # 8D
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last_action_feature = np.zeros(8, dtype=np.float32)
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if 0 <= last_action < 8:
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last_action_feature[last_action] = 1.0
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# The legal action mask is passed separately to PPO. Reusing this 8D slot
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# for action history makes the 69D observation more informative without
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# breaking the framework's fixed tensor shape.
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feature = np.concatenate([local_view, global_state, last_action_feature]) # 69D
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reward = self.reward_process()
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return feature, legal_action, reward
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def reward_process(self):
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# Cleaning reward / 清扫奖励
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cleaned_this_step = max(0, self.dirt_cleaned - self.last_dirt_cleaned)
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cleaning_reward = 0.25 * cleaned_this_step
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# Step penalty / 时间惩罚
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step_penalty = -0.002
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# Dense guidance: prefer moving toward visible dirt.
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# 稠密引导：鼓励向视野内污渍靠近。
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approach_reward = 0.0
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if self.last_nearest_dirt_dist < 200.0 or self.nearest_dirt_dist < 200.0:
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dist_delta = float(np.clip(self.last_nearest_dirt_dist - self.nearest_dirt_dist, -5.0, 5.0))
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approach_reward = 0.01 * dist_delta if dist_delta > 0 else 0.006 * dist_delta
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# Encourage covering new passable cells and mildly discourage loops.
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# 鼓励探索新格子，轻微惩罚反复绕圈。
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exploration_reward = 0.002 if self.is_new_cell else -0.0008 * min(self.current_visit_count, 5)
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# Collision/stuck signal: invalid moves waste both step and battery.
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# 撞墙/原地不动会浪费步数和电量。
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stuck_penalty = 0.0
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if self.prev_pos is not None and self.cur_pos == self.prev_pos and 0 <= self.last_action < 8:
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stuck_penalty = -0.03
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return cleaning_reward + approach_reward + exploration_reward + stuck_penalty + step_penalty
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