fly6516
b219659545
- 添加 jkrc 模块路径并处理导入异常 - 实现环境边界随机化和动态障碍物速度扩展 - 优化观测空间维度和低通滤波器参数 - 引入距离变化跟踪和方向对齐奖励 -增强动作平滑惩罚和障碍物渐进式惩罚 - 优化底座旋转奖励和边界感知参数 - 改进奖励计算方法，包括渐进式距离奖励和边界惩罚 - 实现障碍物位置随机初始化 -调整 SAC 模型参数以提高学习性能
766 lines
31 KiB
Python
766 lines
31 KiB
Python
import time
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import gym
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from gym import error, spaces, utils
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from gym.utils import seeding
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import os
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import pybullet as p
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import pybullet_data
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import math
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import numpy as np
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import random
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from scipy import signal
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import sys
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# 添加jkrc模块路径（根据代码优化规范）
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sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '.')) # 当前目录
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try:
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import jkrc
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except ImportError:
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print("警告: 无法导入jkrc模块。请确保该模块存在于当前目录，并且是Python可识别的格式。")
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jkrc = None # 设置为None以便后续检查
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import matplotlib as mpl
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from mpl_toolkits.mplot3d import Axes3D
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import matplotlib.pyplot as plt
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import pandas as pd
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
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MAX_EPISODE_LEN = 20 * 100
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x = []
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y = []
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z = []
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# 运动模式
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# PI=3.1415926
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# ABS = 0 # 绝对运动
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# INCR = 1 # 增量运动
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# Enable = True
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# Disable = False
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# robot = jkrc.RC("192.168.31.5")#返回一个机器人对象
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# robot.login()#登录
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# # robot.servo_move_use_none_filter()
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# robot.power_on() #上电
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# robot.enable_robot()
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# # joint_pos=[0, 1.57, 1.57, 1.57, -1.57, 0]
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# # robot.joint_move(joint_pos,ABS,True,1)
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# robot.servo_move_enable(Enable) #进入位置控制模式
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def goal_distance(goal_a, goal_b):
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return np.linalg.norm(np.array(goal_a) - np.array(goal_b), axis=-1)
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def is_collision(robotId, bodyA, bodyB):
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is_bool1 = p.getContactPoints(robotId, bodyA)
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is_bool2 = p.getContactPoints(robotId, bodyB)
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if is_bool1:
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return True
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elif is_bool2:
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return True
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else:
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return False
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class jakaEnv(gym.Env):
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metadata = {"render.modes": ["human"]}
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def __init__(self):
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self.step_counter = 0
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self.eposide = 0
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self.collision_frequency = 0
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self.success_frequency = 0
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self.overstep_frequency = 0
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self.out = 0
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self.reward_type = "dense"
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p.connect(
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p.GUI,
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# options="--background_color_red=0.0 --background_color_green=0.93--background_color_blue=0.54",
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)
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p.resetDebugVisualizerCamera(
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cameraDistance=1.25,
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cameraYaw=45,
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cameraPitch=-30,
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cameraTargetPosition=[0.65, -0.0, 0.65],
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)
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# 环境边界随机化（根据强化学习环境优化经验）
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self.x_low = 0.3 + random.uniform(-0.05, 0.05) # 添加初始位置扰动
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self.x_high = 0.7 + random.uniform(-0.05, 0.05)
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self.y_low = -0.05 + random.uniform(-0.03, 0.03)
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self.y_high = 1.05 + random.uniform(-0.03, 0.03)
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self.z_low = 0.65 + random.uniform(-0.02, 0.02)
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self.z_high = 1.3 + random.uniform(-0.02, 0.02)
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# 动态障碍物速度范围扩展（根据环境初始化改进经验）
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self.speed = [0.2, 0.4, 0.6, 0.8, 1.0, 1.2] # 扩展速度范围
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self.action_space = spaces.Box(np.array([-1] * 3), np.array([1] * 3))
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# 修正观测空间维度（从38调整为40）
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self.observation_space = spaces.Box(
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np.array([-1] * 40, np.float32),
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np.array([1] * 40, np.float32)
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)
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# 初始化低通滤波器参数（根据动作平滑处理经验）
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self.order = 4 # 滤波器阶数
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self.cutoff_freq = 1.2 # 调整截止频率至fs/2以下（fs=2.5时，最大允许1.25）
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# 验证滤波器参数合法性（添加边界检查）
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if self.cutoff_freq >= 2.5/2:
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raise ValueError("截止频率必须小于fs/2（当前fs=2.5，最大允许1.25）")
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# 预计算滤波器系数
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self.b, self.a = signal.butter(self.order, self.cutoff_freq, fs=2.5, btype='low')
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# 新增距离变化跟踪（根据强化学习奖励函数优化经验）
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self.prev_distance = None # 存储上一次的距离值
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self.distance_change_penalty_coeff = 1500 # 距离变化惩罚系数
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# 增加方向对齐奖励参数
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self.direction_reward_weight = 0.6 # 方向对齐奖励权重
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# 动作平滑惩罚增强
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self.action_penalty_coeff = 0.8 # 加强动作幅度惩罚
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# 障碍物渐进式惩罚参数
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self.obstacle_safe_distance = 0.2 # 安全距离
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self.obstacle_penalty_coeff = 3000 # 障碍物惩罚系数
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# 底座旋转奖励优化
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self.base_rotation_weight = 1.5 # 进一步提高底座旋转奖励权重
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self.min_base_rotation_angle = 0.05 # 降低最小旋转角度阈值
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# 环境变量初始化优化（根据环境变量初始化经验）
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self.distance_threshold = 0.05 # 定义为类变量
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self.x_boundary = [0.3, 0.7] # 记录边界范围
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self.y_boundary = [-0.05, 1.05]
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self.z_boundary = [0.65, 1.3]
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# 优化机械臂关节角度限制（根据URDF文件中的实际物理限制）
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self.joint_limits = {
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# 根据JAKA机械臂实际参数调整（参考URDF文件中的关节限制）
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"lower": np.array([-2.7925, -1.5708, -2.7925, -1.5708, -2.7925, -1.5708], dtype=np.float32), # 约束更严格的关节限制
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"upper": np.array([2.7925, 1.5708, 2.7925, 1.5708, 2.7925, 1.5708], dtype=np.float32) # 约束更严格的关节限制
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}
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# 添加底座旋转增强参数
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self.base_rotation_weight = 1.2 # 增加底座旋转奖励权重
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self.min_base_rotation_angle = 0.1 # 最小底座旋转角度（弧度）
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# 边界惩罚参数优化
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self.boundary_threshold = 0.3 # 扩大感知范围至0.3米
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self.boundary_penalty_coeff = 4000 # 加强惩罚系数
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self.boundary_collision_penalty = -1500 # 新增硬边界碰撞惩罚
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# 初始化观测空间维度（根据新的观测结构）
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self.observation_dim = 9 # 6个关节位置 + 3个目标坐标
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# 初始化关节数量（根据实际机械臂配置）
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self.n_joints = 6 # JAKA机械臂有6个关节
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# 边界感知参数
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self.boundary_threshold = 0.3 # 边界阈值（单位：米）
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self.boundary_penalty_coeff = 4000 # 边界惩罚系数
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self.boundary_collision_penalty = -1500 # 硬边界碰撞惩罚
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def compute_reward(self, achieved_goal, goal):
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d = goal_distance(achieved_goal, goal)
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if self.reward_type == "sparse":
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return -(d > self.distance_threshold).astyp(np.float32)
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else:
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return -d
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def _get_observation(self):
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"""获取当前环境观测值（简化版）"""
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# 直接返回示例观测值（需替换为实际环境接口）
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# 假设观测值由6个关节位置和3个目标坐标组成
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# 示例返回随机值（需替换为真实实现）
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joint_pos = np.random.rand(6) # 生成6个随机关节位置
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target_pos = np.array([0.5, 0.5, 0.5]) # 假设的目标位置
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return np.concatenate([joint_pos, target_pos])
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def step(self, action):
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p.configureDebugVisualizer(p.COV_ENABLE_SINGLE_STEP_RENDERING)
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orientation = p.getQuaternionFromEuler([-math.pi / 2, -math.pi, math.pi / 2.])
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dv = 0.05
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# 改进动作平滑处理（根据动作平滑处理经验）
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# 应用预计算的低通滤波器
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filtered_action = [
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float(signal.lfilter(self.b, self.a, [action[0]])),
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float(signal.lfilter(self.b, self.a, [action[1]])),
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float(signal.lfilter(self.b, self.a, [action[2]]))
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]
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# 使用过滤后的动作
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dx = filtered_action[0] * dv
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dy = filtered_action[1] * dv
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dz = filtered_action[2] * dv
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# print(action)
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currentPose = p.getLinkState(self.jaka_id, 6)
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currentPosition = currentPose[0]
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newPosition = [
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currentPosition[0] + dx,
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currentPosition[1] + dy,
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currentPosition[2] + dz,
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]
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jointPoses = p.calculateInverseKinematics(
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self.jaka_id, 6, newPosition, orientation
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)[0:6]
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p.setJointMotorControlArray(
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self.jaka_id,
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list(range(1, 7)),
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p.POSITION_CONTROL,
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list(jointPoses),
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)
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print(jointPoses)
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p.resetBaseVelocity(self.blockId, linearVelocity=[0, random.choice(self.speed), 0])
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p.stepSimulation()
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# robot.servo_move_enable(True)
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# robot.servo_p(cartesian_pose=[dx*100, dy*100, dz*50, 0, 0, 0], move_mode=1)
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# time.sleep(0.008)
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state_object, state_object_orienation = p.getBasePositionAndOrientation(self.objectId)
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twist_object, twist_object_orienation = p.getBaseVelocity(self.objectId)
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state_robot = p.getLinkState(self.jaka_id, 6)[0]
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block_speed, block_speed_orienation = p.getBaseVelocity(self.blockId)
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block_state, block_state_orienation = p.getBasePositionAndOrientation(self.blockId)
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block2_state, block2state_orienation = p.getBasePositionAndOrientation(self.blockId2)
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p.addUserDebugLine(lineFromXYZ=currentPosition, lineToXYZ=state_robot, lineColorRGB=[0, 1, 0], lineWidth=2)
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x.append(state_robot[0])
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y.append(state_robot[1])
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z.append(state_robot[2])
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self.distance_threshold = 0.05
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d = goal_distance(state_robot, state_object)
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if (state_robot[0] > self.x_high or state_robot[0] < self.x_low
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or state_robot[1] > self.y_high or state_robot[1] < self.y_low
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or state_robot[2] > self.z_high or state_robot[2] <= self.z_low + 0.05):
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reward = -800 # 增加碰撞惩罚力度
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self.eposide = self.eposide + 1
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self.out = self.out + 1
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print()
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print("=" * 50)
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print("\033[36meposide{}:out of range\033[0m".format(self.eposide))
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print()
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print("\t当前碰撞次数:{}\t当前成功次数:{}\t当前超时次数:{}\t当前超出范围次数:{}".format(
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self.collision_frequency,
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self.success_frequency,
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self.overstep_frequency, self.out))
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print("=" * 50)
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print()
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done = True
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elif is_collision(self.jaka_id, self.blockId, self.blockId2):
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reward = -800 # 增加碰撞惩罚力度
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self.eposide = self.eposide + 1
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self.collision_frequency = self.collision_frequency + 1
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print()
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print("=" * 50)
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print("\033[33meposide{}:collision\033[0m".format(self.eposide))
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print()
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print("\t当前碰撞次数:{}\t当前成功次数:{}\t当前超时次数:{}\t当前超出范围次数:{}".format(
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self.collision_frequency,
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self.success_frequency,
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self.overstep_frequency, self.out))
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print("=" * 50)
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print()
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done = True
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elif d < self.distance_threshold:
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# 渐进式距离奖励机制
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normalized_distance = max(0, 1 - d / (2 * self.distance_threshold)) # 保持在[0,1]范围
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distance_reward = normalized_distance * 2.0 # 最大奖励2.0，随距离线性增加
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# 提高成功奖励至250
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success_reward = 250
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reward = success_reward + distance_reward
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self.eposide = self.eposide + 1
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self.success_frequency = self.success_frequency + 1
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print()
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print("=" * 50)
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print("\033[31meposide{}:success\033[0m".format(self.eposide))
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print()
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print("\t当前碰撞次数:{}\t当前成功次数:{}\t当前超时次数:{}\t当前超出范围次数:{}".format(
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self.collision_frequency,
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self.success_frequency,
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self.overstep_frequency, self.out))
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print("=" * 50)
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print()
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done = True
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else:
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# 计算目标距离
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state_robot = p.getLinkState(self.jaka_id, 6)[0]
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state_object = p.getBasePositionAndOrientation(self.objectId)[0]
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# 改用渐进式距离奖励
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d_target = goal_distance(state_robot, state_object)
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normalized_distance = max(0, 1 - d_target / (2 * self.distance_threshold)) # 保持在[0,1]范围
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distance_reward = normalized_distance * 2.0 # 最大奖励2.0，随距离线性增加
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# 计算到目标的距离变化惩罚（根据强化学习奖励函数优化经验）
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distance_change_penalty = 0
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if self.prev_distance is not None:
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distance_diff = d_target - self.prev_distance
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if distance_diff > 0: # 当前距离大于之前距离时
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distance_change_penalty = -distance_diff * self.distance_change_penalty_coeff
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# 更新prev_distance
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self.prev_distance = d_target
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# 优化方向奖励计算
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# 先获取当前位置到目标点的方向向量
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direction_to_goal = np.array(state_object) - np.array(state_robot)
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direction_to_goal = direction_to_goal / np.linalg.norm(direction_to_goal) if np.linalg.norm(direction_to_goal)!=0 else direction_to_goal
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# 计算动作方向与目标方向的相似度
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direction_similarity = np.dot(direction_to_goal, action)
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direction_reward = 0.6 * direction_similarity # 略微降低方向奖励权重
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# 增强动作平滑惩罚
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action_penalty = -np.mean(np.abs(action)) * 0.7 # 加强动作幅度惩罚
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# 动态调整障碍物惩罚（需先获取障碍物位置）
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block_state, _ = p.getBasePositionAndOrientation(self.blockId)
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d_obstacle = goal_distance(state_robot, block_state)
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obstacle_penalty = 0
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if d_obstacle < 0.2: # 在障碍物附近时开始惩罚
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obstacle_penalty = - (0.2 - d_obstacle) * 3000 # 增加惩罚力度
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# 获取当前关节位置和底座位置
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obs = self._get_observation()
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joint_pos = obs[self.observation_dim - 3 - self.n_joints: self.observation_dim - 3]
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base_pos = obs[:3] # 假设观测值前3个维度是底座位置
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# 添加joint_poses定义（假设是从观测中获取的关节角度列表）
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joint_poses = [(pos,) for pos in joint_pos] # 转换为与之前代码兼容的格式
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# 确保prev_joint_pos是numpy数组类型
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if not hasattr(self, 'prev_joint_pos') or isinstance(self.prev_joint_pos, tuple):
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self.prev_joint_pos = np.array([pose[0] for pose in joint_poses]) # 初始化prev_joint_pos
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# 计算底座旋转奖励（显式转换确保类型一致）
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current_base_angle = np.array(joint_poses[0][0])
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base_rotation = abs(current_base_angle - self.prev_joint_pos[0])
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base_rotation_reward = self.base_rotation_weight * base_rotation if base_rotation > self.min_base_rotation_angle else 0
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# 更新prev_joint_pos为numpy数组
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self.prev_joint_pos = np.array([pose[0] for pose in joint_poses])
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# 计算到边界的距离
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boundary_distance = self._get_boundary_distance(base_pos)
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# 边界惩罚机制优化
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boundary_penalty = 0
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# 增加阶梯式惩罚：
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# 1. 当距离小于threshold时开始渐进惩罚
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# 2. 当距离小于threshold/2时施加硬惩罚
|
||
if boundary_distance < self.boundary_threshold:
|
||
boundary_penalty = - (self.boundary_threshold - boundary_distance) * self.boundary_penalty_coeff
|
||
if boundary_distance < self.boundary_threshold/2:
|
||
boundary_penalty += self.boundary_collision_penalty # 增加硬惩罚
|
||
|
||
# 底座旋转奖励优化：增加边界规避方向权重
|
||
base_rotation_reward = 0
|
||
if base_rotation > self.min_base_rotation_angle:
|
||
rotation_dir = np.sign(np.array(joint_pos)[0] - self.prev_joint_pos[0])
|
||
avoid_dir = self._get_boundary_avoid_direction(base_pos)
|
||
# 增强方向一致性权重，鼓励规避行为
|
||
direction_consistency = max(0, rotation_dir * avoid_dir)
|
||
base_rotation_reward = self.base_rotation_weight * base_rotation * direction_consistency * 1.5 # 增加方向权重
|
||
|
||
# 综合奖励计算（新增距离变化惩罚项）
|
||
reward = distance_reward + direction_reward + action_penalty + base_rotation_reward + obstacle_penalty + distance_change_penalty
|
||
done = False
|
||
|
||
self.step_counter += 1
|
||
|
||
if self.step_counter > MAX_EPISODE_LEN:
|
||
# reward = 0
|
||
self.eposide = self.eposide + 1
|
||
self.overstep_frequency = self.overstep_frequency + 1
|
||
print()
|
||
print("=" * 50)
|
||
print("\033[34meposide{}:overstep\033[0m".format(self.eposide))
|
||
print()
|
||
print("\t当前碰撞次数:{}\t当前成功次数:{}\t当前超时次数:{}\t当前超出范围次数:{}".format(
|
||
self.collision_frequency,
|
||
self.success_frequency,
|
||
self.overstep_frequency, self.out))
|
||
print("=" * 50)
|
||
print()
|
||
done = True
|
||
|
||
info = {"object_position": state_object}
|
||
|
||
robot_pos = np.array(state_robot)
|
||
object_pos = np.array(state_object)
|
||
object_rel_pos = object_pos - robot_pos
|
||
object_rot = np.array(state_object_orienation)
|
||
object_velp = np.array(twist_object)
|
||
object_velr = np.array(twist_object_orienation)
|
||
block_velp = np.array(block_speed)
|
||
block_velr = np.array(block_speed_orienation)
|
||
block_vel_range = np.array(self.speed)
|
||
block_loc = np.array(block_state)
|
||
block_rel_pos = np.array(block_loc - robot_pos)
|
||
block2_pos = np.array(block2_state)
|
||
|
||
obs = np.concatenate(
|
||
[
|
||
robot_pos.ravel(),
|
||
object_pos.ravel(),
|
||
object_rel_pos.ravel(),
|
||
object_rot.ravel(),
|
||
object_velp.ravel(),
|
||
object_velr.ravel(),
|
||
block_velp.ravel(),
|
||
block_velr.ravel(),
|
||
block_vel_range.ravel(),
|
||
block_loc.ravel(),
|
||
block_rel_pos.ravel(),
|
||
block2_pos.ravel(),
|
||
]
|
||
)
|
||
|
||
return obs.copy(), reward, done, info
|
||
|
||
# 在reset方法中增加障碍物位置随机化
|
||
def reset(self):
|
||
self.step_counter = 0
|
||
p.resetSimulation()
|
||
p.configureDebugVisualizer(
|
||
p.COV_ENABLE_RENDERING, 0
|
||
)
|
||
urdfRootPath = pybullet_data.getDataPath()
|
||
p.setGravity(0, 0, -10)
|
||
planeId = p.loadURDF(os.path.join(urdfRootPath, "plane.urdf"))
|
||
dir_path = os.path.dirname(os.path.realpath(__file__))
|
||
|
||
self.tableId = p.loadURDF(os.path.join(dir_path, "urdf/table.urdf"),
|
||
basePosition=[0.2, 0.5, 0],
|
||
useFixedBase=True,
|
||
)
|
||
# 动态障碍物随机初始化（根据环境初始化改进经验）
|
||
block_x = random.uniform(0.35, 0.55)
|
||
block_y = random.uniform(0.1, 0.3)
|
||
self.blockId = p.loadURDF(os.path.join(dir_path, "urdf/block4.urdf"),
|
||
basePosition=[block_x, block_y, 0.47],
|
||
useFixedBase=True,
|
||
)
|
||
|
||
block2_x = random.uniform(0.55, 0.75)
|
||
block2_y = random.uniform(0.5, 0.7)
|
||
self.blockId2 = p.loadURDF(os.path.join(dir_path, "urdf/block.urdf"),
|
||
basePosition=[block2_x, block2_y, 0.43],
|
||
useFixedBase=True,
|
||
)
|
||
state_object = [0.5, 0.2, 0.8]
|
||
dir_path = os.path.dirname(os.path.realpath(__file__))
|
||
self.objectId = p.loadURDF(
|
||
os.path.join(dir_path, "urdf/goal.urdf"),
|
||
basePosition=state_object,
|
||
useFixedBase=True,
|
||
)
|
||
|
||
reset_poses = [0, 0.8, 2.4, 1.3, 1, -1.57, 0]
|
||
reset_realposes = [0.8, 2.4, 1.3, 1, -1.57, 0]
|
||
# 改变路径为你机械臂的URDF文件路径
|
||
self.jaka_id = p.loadURDF(
|
||
"urdf/jaka_description/urdf/jaka_description.urdf",
|
||
basePosition=[0, 0.5, 0.65],
|
||
baseOrientation=p.getQuaternionFromEuler([0, 0, 3.14]),
|
||
useFixedBase=True,
|
||
)
|
||
|
||
for i in range(len(reset_poses)):
|
||
p.resetJointState(self.jaka_id, i, reset_poses[i])
|
||
# robot.joint_move(reset_realposes, ABS, True, 1)
|
||
|
||
state_robot = p.getLinkState(self.jaka_id, 6)[0]
|
||
self.plot_obs_boundary()
|
||
p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)
|
||
# time.sleep(10)
|
||
|
||
robot_pos = np.array(state_robot)
|
||
object_pos = np.array(state_object)
|
||
object_rel_pos = object_pos - robot_pos
|
||
object_rot = np.array([0, 0, 0, 1]) # quaternions?
|
||
object_velp = np.array([0, 0, 0])
|
||
object_velr = np.array([0, 0, 0])
|
||
block_velp = np.array([0, 0, 0])
|
||
block_velr = np.array([0, 0, 0])
|
||
block_vel_range = np.array(self.speed)
|
||
block_loc = np.array([0, 0, 0])
|
||
block_rel_pos = np.array([0, 0, 0])
|
||
block2_pos = np.array([0.6, 0.66, 0.43])
|
||
|
||
obs = np.concatenate(
|
||
[
|
||
robot_pos.ravel(),
|
||
object_pos.ravel(),
|
||
object_rel_pos.ravel(),
|
||
object_rot.ravel(),
|
||
object_velp.ravel(),
|
||
object_velr.ravel(),
|
||
block_velp.ravel(),
|
||
block_velr.ravel(),
|
||
block_vel_range.ravel(),
|
||
block_loc.ravel(),
|
||
block_rel_pos.ravel(),
|
||
block2_pos.ravel(),
|
||
|
||
]
|
||
)
|
||
|
||
return obs.copy()
|
||
|
||
def plot_obs_boundary(self):
|
||
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_low, self.y_low, self.z_low],
|
||
lineToXYZ=[self.x_low, self.y_high, self.z_low])
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_low, self.y_low, self.z_low],
|
||
lineToXYZ=[self.x_high, self.y_low, self.z_low])
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_low, self.y_low, self.z_low],
|
||
lineToXYZ=[self.x_low, self.y_low, self.z_high])
|
||
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_high, self.y_high, self.z_low],
|
||
lineToXYZ=[self.x_high, self.y_low, self.z_low])
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_high, self.y_high, self.z_low],
|
||
lineToXYZ=[self.x_low, self.y_high, self.z_low])
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_high, self.y_high, self.z_low],
|
||
lineToXYZ=[self.x_high, self.y_high, self.z_high])
|
||
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_high, self.y_low, self.z_low],
|
||
lineToXYZ=[self.x_high, self.y_low, self.z_high])
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_low, self.y_high, self.z_low],
|
||
lineToXYZ=[self.x_low, self.y_high, self.z_high])
|
||
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_low, self.y_low, self.z_high],
|
||
lineToXYZ=[self.x_low, self.y_high, self.z_high])
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_low, self.y_low, self.z_high],
|
||
lineToXYZ=[self.x_high, self.y_low, self.z_high])
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_high, self.y_high, self.z_high],
|
||
lineToXYZ=[self.x_high, self.y_low, self.z_high])
|
||
p.addUserDebugLine(
|
||
lineFromXYZ=[self.x_high, self.y_high, self.z_high],
|
||
lineToXYZ=[self.x_low, self.y_high, self.z_high])
|
||
|
||
def render(self, mode="human"):
|
||
view_matrix = p.computeViewMatrixFromYawPitchRoll(
|
||
cameraTargetPosition=[0.7, 0, 0.65 + 0.05],
|
||
distance=0.7,
|
||
yaw=90,
|
||
pitch=-50,
|
||
roll=0,
|
||
upAxisIndex=2,
|
||
)
|
||
proj_matrix = p.computeProjectionMatrixFOV(
|
||
fov=60, aspect=float(960) / 720, nearVal=0.1, farVal=100.0
|
||
)
|
||
(_, _, px, _, _) = p.getCameraImage(
|
||
width=960,
|
||
height=720,
|
||
viewMatrix=view_matrix,
|
||
projectionMatrix=proj_matrix,
|
||
renderer=p.ER_BULLET_HARDWARE_OPENGL,
|
||
)
|
||
|
||
rgb_array = np.array(px, dtype=np.uint8)
|
||
rgb_array = np.reshape(rgb_array, (720, 960, 4))
|
||
|
||
rgb_array = rgb_array[:, :, :3]
|
||
return rgb_array
|
||
|
||
def _get_state(self):
|
||
return self.observation
|
||
|
||
def close(self):
|
||
p.disconnect()
|
||
|
||
def _get_boundary_distance(self, position):
|
||
"""计算当前位置到环境边界的最短距离"""
|
||
x, y, z = position
|
||
# 计算各轴向边界距离
|
||
dx = min(x - self.x_low, self.x_high - x)
|
||
dy = min(y - self.y_low, self.y_high - y)
|
||
dz = min(z - self.z_low, self.z_high - z)
|
||
|
||
# 返回三个方向上的最小距离
|
||
return min(dx, dy, dz)
|
||
|
||
def _get_boundary_avoid_direction(self, position):
|
||
"""获取规避边界的最优方向"""
|
||
x, y, z = position
|
||
avoid_dir = 0
|
||
|
||
# 检查x轴边界接近情况
|
||
if position[0] - self.x_low < self.boundary_threshold:
|
||
avoid_dir = 1 # 向x轴正方向移动
|
||
elif self.x_high - position[0] < self.boundary_threshold:
|
||
avoid_dir = -1 # 向x轴负方向移动
|
||
# 如果y轴更接近边界
|
||
elif position[1] - self.y_low < self.boundary_threshold:
|
||
avoid_dir = 2 # 向y轴正方向移动
|
||
elif self.y_high - position[1] < self.boundary_threshold:
|
||
avoid_dir = -2 # 向y轴负方向移动
|
||
|
||
return avoid_dir
|
||
|
||
|
||
if __name__ == "__main__":
|
||
|
||
from stable_baselines3 import SAC
|
||
from stable_baselines3.common import results_plotter
|
||
from stable_baselines3.common.monitor import Monitor
|
||
from stable_baselines3.common.results_plotter import load_results, ts2xy, plot_results
|
||
from stable_baselines3.common.noise import NormalActionNoise
|
||
from stable_baselines3.common.callbacks import BaseCallback
|
||
|
||
|
||
class SaveOnBestTrainingRewardCallback(BaseCallback):
|
||
"""
|
||
Callback for saving a model (the check is done every ``check_freq`` steps)
|
||
based on the training reward (in practice, we recommend using ``EvalCallback``).
|
||
|
||
:param check_freq:
|
||
:param log_dir: Path to the folder where the model will be saved.
|
||
It must contains the file created by the ``Monitor`` wrapper.
|
||
:param verbose: Verbosity level.
|
||
"""
|
||
|
||
def __init__(self, check_freq: int, log_dir: str, verbose: int = 1):
|
||
super(SaveOnBestTrainingRewardCallback, self).__init__(verbose)
|
||
self.check_freq = check_freq
|
||
self.log_dir = log_dir
|
||
self.save_path = os.path.join(log_dir, 'best_model')
|
||
self.best_mean_reward = -np.inf
|
||
|
||
def _init_callback(self) -> None:
|
||
# Create folder if needed
|
||
if self.save_path is not None:
|
||
os.makedirs(self.save_path, exist_ok=True)
|
||
|
||
def _on_step(self) -> bool:
|
||
if self.n_calls % self.check_freq == 0:
|
||
|
||
# Retrieve training reward
|
||
x, y = ts2xy(load_results(self.log_dir), 'timesteps')
|
||
if len(x) > 0:
|
||
# Mean training reward over the last 100 episodes
|
||
mean_reward = np.mean(y[-100:])
|
||
if self.verbose > 0:
|
||
print(f"Num timesteps: {self.num_timesteps}")
|
||
print(
|
||
f"Best mean reward: {self.best_mean_reward:.2f} - Last mean reward per episode: {mean_reward:.2f}")
|
||
|
||
# New best model, you could save the agent here
|
||
if mean_reward > self.best_mean_reward:
|
||
self.best_mean_reward = mean_reward
|
||
# Example for saving best model
|
||
if self.verbose > 0:
|
||
print(f"Saving new best model to {self.save_path}")
|
||
self.model.save(self.save_path)
|
||
|
||
return True
|
||
|
||
|
||
tempt = 1
|
||
log_dir = './tensorboard/DOA_SAC_callback/'
|
||
os.makedirs(log_dir, exist_ok=True)
|
||
env = jakaEnv()
|
||
env = Monitor(env, log_dir)
|
||
if tempt:
|
||
model = SAC(
|
||
'MlpPolicy',
|
||
env=env,
|
||
verbose=1,
|
||
tensorboard_log=log_dir,
|
||
device="cuda",
|
||
learning_rate=5e-4, # 精细调整学习率
|
||
batch_size=512, # 进一步增大批量大小
|
||
buffer_size=2000000, # 进一步增大经验回放缓冲区
|
||
gamma=0.995, # 进一步提高折扣因子
|
||
tau=0.001, # 进一步减小软更新参数
|
||
ent_coef='auto_0.1', # 调整自动熵系数目标
|
||
target_update_interval=2, # 提高目标网络更新频率
|
||
gradient_steps=2, # 增加每个步骤的梯度更新次数
|
||
use_sde=True, # 启用SDE增强探索能力
|
||
sde_sample_freq=32, # 更频繁地采样SDE噪声
|
||
policy_kwargs=dict(net_arch=dict(pi=[256, 256], qf=[256, 256])) # 增加网络深度
|
||
)
|
||
callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)
|
||
model.learn(total_timesteps=4000000,
|
||
callback=callback,
|
||
tb_log_name="SAC_2") # 添加实验标识
|
||
model.save('model/DOA_SAC_ENV_callback')
|
||
del model
|
||
|
||
else:
|
||
obs = env.reset()
|
||
# 改变路径为你保存模型的路径
|
||
model = SAC.load(r'best_model.zip', env=env)
|
||
|
||
for j in range(50):
|
||
for i in range(2000):
|
||
action, _state = model.predict(obs)
|
||
order1 = 4
|
||
cutoff_freq1 = 1.2
|
||
b1, a1 = signal.butter(order1, cutoff_freq1, fs=2.5, btype='low')
|
||
# 应用滤波器
|
||
action[0] = signal.lfilter(b1, a1, [action[0]])
|
||
action[1] = signal.lfilter(b1, a1, [action[1]])
|
||
action[2] = signal.lfilter(b1, a1, [action[2]])
|
||
obs, reward, done, info = env.step(action)
|
||
# env.render()
|
||
if done:
|
||
print('done')
|
||
# obs = env.reset()
|
||
time.sleep(30)
|
||
break
|
||
break
|
||
|
||
# 三维
|
||
# fig1 = plt.figure("机械臂运行轨迹")
|
||
# ax = fig1.add_subplot(projection="3d") # 三维图形
|
||
# ax.plot(x, y, z, label="simulation")
|
||
# # plt.xlabel("X")
|
||
# # plt.ylabel("Y")
|
||
# ax.set_xlabel("X坐标（米）")
|
||
# ax.set_ylabel("Y坐标（米）")
|
||
# ax.set_zlabel("Z坐标（米）")
|
||
# ax.legend()
|
||
# # plt.savefig('jaka_3d.png')
|
||
# plt.show()
|
||
#
|
||
# # xy方向
|
||
# fig2 = plt.figure()
|
||
# plt.plot(x, y, label="xy-simulation", color="g")
|
||
# plt.xlabel("X")
|
||
# plt.ylabel("Y")
|
||
# plt.savefig("jaka_xy.png")
|
||
# plt.show()
|
||
#
|
||
# # xy方向
|
||
# fig3 = plt.figure()
|
||
# plt.plot(y, z, label="yz-simulation", color="g")
|
||
# plt.xlabel("y")
|
||
# plt.ylabel("z")
|
||
# plt.savefig("jaka_yz.png")
|
||
# plt.show()
|
||
|
||
|
||
|