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new_reward_plot.py
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# coding=utf-8
# DDPG TD3 SAC 训练曲线
import numpy as np
import sys
import matplotlib.pyplot as plt
import os
import matplotlib
plt.rc('font',family='Times New Roman')
# matplotlib.rcParams['font.sans-serif']=['SimHei'] # 用黑体显示中文
# matplotlib.rcParams['axes.unicode_minus']=False # 正常显示负号
plt.figure(figsize=(5,3))
plt.rcParams['savefig.dpi'] = 600 #图片像素
plt.rcParams['figure.dpi'] = 600 #分辨率
ax = plt.gca()
# ax.set_xlim(0, 1000)
# ax.set_ylim(-60, 80)
ax.tick_params(bottom=False,top=False,left=False,right=False)
# 设置网格颜色
ax.grid(color='grey', linestyle='-', linewidth=1, alpha=0.5, zorder=0)
# ax.grid(color='w', linestyle='-', linewidth=2, alpha=1, zorder=0)
# 设置背景颜色
# ax.patch.set_facecolor("#e9e9f2")
# 去除边框
# ax.spines['top'].set_visible(False)
# ax.spines['bottom'].set_visible(False)
# ax.spines['left'].set_visible(False)
# ax.spines['right'].set_visible(False)
# 加载数据
def load(policy, num=3):
url = os.path.dirname(os.path.realpath(__file__))
result = []
for i in range(num):
temp = np.load("/home/he/下载/训练数据/corridor/"+policy+"/step_reward.npy")
result.append(temp)
return result
def smooth(arr, fineness):
result = arr[:]
for i in range(fineness, arr.size):
temp = 0
for j in range(fineness):
temp += result[i-j]
result[i] = temp/fineness
return np.array(result)
def get_mean_max_min(data_list, smooth_flag, fineness):
n = sys.maxsize
for data in data_list:
n = min(n, data.size)
max_data = np.zeros((n))
min_data = np.zeros((n))
mean_data = np.zeros((n))
for i in range(n):
temp = []
for data in data_list:
temp.append(data[i])
temp = np.array(temp)
max_data[i] = temp.max()
min_data[i] = temp.min()
mean_data[i] = temp.mean()
data = [mean_data, max_data, min_data]
if smooth_flag:
for i in range(len(data)):
for j in range(2, fineness):
data[i] = smooth(data[i], j)
return data[0], data[1], data[2]
# SAC_data = load("SAC", 3)
# TD3_data = load("TD3", 3)
# DDPG_data = load("DDPG", 3)
SAC_data = load("DCDDPG", 3)
TD3_data = load("DCEERDDPG(无human)", 3)
DDPG_data = load("DCEERDDPG(有human)", 3)
fineness = 7
SAC_mean_data, SAC_max_data, SAC_min_data = get_mean_max_min(SAC_data, True, fineness)
TD3_mean_data, TD3_max_data, TD3_min_data = get_mean_max_min(TD3_data, True, fineness)
DDPG_mean_data, DDPG_max_data, DDPG_min_data = get_mean_max_min(DDPG_data, True, fineness)
SAC_x = range(SAC_mean_data.size)
plt.fill_between(SAC_x, SAC_min_data, SAC_max_data, alpha=0.2, zorder=2, color="blue")
plt.plot(SAC_x, SAC_mean_data, linewidth=2, label="SAC", zorder=3, color="blue")
TD3_x = range(TD3_mean_data.size)
plt.fill_between(TD3_x, TD3_min_data, TD3_max_data, alpha=0.2, zorder=2, color="red")
plt.plot(TD3_x, TD3_mean_data, linewidth=2, label="TD3", zorder=3, color="red")
DDPG_x = range(DDPG_mean_data.size)
plt.fill_between(DDPG_x, DDPG_min_data, DDPG_max_data, alpha=0.2, zorder=2, color="green")
plt.plot(DDPG_x, DDPG_mean_data, linewidth=2, label="DDPG", zorder=3, color="green")
# plt.title("Training curve", pad=15)
plt.xlabel("Episode", labelpad=8.5)
plt.ylabel("Accumulated Reward", labelpad=8.5)
plt.tight_layout()
plt.legend(loc="lower right", frameon=True)
plt.savefig('episode_reward_plot.jpg')
plt.show()