plotting.py 的版本不同，可以使用下列版本
reinforcement-learning/lib/plotting.py at master · dennybritz/reinforcement-learning · GitHubImplementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course. - reinforcement-learning/lib/plotting.py at master · dennybritz/reinforcement-learning
https://github.com/dennybritz/reinforcement-learning/blob/master/lib/plotting.py
import matplotlib
import numpy as np
import pandas as pd
from collections import namedtuple
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3DEpisodeStats = namedtuple("Stats",["episode_lengths", "episode_rewards"])def plot_cost_to_go_mountain_car(env, estimator, num_tiles=20):x = np.linspace(env.observation_space.low[0], env.observation_space.high[0], num=num_tiles)y = np.linspace(env.observation_space.low[1], env.observation_space.high[1], num=num_tiles)X, Y = np.meshgrid(x, y)Z = np.apply_along_axis(lambda _: -np.max(estimator.predict(_)), 2, np.dstack([X, Y]))fig = plt.figure(figsize=(10, 5))ax = fig.add_subplot(111, projection='3d')surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0)ax.set_xlabel('Position')ax.set_ylabel('Velocity')ax.set_zlabel('Value')ax.set_title("Mountain \"Cost To Go\" Function")fig.colorbar(surf)plt.show()def plot_value_function(V, title="Value Function"):"""Plots the value function as a surface plot."""min_x = min(k[0] for k in V.keys())max_x = max(k[0] for k in V.keys())min_y = min(k[1] for k in V.keys())max_y = max(k[1] for k in V.keys())x_range = np.arange(min_x, max_x + 1)y_range = np.arange(min_y, max_y + 1)X, Y = np.meshgrid(x_range, y_range)# Find value for all (x, y) coordinatesZ_noace = np.apply_along_axis(lambda _: V[(_[0], _[1], False)], 2, np.dstack([X, Y]))Z_ace = np.apply_along_axis(lambda _: V[(_[0], _[1], True)], 2, np.dstack([X, Y]))def plot_surface(X, Y, Z, title):fig = plt.figure(figsize=(20, 10))ax = fig.add_subplot(111, projection='3d')surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1,cmap=matplotlib.cm.coolwarm, vmin=-1.0, vmax=1.0)ax.set_xlabel('Player Sum')ax.set_ylabel('Dealer Showing')ax.set_zlabel('Value')ax.set_title(title)ax.view_init(ax.elev, -120)fig.colorbar(surf)plt.show()plot_surface(X, Y, Z_noace, "{} (No Usable Ace)".format(title))plot_surface(X, Y, Z_ace, "{} (Usable Ace)".format(title))def plot_episode_stats(stats, smoothing_window=10, noshow=False):# Plot the episode length over timefig1 = plt.figure(figsize=(10,5))plt.plot(stats.episode_lengths)plt.xlabel("Episode")plt.ylabel("Episode Length")plt.title("Episode Length over Time")if noshow:plt.close(fig1)else:plt.show(fig1)# Plot the episode reward over timefig2 = plt.figure(figsize=(10,5))rewards_smoothed = pd.Series(stats.episode_rewards).rolling(smoothing_window, min_periods=smoothing_window).mean()plt.plot(rewards_smoothed)plt.xlabel("Episode")plt.ylabel("Episode Reward (Smoothed)")plt.title("Episode Reward over Time (Smoothed over window size {})".format(smoothing_window))if noshow:plt.close(fig2)else:plt.show(fig2)# Plot time steps and episode numberfig3 = plt.figure(figsize=(10,5))plt.plot(np.cumsum(stats.episode_lengths), np.arange(len(stats.episode_lengths)))plt.xlabel("Time Steps")plt.ylabel("Episode")plt.title("Episode per time step")if noshow:plt.close(fig3)else:plt.show(fig3)return fig1, fig2, fig3详情：python - module 'plotting' has no attribute 'EpisodeStats' - Stack Overflowhttps://stackoverflow.com/questions/62287080/module-plotting-has-no-attribute-episodestats
