【星云闪耀1024<AI能量星球>】 AI玩转超级玛丽-【我们都是华为云专家】

AI玩转超级玛丽的整体流程：创建马里奥环境->构建PPO算法->训练->推理->可视化效果

环境配置：运行环境为 Pytorch-1.0.0，且需使用 GPU 运行

PPO算法的基本结构

PPO算法有两种主要形式：PPO-Penalty和PPO-Clip(PPO2)。在这里，我们讨论PPO-Clip（OpenAI使用的主要形式）。 PPO的主要特点如下：

• PPO属于on-policy算法
• PPO同时适用于离散和连续的动作空间
• 损失函数 PPO-Clip算法最精髓的地方就是加入了一项比例用以描绘新老策略的差异,通过超参数ϵ限制策略的更新步长：
  
• 更新策略：
  
• 探索策略 PPO采用随机探索策略。
• 优势函数 表示在状态s下采取动作a，相较于其他动作有多少优势，如果>0,则当前动作比平均动作好，反之，则差
  
• 算法主要流程大致如下： 

实验步骤：

1.安装基础依赖

!pip install gym
!pip install nes-py
!pip install gym-super-mario-bros

第2步：导入相关的库

import os
import shutil
import subprocess as sp
from collections import deque

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as _mp
from torch.distributions import Categorical
import torch.multiprocessing as mp
from nes_py.wrappers import JoypadSpace
import gym_super_mario_bros
from gym.spaces import Box
from gym import Wrapper
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT, COMPLEX_MOVEMENT, RIGHT_ONLY
import cv2
import matplotlib.pyplot as plt
from IPython import display

import moxing as mox

第3步：训练参数初始化

opt={
    "world": 1,                # 可选大关：1,2,3,4,5,6,7,8
    "stage": 1,                # 可选小关：1,2,3,4 
    "action_type": "simple",   # 动作类别："simple"，"right_only", "complex"
    'lr': 1e-4,                # 建议学习率：1e-3，1e-4, 1e-5，7e-5
    'gamma': 0.9,              # 奖励折扣
    'tau': 1.0,                # GAE参数
    'beta': 0.01,              # 熵系数
    'epsilon': 0.2,            # PPO的Clip系数
    'batch_size': 16,          # 经验回放的batch_size
    'max_episode':10,          # 最大训练局数
    'num_epochs': 10,          # 每条经验回放次数
    "num_local_steps": 512,    # 每局的最大步数
    "num_processes": 8,        # 训练进程数，一般等于训练机核心数
    "save_interval": 5,        # 每{}局保存一次模型
    "log_path": "./log",       # 日志保存路径
    "saved_path": "./model",   # 训练模型保存路径
    "pretrain_model": True,    # 是否加载预训练模型，目前只提供1-1关卡的预训练模型，其他需要从零开始训练
    "episode":5
}

第4步：创建环境

# 对原始环境进行修改，以获得更好的训练效果
class CustomReward(Wrapper):
    def __init__(self, env=None, world=None, stage=None, monitor=None):
        super(CustomReward, self).__init__(env)
        self.observation_space = Box(low=0, high=255, shape=(1, 84, 84))
        self.curr_score = 0
        self.current_x = 40
        self.world = world
        self.stage = stage
        if monitor:
            self.monitor = monitor
        else:
            self.monitor = None

    def step(self, action):
        state, reward, done, info = self.env.step(action)
        if self.monitor:
            self.monitor.record(state)
        state = process_frame(state)
        reward += (info["score"] - self.curr_score) / 40.
        self.curr_score = info["score"]
        if done:
            if info["flag_get"]:
                reward += 50
            else:
                reward -= 50
        if self.world == 7 and self.stage == 4:
            if (506 <= info["x_pos"] <= 832 and info["y_pos"] > 127) or (
                    832 < info["x_pos"] <= 1064 and info["y_pos"] < 80) or (
                    1113 < info["x_pos"] <= 1464 and info["y_pos"] < 191) or (
                    1579 < info["x_pos"] <= 1943 and info["y_pos"] < 191) or (
                    1946 < info["x_pos"] <= 1964 and info["y_pos"] >= 191) or (
                    1984 < info["x_pos"] <= 2060 and (info["y_pos"] >= 191 or info["y_pos"] < 127)) or (
                    2114 < info["x_pos"] < 2440 and info["y_pos"] < 191) or info["x_pos"] < self.current_x - 500:
                reward -= 50
                done = True
        if self.world == 4 and self.stage == 4:
            if (info["x_pos"] <= 1500 and info["y_pos"] < 127) or (
                    1588 <= info["x_pos"] < 2380 and info["y_pos"] >= 127):
                reward = -50
                done = True

        self.current_x = info["x_pos"]
        return state, reward / 10., done, info

    def reset(self):
        self.curr_score = 0
        self.current_x = 40
        return process_frame(self.env.reset())


class MultipleEnvironments:
    def __init__(self, world, stage, action_type, num_envs, output_path=None):
        self.agent_conns, self.env_conns = zip(*[mp.Pipe() for _ in range(num_envs)])
        if action_type == "right_only":
            actions = RIGHT_ONLY
        elif action_type == "simple":
            actions = SIMPLE_MOVEMENT
        else:
            actions = COMPLEX_MOVEMENT
        self.envs = [create_train_env(world, stage, actions, output_path=output_path) for _ in range(num_envs)]
        self.num_states = self.envs[0].observation_space.shape[0]
        self.num_actions = len(actions)
        for index in range(num_envs):
            process = mp.Process(target=self.run, args=(index,))
            process.start()
            self.env_conns[index].close()

    def run(self, index):
        self.agent_conns[index].close()
        while True:
            request, action = self.env_conns[index].recv()
            if request == "step":
                self.env_conns[index].send(self.envs[index].step(action.item()))
            elif request == "reset":
                self.env_conns[index].send(self.envs[index].reset())
            else:
                raise NotImplementedError


def process_frame(frame):
    if frame is not None:
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        frame = cv2.resize(frame, (84, 84))[None, :, :] / 255.
        return frame
    else:
        return np.zeros((1, 84, 84))


class CustomSkipFrame(Wrapper):
    def __init__(self, env, skip=4):
        super(CustomSkipFrame, self).__init__(env)
        self.observation_space = Box(low=0, high=255, shape=(skip, 84, 84))
        self.skip = skip
        self.states = np.zeros((skip, 84, 84), dtype=np.float32)

    def step(self, action):
        total_reward = 0
        last_states = []
        for i in range(self.skip):
            state, reward, done, info = self.env.step(action)
            total_reward += reward
            if i >= self.skip / 2:
                last_states.append(state)
            if done:
                self.reset()
                return self.states[None, :, :, :].astype(np.float32), total_reward, done, info
        max_state = np.max(np.concatenate(last_states, 0), 0)
        self.states[:-1] = self.states[1:]
        self.states[-1] = max_state
        return self.states[None, :, :, :].astype(np.float32), total_reward, done, info

    def reset(self):
        state = self.env.reset()
        self.states = np.concatenate([state for _ in range(self.skip)], 0)
        return self.states[None, :, :, :].astype(np.float32)

第5步：定义神经网络

class Net(nn.Module):
    def __init__(self, num_inputs, num_actions):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1)
        self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
        self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1)
        self.linear = nn.Linear(32 * 6 * 6, 512)
        self.critic_linear = nn.Linear(512, 1)
        self.actor_linear = nn.Linear(512, num_actions)
        self._initialize_weights()

    def _initialize_weights(self):
        for module in self.modules():
            if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
                nn.init.orthogonal_(module.weight, nn.init.calculate_gain('relu'))
                nn.init.constant_(module.bias, 0)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(self.conv2(x))
        x = F.relu(self.conv3(x))
        x = F.relu(self.conv4(x))
        x = self.linear(x.view(x.size(0), -1))
        return self.actor_linear(x), self.critic_linear(x)

第6步： 定义PPO算法

def evaluation(opt, global_model, num_states, num_actions, curr_episode):
    print('start evalution !')
    torch.manual_seed(123)
    if opt['action_type'] == "right":
        actions = RIGHT_ONLY
    elif opt['action_type'] == "simple":
        actions = SIMPLE_MOVEMENT
    else:
        actions = COMPLEX_MOVEMENT
    env = create_train_env(opt['world'], opt['stage'], actions)
    local_model = Net(num_states, num_actions)
    if torch.cuda.is_available():
        local_model.cuda()
    local_model.eval()
    state = torch.from_numpy(env.reset())
    if torch.cuda.is_available():
        state = state.cuda()

    plt.figure(figsize=(10, 10))
    img = plt.imshow(env.render(mode='rgb_array'))

    done = False
    local_model.load_state_dict(global_model.state_dict())  # 加载网络参数\

    while not done:
        if torch.cuda.is_available():
            state = state.cuda()
        logits, value = local_model(state)
        policy = F.softmax(logits, dim=1)
        action = torch.argmax(policy).item()
        state, reward, done, info = env.step(action)
        state = torch.from_numpy(state)

        img.set_data(env.render(mode='rgb_array'))  # just update the data
        display.display(plt.gcf())
        display.clear_output(wait=True)

        if info["flag_get"]:
            print("flag getted in episode:{}!".format(curr_episode))
            torch.save(local_model.state_dict(),
                       "{}/ppo_super_mario_bros_{}_{}_{}".format(opt['saved_path'], opt['world'], opt['stage'],
                                                                 curr_episode))
            opt.update({'episode': curr_episode})
            env.close()
            return True
    return False


def train(opt):
    # 判断cuda是否可用
    if torch.cuda.is_available():
        torch.cuda.manual_seed(123)
    else:
        torch.manual_seed(123)
    if os.path.isdir(opt['log_path']):
        shutil.rmtree(opt['log_path'])

    os.makedirs(opt['log_path'])
    if not os.path.isdir(opt['saved_path']):
        os.makedirs(opt['saved_path'])
    mp = _mp.get_context("spawn")
    # 创建环境
    envs = MultipleEnvironments(opt['world'], opt['stage'], opt['action_type'], opt['num_processes'])
    # 创建模型
    model = Net(envs.num_states, envs.num_actions)
    if opt['pretrain_model']:
        print('加载预训练模型')
        if not os.path.exists("ppo_super_mario_bros_1_1_0"):
            mox.file.copy_parallel(
                "obs://modelarts-labs-bj4/course/modelarts/zjc_team/reinforcement_learning/ppo_mario/ppo_super_mario_bros_1_1_0",
                "ppo_super_mario_bros_1_1_0")
        if torch.cuda.is_available():
            model.load_state_dict(torch.load("ppo_super_mario_bros_1_1_0"))
            model.cuda()
        else:
            model.load_state_dict(torch.load("ppo_super_mario_bros_1_1_0", torch.device('cpu')))
    else:
        model.cuda()
    model.share_memory()
    optimizer = torch.optim.Adam(model.parameters(), lr=opt['lr'])
    # 环境重置
    [agent_conn.send(("reset", None)) for agent_conn in envs.agent_conns]
    # 接收当前状态
    curr_states = [agent_conn.recv() for agent_conn in envs.agent_conns]
    curr_states = torch.from_numpy(np.concatenate(curr_states, 0))
    if torch.cuda.is_available():
        curr_states = curr_states.cuda()
    curr_episode = 0
    # 在最大局数内训练
    while curr_episode < opt['max_episode']:
        if curr_episode % opt['save_interval'] == 0 and curr_episode > 0:
            torch.save(model.state_dict(),
                       "{}/ppo_super_mario_bros_{}_{}_{}".format(opt['saved_path'], opt['world'], opt['stage'],
                                                                 curr_episode))
        curr_episode += 1
        old_log_policies = []
        actions = []
        values = []
        states = []
        rewards = []
        dones = []
        # 一局内最大步数
        for _ in range(opt['num_local_steps']):
            states.append(curr_states)
            logits, value = model(curr_states)
            values.append(value.squeeze())
            policy = F.softmax(logits, dim=1)
            old_m = Categorical(policy)
            action = old_m.sample()
            actions.append(action)
            old_log_policy = old_m.log_prob(action)
            old_log_policies.append(old_log_policy)
            # 执行action
            if torch.cuda.is_available():
                [agent_conn.send(("step", act)) for agent_conn, act in zip(envs.agent_conns, action.cpu())]
            else:
                [agent_conn.send(("step", act)) for agent_conn, act in zip(envs.agent_conns, action)]
            state, reward, done, info = zip(*[agent_conn.recv() for agent_conn in envs.agent_conns])
            state = torch.from_numpy(np.concatenate(state, 0))
            if torch.cuda.is_available():
                state = state.cuda()
                reward = torch.cuda.FloatTensor(reward)
                done = torch.cuda.FloatTensor(done)
            else:
                reward = torch.FloatTensor(reward)
                done = torch.FloatTensor(done)
            rewards.append(reward)
            dones.append(done)
            curr_states = state

        _, next_value, = model(curr_states)
        next_value = next_value.squeeze()
        old_log_policies = torch.cat(old_log_policies).detach()
        actions = torch.cat(actions)
        values = torch.cat(values).detach()
        states = torch.cat(states)
        gae = 0
        R = []
        # gae计算
        for value, reward, done in list(zip(values, rewards, dones))[::-1]:
            gae = gae * opt['gamma'] * opt['tau']
            gae = gae + reward + opt['gamma'] * next_value.detach() * (1 - done) - value.detach()
            next_value = value
            R.append(gae + value)
        R = R[::-1]
        R = torch.cat(R).detach()
        advantages = R - values
        # 策略更新
        for i in range(opt['num_epochs']):
            indice = torch.randperm(opt['num_local_steps'] * opt['num_processes'])
            for j in range(opt['batch_size']):
                batch_indices = indice[
                                int(j * (opt['num_local_steps'] * opt['num_processes'] / opt['batch_size'])): int(
                                    (j + 1) * (
                                            opt['num_local_steps'] * opt['num_processes'] / opt['batch_size']))]
                logits, value = model(states[batch_indices])
                new_policy = F.softmax(logits, dim=1)
                new_m = Categorical(new_policy)
                new_log_policy = new_m.log_prob(actions[batch_indices])
                ratio = torch.exp(new_log_policy - old_log_policies[batch_indices])
                actor_loss = -torch.mean(torch.min(ratio * advantages[batch_indices],
                                                   torch.clamp(ratio, 1.0 - opt['epsilon'], 1.0 + opt['epsilon']) *
                                                   advantages[
                                                       batch_indices]))
                critic_loss = F.smooth_l1_loss(R[batch_indices], value.squeeze())
                entropy_loss = torch.mean(new_m.entropy())
                # 损失函数包含三个部分：actor损失，critic损失，和动作entropy损失
                total_loss = actor_loss + critic_loss - opt['beta'] * entropy_loss
                optimizer.zero_grad()
                total_loss.backward()
                torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
                optimizer.step()
        print("Episode: {}. Total loss: {}".format(curr_episode, total_loss))

        finish = False
        for i in range(opt["num_processes"]):
            if info[i]["flag_get"]:
                finish = evaluation(opt, model, envs.num_states, envs.num_actions, curr_episode)
                if finish:
                    break
        if finish:
            break

第7步：训练模型

训练10 Episode，耗时约5分钟

train(opt)

加载预训练模型

Episode: 1. Total loss: 1.2083117961883545

Episode: 2. Total loss: 1.426728367805481

Episode: 3. Total loss: 1.1081796884536743

Episode: 4. Total loss: 0.8977137804031372

Episode: 5. Total loss: 1.4884564876556396

Episode: 6. Total loss: 0.9826470613479614

Episode: 7. Total loss: 1.8124927282333374

Episode: 8. Total loss: 1.2769889831542969

Episode: 9. Total loss: 1.3030457496643066

第8步：使用模型推理游戏

def infer(opt):
    if torch.cuda.is_available():
        torch.cuda.manual_seed(123)
    else:
        torch.manual_seed(123)
    if opt['action_type'] == "right":
        actions = RIGHT_ONLY
    elif opt['action_type'] == "simple":
        actions = SIMPLE_MOVEMENT
    else:
        actions = COMPLEX_MOVEMENT
    env = create_train_env(opt['world'], opt['stage'], actions)
    model = Net(env.observation_space.shape[0], len(actions))
    if torch.cuda.is_available():
        model.load_state_dict(torch.load(
            "{}/ppo_super_mario_bros_{}_{}_{}".format(opt['saved_path'], opt['world'], opt['stage'], opt['episode'])))
        model.cuda()
    else:
        model.load_state_dict(torch.load(
            "{}/ppo_super_mario_bros_{}_{}_{}".format(opt['saved_path'], opt['world'], opt['stage'], opt['episode']),
            map_location=torch.device('cpu')))
    model.eval()
    state = torch.from_numpy(env.reset())

    plt.figure(figsize=(10, 10))
    img = plt.imshow(env.render(mode='rgb_array'))

    while True:
        if torch.cuda.is_available():
            state = state.cuda()
        logits, value = model(state)
        policy = F.softmax(logits, dim=1)
        action = torch.argmax(policy).item()
        state, reward, done, info = env.step(action)
        state = torch.from_numpy(state)

        img.set_data(env.render(mode='rgb_array'))  # just update the data
        display.display(plt.gcf())
        display.clear_output(wait=True)

        if info["flag_get"]:
            print("World {} stage {} completed".format(opt['world'], opt['stage']))
            break

        if done and info["flag_get"] is False:
            print('Game Failed')
            break

infer(opt)

选择其他关卡时，需要调整第三步参数world和stage。我选择了1-2关卡

opt={
    "world": 1,                # 可选大关：1,2,3,4,5,6,7,8
    "stage": 1-2,                # 可选小关：1,2,3,4 
    "action_type": "simple",   # 动作类别："simple"，"right_only", "complex"
    'lr': 1e-4,                # 建议学习率：1e-3，1e-4, 1e-5，7e-5
    'gamma': 0.9,              # 奖励折扣
    'tau': 1.0,                # GAE参数
    'beta': 0.01,              # 熵系数
    'epsilon': 0.2,            # PPO的Clip系数
    'batch_size': 16,          # 经验回放的batch_size
    'max_episode':10,          # 最大训练局数
    'num_epochs': 10,          # 每条经验回放次数
    "num_local_steps": 512,    # 每局的最大步数
    "num_processes": 8,        # 训练进程数，一般等于训练机核心数
    "save_interval": 5,        # 每{}局保存一次模型
    "log_path": "./log",       # 日志保存路径
    "saved_path": "./model",   # 训练模型保存路径
    "pretrain_model": False,    # 是否加载预训练模型，目前只提供1-1关卡的预训练模型，其他需要从零开始训练
    "episode":5
}

重新训练后，得到的推理游戏为：

想要通关关卡，还需调整里面的一些算法，鉴于本人水平有限，暂未通关0.0

GAME OVER。