训练一个玩马里奥的强化学习代理

作者： 冯元松、Suraj Subramanian、王浩华、郭宇张。

本教程将引导你了解深度强化学习的基础知识。最后，你将实现一个由 AI 驱动的马里奥（使用 双深度 Q 网络），它可以自己玩游戏。

虽然本教程不需要任何关于 RL 的预备知识，但你可以熟悉这些 RL 概念，并以这份方便的 备忘单 作为你的伴侣。完整的代码可在 此处 获取。

%%bash
pip install gym-super-mario-bros==7.4.0
pip install tensordict==0.3.0
pip install torchrl==0.3.0

import torch
from torch import nn
from torchvision import transforms as T
from PIL import Image
import numpy as np
from pathlib import Path
from collections import deque
import random, datetime, os

# Gym is an OpenAI toolkit for RL
import gym
from gym.spaces import Box
from gym.wrappers import FrameStack

# NES Emulator for OpenAI Gym
from nes_py.wrappers import JoypadSpace

# Super Mario environment for OpenAI Gym
import gym_super_mario_bros

from tensordict import TensorDict
from torchrl.data import TensorDictReplayBuffer, LazyMemmapStorage

RL 定义

环境 代理与其交互并从中学习的世界。

动作 \(a\)：代理对环境的响应方式。所有可能动作的集合称为动作空间。

状态 \(s\)：环境的当前特征。环境可能处于的所有可能状态的集合称为状态空间。

奖励 \(r\)：奖励是环境对代理的关键反馈。它推动代理学习并改变其未来的行为。多个时间步长的奖励的聚合称为回报。

最优动作价值函数 \(Q^*(s,a)\)：表示如果你从状态 \(s\) 出发，采取任意动作 \(a\)，然后在每个未来的时间步长都采取最大化回报的动作，所获得的预期回报。 \(Q\) 可以理解为某个状态下动作的“质量”。我们试图近似这个函数。

环境

初始化环境

在马里奥游戏中，环境由管道、蘑菇和其他组件组成。

当马里奥采取一个动作时，环境会以改变后的（下一个）状态、奖励和其他信息作为响应。

# Initialize Super Mario environment (in v0.26 change render mode to 'human' to see results on the screen)
if gym.__version__ < '0.26':
    env = gym_super_mario_bros.make("SuperMarioBros-1-1-v0", new_step_api=True)
else:
    env = gym_super_mario_bros.make("SuperMarioBros-1-1-v0", render_mode='rgb', apply_api_compatibility=True)

# Limit the action-space to
#   0. walk right
#   1. jump right
env = JoypadSpace(env, [["right"], ["right", "A"]])

env.reset()
next_state, reward, done, trunc, info = env.step(action=0)
print(f"{next_state.shape},\n {reward},\n {done},\n {info}")

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/gym/envs/registration.py:555: UserWarning:

WARN: The environment SuperMarioBros-1-1-v0 is out of date. You should consider upgrading to version `v3`.

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/gym/envs/registration.py:627: UserWarning:

WARN: The environment creator metadata doesn't include `render_modes`, contains: ['render.modes', 'video.frames_per_second']

/opt/conda/envs/py_3.10/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning:

`np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)

(240, 256, 3),
 0.0,
 False,
 {'coins': 0, 'flag_get': False, 'life': 2, 'score': 0, 'stage': 1, 'status': 'small', 'time': 400, 'world': 1, 'x_pos': 40, 'y_pos': 79}

预处理环境

环境数据以 next_state 的形式返回给智能体。如上所述，每个状态都由一个大小为 [3, 240, 256] 的数组表示。通常，这比我们的智能体需要的更多信息；例如，马里奥的动作并不依赖于管道的颜色或天空的颜色！

我们使用包装器在将环境数据发送到智能体之前对其进行预处理。

GrayScaleObservation 是一个常见的包装器，用于将 RGB 图像转换为灰度图像；这样做可以减少状态表示的大小，而不会丢失有用的信息。现在每个状态的大小为：[1, 240, 256]

ResizeObservation 将每个观察结果下采样为一个正方形图像。新大小：[1, 84, 84]

SkipFrame 是一个自定义包装器，它继承自 gym.Wrapper 并实现了 step() 函数。由于连续帧的变化不大，因此我们可以跳过 n 个中间帧而不会丢失太多信息。第 n 帧会聚合在每个跳过的帧中累积的奖励。

FrameStack 是一个包装器，它允许我们将环境的连续帧压缩到一个观察点，以馈送到我们的学习模型。这样，我们可以根据马里奥在之前几帧中的运动方向来识别他是否正在着陆或跳跃。

class SkipFrame(gym.Wrapper):
    def __init__(self, env, skip):
        """Return only every `skip`-th frame"""
        super().__init__(env)
        self._skip = skip

    def step(self, action):
        """Repeat action, and sum reward"""
        total_reward = 0.0
        for i in range(self._skip):
            # Accumulate reward and repeat the same action
            obs, reward, done, trunk, info = self.env.step(action)
            total_reward += reward
            if done:
                break
        return obs, total_reward, done, trunk, info


class GrayScaleObservation(gym.ObservationWrapper):
    def __init__(self, env):
        super().__init__(env)
        obs_shape = self.observation_space.shape[:2]
        self.observation_space = Box(low=0, high=255, shape=obs_shape, dtype=np.uint8)

    def permute_orientation(self, observation):
        # permute [H, W, C] array to [C, H, W] tensor
        observation = np.transpose(observation, (2, 0, 1))
        observation = torch.tensor(observation.copy(), dtype=torch.float)
        return observation

    def observation(self, observation):
        observation = self.permute_orientation(observation)
        transform = T.Grayscale()
        observation = transform(observation)
        return observation


class ResizeObservation(gym.ObservationWrapper):
    def __init__(self, env, shape):
        super().__init__(env)
        if isinstance(shape, int):
            self.shape = (shape, shape)
        else:
            self.shape = tuple(shape)

        obs_shape = self.shape + self.observation_space.shape[2:]
        self.observation_space = Box(low=0, high=255, shape=obs_shape, dtype=np.uint8)

    def observation(self, observation):
        transforms = T.Compose(
            [T.Resize(self.shape, antialias=True), T.Normalize(0, 255)]
        )
        observation = transforms(observation).squeeze(0)
        return observation


# Apply Wrappers to environment
env = SkipFrame(env, skip=4)
env = GrayScaleObservation(env)
env = ResizeObservation(env, shape=84)
if gym.__version__ < '0.26':
    env = FrameStack(env, num_stack=4, new_step_api=True)
else:
    env = FrameStack(env, num_stack=4)

在将上述包装器应用于环境之后，最终的包装状态由 4 个灰度连续帧堆叠在一起组成，如上图左侧所示。每次马里奥采取一个动作时，环境都会以这种结构的状态作为响应。该结构由一个大小为 [4, 84, 84] 的 3 维数组表示。

智能体

我们创建一个名为 Mario 的类来表示我们游戏中的人物。马里奥应该能够

• 行动：根据当前状态（环境的状态）采取最优动作策略。

• 记忆：经历。经历 = （当前状态，当前动作，奖励，下一状态）。马里奥缓存并稍后回忆他的经历以更新他的动作策略。

• 学习：随着时间的推移学习更好的动作策略

class Mario:
    def __init__():
        pass

    def act(self, state):
        """Given a state, choose an epsilon-greedy action"""
        pass

    def cache(self, experience):
        """Add the experience to memory"""
        pass

    def recall(self):
        """Sample experiences from memory"""
        pass

    def learn(self):
        """Update online action value (Q) function with a batch of experiences"""
        pass

在接下来的部分中，我们将填充马里奥的参数并定义他的函数。

行动

对于任何给定的状态，智能体可以选择采取最优动作（利用）或随机动作（探索）。

马里奥以 self.exploration_rate 的概率随机探索；当他选择利用时，他依靠 MarioNet（在 Learn 部分实现）提供最优动作。

class Mario:
    def __init__(self, state_dim, action_dim, save_dir):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.save_dir = save_dir

        self.device = "cuda" if torch.cuda.is_available() else "cpu"

        # Mario's DNN to predict the most optimal action - we implement this in the Learn section
        self.net = MarioNet(self.state_dim, self.action_dim).float()
        self.net = self.net.to(device=self.device)

        self.exploration_rate = 1
        self.exploration_rate_decay = 0.99999975
        self.exploration_rate_min = 0.1
        self.curr_step = 0

        self.save_every = 5e5  # no. of experiences between saving Mario Net

    def act(self, state):
        """
    Given a state, choose an epsilon-greedy action and update value of step.

    Inputs:
    state(``LazyFrame``): A single observation of the current state, dimension is (state_dim)
    Outputs:
    ``action_idx`` (``int``): An integer representing which action Mario will perform
    """
        # EXPLORE
        if np.random.rand() < self.exploration_rate:
            action_idx = np.random.randint(self.action_dim)

        # EXPLOIT
        else:
            state = state[0].__array__() if isinstance(state, tuple) else state.__array__()
            state = torch.tensor(state, device=self.device).unsqueeze(0)
            action_values = self.net(state, model="online")
            action_idx = torch.argmax(action_values, axis=1).item()

        # decrease exploration_rate
        self.exploration_rate *= self.exploration_rate_decay
        self.exploration_rate = max(self.exploration_rate_min, self.exploration_rate)

        # increment step
        self.curr_step += 1
        return action_idx

缓存和回忆

这两个函数充当马里奥的“记忆”过程。

cache()：每次马里奥执行一个动作时，他都会将 experience 存储到他的记忆中。他的经历包括当前状态、执行的动作、动作的奖励、下一状态以及游戏是否结束。

recall()：马里奥从他的记忆中随机采样一批经历，并用它来学习游戏。

class Mario(Mario):  # subclassing for continuity
    def __init__(self, state_dim, action_dim, save_dir):
        super().__init__(state_dim, action_dim, save_dir)
        self.memory = TensorDictReplayBuffer(storage=LazyMemmapStorage(100000, device=torch.device("cpu")))
        self.batch_size = 32

    def cache(self, state, next_state, action, reward, done):
        """
        Store the experience to self.memory (replay buffer)

        Inputs:
        state (``LazyFrame``),
        next_state (``LazyFrame``),
        action (``int``),
        reward (``float``),
        done(``bool``))
        """
        def first_if_tuple(x):
            return x[0] if isinstance(x, tuple) else x
        state = first_if_tuple(state).__array__()
        next_state = first_if_tuple(next_state).__array__()

        state = torch.tensor(state)
        next_state = torch.tensor(next_state)
        action = torch.tensor([action])
        reward = torch.tensor([reward])
        done = torch.tensor([done])

        # self.memory.append((state, next_state, action, reward, done,))
        self.memory.add(TensorDict({"state": state, "next_state": next_state, "action": action, "reward": reward, "done": done}, batch_size=[]))

    def recall(self):
        """
        Retrieve a batch of experiences from memory
        """
        batch = self.memory.sample(self.batch_size).to(self.device)
        state, next_state, action, reward, done = (batch.get(key) for key in ("state", "next_state", "action", "reward", "done"))
        return state, next_state, action.squeeze(), reward.squeeze(), done.squeeze()

学习

马里奥在幕后使用DDQN 算法。DDQN 使用两个卷积神经网络 - \(Q_{online}\) 和 \(Q_{target}\) - 来独立地近似最优动作价值函数。

在我们的实现中，我们在 \(Q_{online}\) 和 \(Q_{target}\) 之间共享特征生成器 features，但为每个网络维护单独的全连接分类器。\(\theta_{target}\)（\(Q_{target}\) 的参数）被冻结以防止通过反向传播更新。相反，它会定期与 \(\theta_{online}\) 同步（稍后详细介绍）。

神经网络

class MarioNet(nn.Module):
    """mini CNN structure
  input -> (conv2d + relu) x 3 -> flatten -> (dense + relu) x 2 -> output
  """

    def __init__(self, input_dim, output_dim):
        super().__init__()
        c, h, w = input_dim

        if h != 84:
            raise ValueError(f"Expecting input height: 84, got: {h}")
        if w != 84:
            raise ValueError(f"Expecting input width: 84, got: {w}")

        self.online = self.__build_cnn(c, output_dim)

        self.target = self.__build_cnn(c, output_dim)
        self.target.load_state_dict(self.online.state_dict())

        # Q_target parameters are frozen.
        for p in self.target.parameters():
            p.requires_grad = False

    def forward(self, input, model):
        if model == "online":
            return self.online(input)
        elif model == "target":
            return self.target(input)

    def __build_cnn(self, c, output_dim):
        return nn.Sequential(
            nn.Conv2d(in_channels=c, out_channels=32, kernel_size=8, stride=4),
            nn.ReLU(),
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2),
            nn.ReLU(),
            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(3136, 512),
            nn.ReLU(),
            nn.Linear(512, output_dim),
        )

TD 估计和 TD 目标

学习中涉及两个值

TD 估计 - 给定状态 \(s\) 的预测最优 \(Q^*\)

\[{TD}_e = Q_{online}^*(s,a)\]

TD 目标 - 当前奖励和下一状态 \(s'\) 中估计的 \(Q^*\) 的聚合

\[a' = argmax_{a} Q_{online}(s', a)\]

\[{TD}_t = r + \gamma Q_{target}^*(s',a')\]

因为我们不知道下一个动作 \(a'\) 会是什么，所以我们使用使下一状态 \(s'\) 中的 \(Q_{online}\) 最大化的动作 \(a'\)。

请注意，我们在 td_target() 上使用了 @torch.no_grad() 装饰器来禁用此处的梯度计算（因为我们不需要在 \(\theta_{target}\) 上进行反向传播）。

class Mario(Mario):
    def __init__(self, state_dim, action_dim, save_dir):
        super().__init__(state_dim, action_dim, save_dir)
        self.gamma = 0.9

    def td_estimate(self, state, action):
        current_Q = self.net(state, model="online")[
            np.arange(0, self.batch_size), action
        ]  # Q_online(s,a)
        return current_Q

    @torch.no_grad()
    def td_target(self, reward, next_state, done):
        next_state_Q = self.net(next_state, model="online")
        best_action = torch.argmax(next_state_Q, axis=1)
        next_Q = self.net(next_state, model="target")[
            np.arange(0, self.batch_size), best_action
        ]
        return (reward + (1 - done.float()) * self.gamma * next_Q).float()

更新模型

当马里奥从他的回放缓冲区中采样输入时，我们计算 \(TD_t\) 和 \(TD_e\) 并将此损失反向传播到 \(Q_{online}\) 以更新其参数 \(\theta_{online}\)（\(\alpha\) 是传递给 optimizer 的学习率 lr）

\[\theta_{online} \leftarrow \theta_{online} + \alpha \nabla(TD_e - TD_t)\]

\(\theta_{target}\) 不通过反向传播更新。相反，我们定期将 \(\theta_{online}\) 复制到 \(\theta_{target}\)

\[\theta_{target} \leftarrow \theta_{online}\]

class Mario(Mario):
    def __init__(self, state_dim, action_dim, save_dir):
        super().__init__(state_dim, action_dim, save_dir)
        self.optimizer = torch.optim.Adam(self.net.parameters(), lr=0.00025)
        self.loss_fn = torch.nn.SmoothL1Loss()

    def update_Q_online(self, td_estimate, td_target):
        loss = self.loss_fn(td_estimate, td_target)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        return loss.item()

    def sync_Q_target(self):
        self.net.target.load_state_dict(self.net.online.state_dict())

保存检查点

class Mario(Mario):
    def save(self):
        save_path = (
            self.save_dir / f"mario_net_{int(self.curr_step // self.save_every)}.chkpt"
        )
        torch.save(
            dict(model=self.net.state_dict(), exploration_rate=self.exploration_rate),
            save_path,
        )
        print(f"MarioNet saved to {save_path} at step {self.curr_step}")

综合起来

class Mario(Mario):
    def __init__(self, state_dim, action_dim, save_dir):
        super().__init__(state_dim, action_dim, save_dir)
        self.burnin = 1e4  # min. experiences before training
        self.learn_every = 3  # no. of experiences between updates to Q_online
        self.sync_every = 1e4  # no. of experiences between Q_target & Q_online sync

    def learn(self):
        if self.curr_step % self.sync_every == 0:
            self.sync_Q_target()

        if self.curr_step % self.save_every == 0:
            self.save()

        if self.curr_step < self.burnin:
            return None, None

        if self.curr_step % self.learn_every != 0:
            return None, None

        # Sample from memory
        state, next_state, action, reward, done = self.recall()

        # Get TD Estimate
        td_est = self.td_estimate(state, action)

        # Get TD Target
        td_tgt = self.td_target(reward, next_state, done)

        # Backpropagate loss through Q_online
        loss = self.update_Q_online(td_est, td_tgt)

        return (td_est.mean().item(), loss)

日志记录

import numpy as np
import time, datetime
import matplotlib.pyplot as plt


class MetricLogger:
    def __init__(self, save_dir):
        self.save_log = save_dir / "log"
        with open(self.save_log, "w") as f:
            f.write(
                f"{'Episode':>8}{'Step':>8}{'Epsilon':>10}{'MeanReward':>15}"
                f"{'MeanLength':>15}{'MeanLoss':>15}{'MeanQValue':>15}"
                f"{'TimeDelta':>15}{'Time':>20}\n"
            )
        self.ep_rewards_plot = save_dir / "reward_plot.jpg"
        self.ep_lengths_plot = save_dir / "length_plot.jpg"
        self.ep_avg_losses_plot = save_dir / "loss_plot.jpg"
        self.ep_avg_qs_plot = save_dir / "q_plot.jpg"

        # History metrics
        self.ep_rewards = []
        self.ep_lengths = []
        self.ep_avg_losses = []
        self.ep_avg_qs = []

        # Moving averages, added for every call to record()
        self.moving_avg_ep_rewards = []
        self.moving_avg_ep_lengths = []
        self.moving_avg_ep_avg_losses = []
        self.moving_avg_ep_avg_qs = []

        # Current episode metric
        self.init_episode()

        # Timing
        self.record_time = time.time()

    def log_step(self, reward, loss, q):
        self.curr_ep_reward += reward
        self.curr_ep_length += 1
        if loss:
            self.curr_ep_loss += loss
            self.curr_ep_q += q
            self.curr_ep_loss_length += 1

    def log_episode(self):
        "Mark end of episode"
        self.ep_rewards.append(self.curr_ep_reward)
        self.ep_lengths.append(self.curr_ep_length)
        if self.curr_ep_loss_length == 0:
            ep_avg_loss = 0
            ep_avg_q = 0
        else:
            ep_avg_loss = np.round(self.curr_ep_loss / self.curr_ep_loss_length, 5)
            ep_avg_q = np.round(self.curr_ep_q / self.curr_ep_loss_length, 5)
        self.ep_avg_losses.append(ep_avg_loss)
        self.ep_avg_qs.append(ep_avg_q)

        self.init_episode()

    def init_episode(self):
        self.curr_ep_reward = 0.0
        self.curr_ep_length = 0
        self.curr_ep_loss = 0.0
        self.curr_ep_q = 0.0
        self.curr_ep_loss_length = 0

    def record(self, episode, epsilon, step):
        mean_ep_reward = np.round(np.mean(self.ep_rewards[-100:]), 3)
        mean_ep_length = np.round(np.mean(self.ep_lengths[-100:]), 3)
        mean_ep_loss = np.round(np.mean(self.ep_avg_losses[-100:]), 3)
        mean_ep_q = np.round(np.mean(self.ep_avg_qs[-100:]), 3)
        self.moving_avg_ep_rewards.append(mean_ep_reward)
        self.moving_avg_ep_lengths.append(mean_ep_length)
        self.moving_avg_ep_avg_losses.append(mean_ep_loss)
        self.moving_avg_ep_avg_qs.append(mean_ep_q)

        last_record_time = self.record_time
        self.record_time = time.time()
        time_since_last_record = np.round(self.record_time - last_record_time, 3)

        print(
            f"Episode {episode} - "
            f"Step {step} - "
            f"Epsilon {epsilon} - "
            f"Mean Reward {mean_ep_reward} - "
            f"Mean Length {mean_ep_length} - "
            f"Mean Loss {mean_ep_loss} - "
            f"Mean Q Value {mean_ep_q} - "
            f"Time Delta {time_since_last_record} - "
            f"Time {datetime.datetime.now().strftime('%Y-%m-%dT%H:%M:%S')}"
        )

        with open(self.save_log, "a") as f:
            f.write(
                f"{episode:8d}{step:8d}{epsilon:10.3f}"
                f"{mean_ep_reward:15.3f}{mean_ep_length:15.3f}{mean_ep_loss:15.3f}{mean_ep_q:15.3f}"
                f"{time_since_last_record:15.3f}"
                f"{datetime.datetime.now().strftime('%Y-%m-%dT%H:%M:%S'):>20}\n"
            )

        for metric in ["ep_lengths", "ep_avg_losses", "ep_avg_qs", "ep_rewards"]:
            plt.clf()
            plt.plot(getattr(self, f"moving_avg_{metric}"), label=f"moving_avg_{metric}")
            plt.legend()
            plt.savefig(getattr(self, f"{metric}_plot"))

让我们玩吧！

在这个例子中，我们运行训练循环 40 个回合，但是为了让马里奥真正学会他的世界，我们建议至少运行 40,000 个回合！

use_cuda = torch.cuda.is_available()
print(f"Using CUDA: {use_cuda}")
print()

save_dir = Path("checkpoints") / datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
save_dir.mkdir(parents=True)

mario = Mario(state_dim=(4, 84, 84), action_dim=env.action_space.n, save_dir=save_dir)

logger = MetricLogger(save_dir)

episodes = 40
for e in range(episodes):

    state = env.reset()

    # Play the game!
    while True:

        # Run agent on the state
        action = mario.act(state)

        # Agent performs action
        next_state, reward, done, trunc, info = env.step(action)

        # Remember
        mario.cache(state, next_state, action, reward, done)

        # Learn
        q, loss = mario.learn()

        # Logging
        logger.log_step(reward, loss, q)

        # Update state
        state = next_state

        # Check if end of game
        if done or info["flag_get"]:
            break

    logger.log_episode()

    if (e % 20 == 0) or (e == episodes - 1):
        logger.record(episode=e, epsilon=mario.exploration_rate, step=mario.curr_step)

Using CUDA: True

Episode 0 - Step 163 - Epsilon 0.9999592508251706 - Mean Reward 635.0 - Mean Length 163.0 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 1.98 - Time 2024-10-17T22:11:31
Episode 20 - Step 5007 - Epsilon 0.9987490329557962 - Mean Reward 667.429 - Mean Length 238.429 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 59.039 - Time 2024-10-17T22:12:30
Episode 39 - Step 8854 - Epsilon 0.9977889477081997 - Mean Reward 656.6 - Mean Length 221.35 - Mean Loss 0.0 - Mean Q Value 0.0 - Time Delta 47.517 - Time 2024-10-17T22:13:18

结论

在本教程中，我们了解了如何使用 PyTorch 训练一个游戏 AI。您可以使用相同的方法训练一个 AI 来玩 OpenAI gym 中的任何游戏。希望您喜欢本教程，欢迎访问 我们的 GitHub！