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# -*- coding: utf-8 -*-
Train a Mario-playing RL Agent
===============================
**Authors:** `Yuansong Feng <https://github.com/YuansongFeng>`__, `Suraj Subramanian <https://github.com/suraj813>`__, `Howard Wang <https://github.com/hw26>`__, `Steven Guo <https://github.com/GuoYuzhang>`__.
This tutorial walks you through the fundamentals of Deep Reinforcement
Learning. At the end, you will implement an AI-powered Mario (using
`Double Deep Q-Networks <https://arxiv.org/pdf/1509.06461.pdf>`__) that
can play the game by itself.
Although no prior knowledge of RL is necessary for this tutorial, you
can familiarize yourself with these RL
`concepts <https://spinningup.openai.com/en/latest/spinningup/rl_intro.html>`__,
and have this handy
`cheatsheet <https://colab.research.google.com/drive/1eN33dPVtdPViiS1njTW_-r-IYCDTFU7N>`__
as your companion. The full code is available
`here <https://github.com/yuansongFeng/MadMario/>`__.
.. figure:: /_static/img/mario.gif
   :alt: mario
######################################################################
#  .. code-block:: bash
#      %%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
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 Definitions
# """"""""""""""""""
# **Environment** The world that an agent interacts with and learns from.
# **Action** :math:`a` : How the Agent responds to the Environment. The
# set of all possible Actions is called *action-space*.
# **State** :math:`s` : The current characteristic of the Environment. The
# set of all possible States the Environment can be in is called
# *state-space*.
# **Reward** :math:`r` : Reward is the key feedback from Environment to
# Agent. It is what drives the Agent to learn and to change its future
# action. An aggregation of rewards over multiple time steps is called
# **Return**.
# **Optimal Action-Value function** :math:`Q^*(s,a)` : Gives the expected
# return if you start in state :math:`s`, take an arbitrary action
# :math:`a`, and then for each future time step take the action that
# maximizes returns. :math:`Q` can be said to stand for the “quality” of
# the action in a state. We try to approximate this function.
######################################################################
# Environment
# """"""""""""""""
# Initialize Environment
# ------------------------
# In Mario, the environment consists of tubes, mushrooms and other
# components.
# When Mario makes an action, the environment responds with the changed
# (next) state, reward and other info.
# 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)
    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}")
######################################################################
# Preprocess Environment
# ------------------------
# Environment data is returned to the agent in ``next_state``. As you saw
# above, each state is represented by a ``[3, 240, 256]`` size array.
# Often that is more information than our agent needs; for instance,
# Mario’s actions do not depend on the color of the pipes or the sky!
# We use **Wrappers** to preprocess environment data before sending it to
# the agent.
# ``GrayScaleObservation`` is a common wrapper to transform an RGB image
# to grayscale; doing so reduces the size of the state representation
# without losing useful information. Now the size of each state:
# ``[1, 240, 256]``
# ``ResizeObservation`` downsamples each observation into a square image.
# New size: ``[1, 84, 84]``
# ``SkipFrame`` is a custom wrapper that inherits from ``gym.Wrapper`` and
# implements the ``step()`` function. Because consecutive frames don’t
# vary much, we can skip n-intermediate frames without losing much
# information. The n-th frame aggregates rewards accumulated over each
# skipped frame.
# ``FrameStack`` is a wrapper that allows us to squash consecutive frames
# of the environment into a single observation point to feed to our
# learning model. This way, we can identify if Mario was landing or
# jumping based on the direction of his movement in the previous several
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)
    env = FrameStack(env, num_stack=4)
######################################################################
# After applying the above wrappers to the environment, the final wrapped
# state consists of 4 gray-scaled consecutive frames stacked together, as
# shown above in the image on the left. Each time Mario makes an action,
# the environment responds with a state of this structure. The structure
# is represented by a 3-D array of size ``[4, 84, 84]``.
# .. figure:: /_static/img/mario_env.png
#    :alt: picture
######################################################################
# """""""""
# We create a class ``Mario`` to represent our agent in the game. Mario
# should be able to:
# -  **Act** according to the optimal action policy based on the current
#    state (of the environment).
# -  **Remember** experiences. Experience = (current state, current
#    action, reward, next state). Mario *caches* and later *recalls* his
#    experiences to update his action policy.
# -  **Learn** a better action policy over time
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
######################################################################
# In the following sections, we will populate Mario’s parameters and
# define his functions.
######################################################################
# --------------
# For any given state, an agent can choose to do the most optimal action
# (**exploit**) or a random action (**explore**).
# Mario randomly explores with a chance of ``self.exploration_rate``; when
# he chooses to exploit, he relies on ``MarioNet`` (implemented in
# ``Learn`` section) to provide the most optimal action.
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
######################################################################
# Cache and Recall
# ----------------------
# These two functions serve as Mario’s “memory” process.
# ``cache()``: Each time Mario performs an action, he stores the
# ``experience`` to his memory. His experience includes the current
# *state*, *action* performed, *reward* from the action, the *next state*,
# and whether the game is *done*.
# ``recall()``: Mario randomly samples a batch of experiences from his
# memory, and uses that to learn the game.
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()
######################################################################
# --------------
# Mario uses the `DDQN algorithm <https://arxiv.org/pdf/1509.06461>`__
# under the hood. DDQN uses two ConvNets - :math:`Q_{online}` and
# :math:`Q_{target}` - that independently approximate the optimal
# action-value function.
# In our implementation, we share feature generator ``features`` across
# :math:`Q_{online}` and :math:`Q_{target}`, but maintain separate FC
# classifiers for each. :math:`\theta_{target}` (the parameters of
# :math:`Q_{target}`) is frozen to prevent updating by backprop. Instead,
# it is periodically synced with :math:`\theta_{online}` (more on this
# Neural Network
# ~~~~~~~~~~~~~~~~~~
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 Estimate & TD Target
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
# Two values are involved in learning:
# **TD Estimate** - the predicted optimal :math:`Q^*` for a given state
# :math:`s`
# .. math::
#    {TD}_e = Q_{online}^*(s,a)
# **TD Target** - aggregation of current reward and the estimated
# :math:`Q^*` in the next state :math:`s'`
# .. math::
#    a' = argmax_{a} Q_{online}(s', a)
# .. math::
#    {TD}_t = r + \gamma Q_{target}^*(s',a')
# Because we don’t know what next action :math:`a'` will be, we use the
# action :math:`a'` maximizes :math:`Q_{online}` in the next state
# :math:`s'`.
# Notice we use the
# `@torch.no_grad() <https://pytorch.org/docs/stable/generated/torch.no_grad.html#no-grad>`__
# decorator on ``td_target()`` to disable gradient calculations here
# (because we don’t need to backpropagate on :math:`\theta_{target}`).
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()
######################################################################
# Updating the model
# ~~~~~~~~~~~~~~~~~~~~~~
# As Mario samples inputs from his replay buffer, we compute :math:`TD_t`
# and :math:`TD_e` and backpropagate this loss down :math:`Q_{online}` to
# update its parameters :math:`\theta_{online}` (:math:`\alpha` is the
# learning rate ``lr`` passed to the ``optimizer``)
# .. math::
#    \theta_{online} \leftarrow \theta_{online} + \alpha \nabla(TD_e - TD_t)
# :math:`\theta_{target}` does not update through backpropagation.
# Instead, we periodically copy :math:`\theta_{online}` to
# :math:`\theta_{target}`
# .. math::
#    \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())
######################################################################
# Save checkpoint
# ~~~~~~~~~~~~~~~~~~
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}")
######################################################################
# Putting it all together
# ~~~~~~~~~~~~~~~~~~~~~~~~~~
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)
######################################################################
# --------------
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"))
######################################################################
# Let’s play!
# """""""""""""""
# In this example we run the training loop for 40 episodes, but for Mario to truly learn the ways of
# his world, we suggest running the loop for at least 40,000 episodes!
use_cuda = torch.cuda.is_available()
print(f"Using CUDA: {use_cuda}")
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)
######################################################################
# Conclusion
# """""""""""""""
# In this tutorial, we saw how we can use PyTorch to train a game-playing AI. You can use the same methods
# to train an AI to play any of the games at the `OpenAI gym <https://gym.openai.com/>`__. Hope you enjoyed this tutorial, feel free to reach us at
# `our github <https://github.com/yuansongFeng/MadMario/>`__!
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