mario_bases.py

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
from nes_py.wrappers import JoypadSpace
import gym_super_mario_bros
import gym
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT
from gym.spaces import Box
from gym.wrappers import FrameStack
from tensordict import TensorDict
from torchrl.data import TensorDictReplayBuffer, LazyMemmapStorage
import numpy as np
import time, datetime
import matplotlib.pyplot as plt
import platform



class Mario:
    def __init__(self, state_dim, action_dim, save_dir, checkpoint=None):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.memory =  TensorDictReplayBuffer(
            storage=LazyMemmapStorage(100000, device=torch.device("cpu"))
        )
        self.batch_size = 32

        self.exploration_rate = 1
        self.exploration_rate_decay = 0.99999975
        self.exploration_rate_min = 0.1
        self.gamma = 0.9

        self.curr_step = 0
        self.burnin = 1e5  # 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

        self.save_every = 1e5   # no. of experiences between saving Mario Net
        self.save_dir = save_dir

        self.use_cuda = torch.cuda.is_available()

        # 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()
        if self.use_cuda:
            self.net = self.net.to(device='cuda')
        if checkpoint:
            print("LOADED FROM CHECKPOINT")
            self.load(checkpoint)

        self.optimizer = torch.optim.Adam(self.net.parameters(), lr=0.00025)
        self.loss_fn = torch.nn.SmoothL1Loss()


    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
        # self.exploration_rate = 0.8
        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=("cuda" if self.use_cuda else "cpu")).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

    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 = random.sample(self.memory, self.batch_size)
        state, next_state, action, reward, done = map(torch.stack, zip(*batch))
        return state, next_state, action.squeeze(), reward.squeeze(), done.squeeze()


    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()


    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())


    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)


    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}")


    def load(self, load_path):
        if not load_path.exists():
            raise ValueError(f"{load_path} does not exist")

        ckp = torch.load(load_path, map_location=('cuda' if self.use_cuda else 'cpu'))
        exploration_rate = ckp.get('exploration_rate')
        state_dict = ckp.get('model')

        print(f"Loading model at {load_path} with exploration rate {exploration_rate}")
        self.net.load_state_dict(state_dict)
        self.exploration_rate = exploration_rate


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.Compose([T.ToPILImage(), T.Grayscale(), T.ToTensor()])
        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):
        transform = T.Compose(
            [T.ToPILImage(), T.Resize(self.shape), T.ToTensor(), T.Normalize(0, 255)]
        )
        observation = transform(observation).squeeze(0)
        return observation


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"))




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),
        )