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Tutorial 1: Learn to play games with RL

Week 3, Day 3: Reinforcement Learning for Games

By Neuromatch Academy

Content creators: Mandana Samiei, Raymond Chua, Tim Lilicrap, Blake Richards

Content reviewers: Arush Tagade, Lily Cheng, Melvin Selim Atay, Kelson Shilling-Scrivo

Content editors: Melvin Selim Atay, Spiros Chavlis

Production editors: Namrata Bafna, Spiros Chavlis

Post-Production team: Gagana B, Spiros Chavlis

Our 2021 Sponsors, including Presenting Sponsor Facebook Reality Labs


Tutorial Objectives

In this tutorial, you will learn how to implement a game loop and improve the performance of a random player.

The specific objectives for this tutorial:

  • Understand the format of two-players games

  • Learn about value network and policy network

In the Bonus sections you will learn about Monte Carlo Tree Search (MCTS) and compare its performance to policy-based and value-based players.

Tutorial slides

These are the slides for the videos in the tutorial. If you want to locally download the slides, click here.


Setup

Install dependencies

# @title Install dependencies
!pip install coloredlogs --quiet

!pip install git+https://github.com/NeuromatchAcademy/evaltools --quiet
from evaltools.airtable import AirtableForm

# generate airtable form
atform = AirtableForm('appn7VdPRseSoMXEG','W3D3_T1','https://portal.neuromatchacademy.org/api/redirect/to/2baacd95-3fb5-4399-bf95-bbe5de255d2b')
# Imports
import os
import math
import time
import torch
import random
import logging
import coloredlogs

import numpy as np

import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

from tqdm.notebook import tqdm
from pickle import Unpickler

log = logging.getLogger(__name__)
coloredlogs.install(level='INFO')  # Change this to DEBUG to see more info.

Set random seed

Executing set_seed(seed=seed) you are setting the seed

# @title Set random seed

# @markdown Executing `set_seed(seed=seed)` you are setting the seed

# For DL its critical to set the random seed so that students can have a
# baseline to compare their results to expected results.
# Read more here: https://pytorch.org/docs/stable/notes/randomness.html

# Call `set_seed` function in the exercises to ensure reproducibility.
import random
import torch

def set_seed(seed=None, seed_torch=True):
  """
  Function that controls randomness. NumPy and random modules must be imported.

  Args:
    seed : Integer
      A non-negative integer that defines the random state. Default is `None`.
    seed_torch : Boolean
      If `True` sets the random seed for pytorch tensors, so pytorch module
      must be imported. Default is `True`.

  Returns:
    Nothing.
  """
  if seed is None:
    seed = np.random.choice(2 ** 32)
  random.seed(seed)
  np.random.seed(seed)
  if seed_torch:
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.benchmark = False
    torch.backends.cudnn.deterministic = True

  print(f'Random seed {seed} has been set.')


# In case that `DataLoader` is used
def seed_worker(worker_id):
  """
  DataLoader will reseed workers following randomness in
  multi-process data loading algorithm.

  Args:
    worker_id: integer
      ID of subprocess to seed. 0 means that
      the data will be loaded in the main process
      Refer: https://pytorch.org/docs/stable/data.html#data-loading-randomness for more details

  Returns:
    Nothing
  """
  worker_seed = torch.initial_seed() % 2**32
  np.random.seed(worker_seed)
  random.seed(worker_seed)

Set device (GPU or CPU). Execute set_device()

# @title Set device (GPU or CPU). Execute `set_device()`
# especially if torch modules used.

# Inform the user if the notebook uses GPU or CPU.

def set_device():
  """
  Set the device. CUDA if available, CPU otherwise

  Args:
    None

  Returns:
    Nothing
  """
  device = "cuda" if torch.cuda.is_available() else "cpu"
  if device != "cuda":
    print("WARNING: For this notebook to perform best, "
        "if possible, in the menu under `Runtime` -> "
        "`Change runtime type.`  select `GPU` ")
  else:
    print("GPU is enabled in this notebook.")

  return device
SEED = 2021
set_seed(seed=SEED)
DEVICE = set_device()
Random seed 2021 has been set.
WARNING: For this notebook to perform best, if possible, in the menu under `Runtime` -> `Change runtime type.`  select `GPU` 

Download the modules

# @title Download the modules

# @markdown Run this cell!

# @markdown Download from OSF. Original repo: https://github.com/raymondchua/nma_rl_games.git

import os, io, sys, shutil, zipfile
from urllib.request import urlopen

# download from github repo directly
#!git clone git://github.com/raymondchua/nma_rl_games.git --quiet
REPO_PATH = 'nma_rl_games'

if os.path.exists(REPO_PATH):
  download_string = "Redownloading"
  shutil.rmtree(REPO_PATH)
else:
  download_string = "Downloading"

zipurl = 'https://osf.io/kf4p9/download'
print(f"{download_string} and unzipping the file... Please wait.")
with urlopen(zipurl) as zipresp:
  with zipfile.ZipFile(io.BytesIO(zipresp.read())) as zfile:
    zfile.extractall()
print("Download completed.")

print(f"Add the {REPO_PATH} in the path and import the modules.")
# add the repo in the path
sys.path.append('nma_rl_games/alpha-zero')

# @markdown Import modules designed for use in this notebook
import Arena

from utils import *
from Game import Game
from MCTS import MCTS
from NeuralNet import NeuralNet

from othello.OthelloPlayers import *
from othello.OthelloLogic import Board
from othello.OthelloGame import OthelloGame
from othello.pytorch.NNet import NNetWrapper as NNet
Downloading and unzipping the file... Please wait.
Download completed.
Add the nma_rl_games in the path and import the modules.

The hyperparameters used throughout the notebook.

args = dotdict({
    'numIters': 1,            # In training, number of iterations = 1000 and num of episodes = 100
    'numEps': 1,              # Number of complete self-play games to simulate during a new iteration.
    'tempThreshold': 15,      # To control exploration and exploitation
    'updateThreshold': 0.6,   # During arena playoff, new neural net will be accepted if threshold or more of games are won.
    'maxlenOfQueue': 200,     # Number of game examples to train the neural networks.
    'numMCTSSims': 15,        # Number of games moves for MCTS to simulate.
    'arenaCompare': 10,       # Number of games to play during arena play to determine if new net will be accepted.
    'cpuct': 1,
    'maxDepth':5,             # Maximum number of rollouts
    'numMCsims': 5,           # Number of monte carlo simulations
    'mc_topk': 3,             # Top k actions for monte carlo rollout

    'checkpoint': './temp/',
    'load_model': False,
    'load_folder_file': ('/dev/models/8x100x50','best.pth.tar'),
    'numItersForTrainExamplesHistory': 20,

    # Define neural network arguments
    'lr': 0.001,               # lr: Learning Rate
    'dropout': 0.3,
    'epochs': 10,
    'batch_size': 64,
    'device': DEVICE,
    'num_channels': 512,
})

Section 0: Introduction

Video 0: Introduction


Section 1: Create a game/agent loop for RL

Time estimate: ~15mins

Video 1: A game loop for RL

Introduction to OthelloGame

Game Components:

  1. A square 8x8 board (you could use a chess board)

  2. 64 discs coloured black on one side and white on the opposite side.

Setup: The board will start with 2 black discs and 2 white discs at the centre of the board. They are arranged with black forming a North-East to South-West direction. White is forming a North-West to South-East direction. The goal is to get the majority of colour discs on the board at the end of the game.

Strategy: Two players ~ each player gets 32 discs and black always starts the game. Then the game alternates between white and black until:

- One player can not make a valid move to outflank the opponent.
- Both players have no valid moves.

When a player has no valid moves, he pass his turn and the opponent continues. A player can not voluntarily forfeit his turn. When both players can not make a valid move the game ends.

You can play Othello online: https://www.eothello.com/ if you like!

Goal: How to setup a game environment with multiple players for reinforcement learning experiments.

Exercise:

  • Build an agent that plays random moves

  • Connect with connect 4 game

  • Generate games including wins and losses

class OthelloGame(Game):
  """
  Instantiate Othello Game
  """
  square_content = {
      -1: "X",
      +0: "-",
      +1: "O"
      }

  @staticmethod
  def getSquarePiece(piece):
    return OthelloGame.square_content[piece]

  def __init__(self, n):
    self.n = n

  def getInitBoard(self):
    # Return initial board (numpy board)
    b = Board(self.n)
    return np.array(b.pieces)

  def getBoardSize(self):
    # (a,b) tuple
    return (self.n, self.n)

  def getActionSize(self):
    # Return number of actions, n is the board size and +1 is for no-op action
    return self.n*self.n + 1

  def getCanonicalForm(self, board, player):
    # Return state if player==1, else return -state if player==-1
    return player*board

  def stringRepresentation(self, board):
    return board.tobytes()

  def stringRepresentationReadable(self, board):
    board_s = "".join(self.square_content[square] for row in board for square in row)
    return board_s

  def getScore(self, board, player):
    b = Board(self.n)
    b.pieces = np.copy(board)
    return b.countDiff(player)

  @staticmethod
  def display(board):
    n = board.shape[0]
    print("   ", end="")
    for y in range(n):
      print(y, end=" ")
    print("")
    print("-----------------------")
    for y in range(n):
      print(y, "|", end="")    # Print the row
      for x in range(n):
        piece = board[y][x]    # Get the piece to print
        print(OthelloGame.square_content[piece], end=" ")
      print("|")
    print("-----------------------")

  def getNextState(self, board, player, action):
    """
    Helper function to make valid move
    If player takes action on board, return next (board,player)
    and action must be a valid move

    Args:
      board: np.ndarray
        Board of size n x n [6x6 in this case]
      player: Integer
        ID of current player
      action: np.ndarray
        Space of actions

    Returns:
      (board,player) tuple signifying next state
    """
    if action == self.n*self.n:
      return (board, -player)
    b = Board(self.n)
    b.pieces = np.copy(board)
    move = (int(action/self.n), action%self.n)
    b.execute_move(move, player)
    return (b.pieces, -player)

  def getValidMoves(self, board, player):
    """
    Helper function to make valid move
    If player takes action on board, return next (board,player)
    and action must be a valid move

    Args:
      board: np.ndarray
        Board of size n x n [6x6 in this case]
      player: Integer
        ID of current player
      action: np.ndarray
        Space of action

    Returns:
      valids: np.ndarray
        Returns a fixed size binary vector
    """
    valids = [0]*self.getActionSize()
    b = Board(self.n)
    b.pieces = np.copy(board)
    legalMoves =  b.get_legal_moves(player)
    if len(legalMoves)==0:
      valids[-1]=1
      return np.array(valids)
    for x, y in legalMoves:
      valids[self.n*x+y]=1
    return np.array(valids)

  def getGameEnded(self, board, player):
    """
    Helper function to signify if game has ended

    Args:
      board: np.ndarray
        Board of size n x n [6x6 in this case]
      player: Integer
        ID of current player

    Returns:
      0 if not ended, 1 if player 1 won, -1 if player 1 lost
    """
    b = Board(self.n)
    b.pieces = np.copy(board)
    if b.has_legal_moves(player):
      return 0
    if b.has_legal_moves(-player):
      return 0
    if b.countDiff(player) > 0:
      return 1
    return -1

  def getSymmetries(self, board, pi):
    """
    Get mirror/rotational configurations of board

    Args:
      board: np.ndarray
        Board of size n x n [6x6 in this case]
      pi: np.ndarray
        Dimension of board

    Returns:
      l: list
        90 degree of board, 90 degree of pi_board
    """
    assert(len(pi) == self.n**2+1)  # 1 for pass
    pi_board = np.reshape(pi[:-1], (self.n, self.n))
    l = []

    for i in range(1, 5):
      for j in [True, False]:
        newB = np.rot90(board, i)
        newPi = np.rot90(pi_board, i)
        if j:
          newB = np.fliplr(newB)
          newPi = np.fliplr(newPi)
        l += [(newB, list(newPi.ravel()) + [pi[-1]])]
    return l

Section 1.1: Create a random player

Coding Exercise 1.1: Implement a random player

class RandomPlayer():
  """
  Simulates Random Player
  """

  def __init__(self, game):
    self.game = game

  def play(self, board):
    """
    Simulates game play

    Args:
      board: np.ndarray
        Board of size n x n [6x6 in this case]

    Returns:
      a: int
        Randomly chosen move
    """
    #################################################
    ## TODO for students: ##
    ## 1. Please compute the valid moves using getValidMoves(). ##
    ## 2. Compute the probability over actions.##
    ## 3. Pick a random action based on the probability computed above.##
    # Fill out function and remove ##
    raise NotImplementedError("Implement the random player")
    #################################################

    valids = ...
    prob = ...
    a = ...

    return a


# Add event to airtable
atform.add_event('Coding Exercise 1.1: Implement a random player')

Click for solution

Section 1.2. Initiate the game board

# Display the board
set_seed(seed=SEED)
game = OthelloGame(6)
board = game.getInitBoard()
game.display(board)
Random seed 2021 has been set.
   0 1 2 3 4 5 
-----------------------
0 |- - - - - - |
1 |- - - - - - |
2 |- - X O - - |
3 |- - O X - - |
4 |- - - - - - |
5 |- - - - - - |
-----------------------
# Observe the game board size
print(f'Board size = {game.getBoardSize()}')

# Observe the action size
print(f'Action size = {game.getActionSize()}')
Board size = (6, 6)
Action size = 37

Section 1.3. Create two random agents to play against each other

# Define the random player
player1 = RandomPlayer(game).play  # Player 1 is a random player
player2 = RandomPlayer(game).play  # Player 2 is a random player

# Define number of games
num_games = 20

# Start the competition
set_seed(seed=SEED)
arena = Arena.Arena(player1, player2 , game, display=None)  # To see the steps of the competition set "display=OthelloGame.display"
result = arena.playGames(num_games, verbose=False)  # return  ( number of games won by player1, num of games won by player2, num of games won by nobody)
print(f"\n\n{result}")
(11, 9, 0)

Section 1.4. Compute win rate for the random player (player 1)

print(f"Number of games won by player1 = {result[0]}, "
      f"Number of games won by player2 = {result[1]} out of {num_games} games")
win_rate_player1 = result[0]/num_games
print(f"\nWin rate for player1 over 20 games: {round(win_rate_player1*100, 1)}%")
Number of games won by player1 = 11, Number of games won by player2 = 9 out of 20 games

Win rate for player1 over 20 games: 55.0%

Section 2: Train a value function from expert game data

Time estimate: ~25mins

Goal: Learn how to train a value function from a dataset of games played by an expert.

Exercise:

  • Load a dataset of expert generated games.

  • Train a network to minimize MSE for win/loss predictions given board states sampled throughout the game. This will be done on a very small number of games. We will provide a network trained on a larger dataset.

Video 2: Train a value function

Section 2.1. Load expert data

def loadTrainExamples(folder, filename):
  """
  Helper function to load Training examples

  Args:
    folder: string
      Path specifying training examples
    filename: string
      File name of training examples

  Returns:
    trainExamplesHistory: list
      Returns examples based on the model were already collected (loaded)
  """
  trainExamplesHistory = []
  modelFile = os.path.join(folder, filename)
  examplesFile = modelFile + ".examples"
  if not os.path.isfile(examplesFile):
    print(f'File "{examplesFile}" with trainExamples not found!')
    r = input("Continue? [y|n]")
    if r != "y":
      sys.exit()
  else:
    print("File with train examples found. Loading it...")
    with open(examplesFile, "rb") as f:
      trainExamplesHistory = Unpickler(f).load()
    print('Loading done!')
    return trainExamplesHistory
path = "nma_rl_games/alpha-zero/pretrained_models/data/"
loaded_games = loadTrainExamples(folder=path, filename='checkpoint_1.pth.tar')
File with train examples found. Loading it...
Loading done!

Section 2.2. Define the Neural Network Architecture for Othello

Coding Exercise 2.2: Implement the NN OthelloNNet for Othello

class OthelloNNet(nn.Module):
  """
  Instantiate Othello Neural Net with following configuration
  nn.Conv2d(1, args.num_channels, 3, stride=1, padding=1) # Convolutional Layer 1
  nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1, padding=1) # Convolutional Layer 2
  nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1) # Convolutional Layer 3
  nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1) # Convolutional Layer 4
  nn.BatchNorm2d(args.num_channels) X 4
  nn.Linear(args.num_channels * (self.board_x - 4) * (self.board_y - 4), 1024) # Fully-connected Layer 1
  nn.Linear(1024, 512) # Fully-connected Layer 2
  nn.Linear(512, self.action_size) # Fully-connected Layer 3
  nn.Linear(512, 1) # Fully-connected Layer 4
  """

  def __init__(self, game, args):
    """
    Initialise game parameters

    Args:
      game: OthelloGame instance
        Instance of the OthelloGame class above;
      args: dictionary
        Instantiates number of iterations and episodes, controls temperature threshold, queue length,
        arena, checkpointing, and neural network parameters:
        learning-rate: 0.001, dropout: 0.3, epochs: 10, batch_size: 64,
        num_channels: 512

    Returns:
      Nothing
    """
    self.board_x, self.board_y = game.getBoardSize()
    self.action_size = game.getActionSize()
    self.args = args

    super(OthelloNNet, self).__init__()
    self.conv1 = nn.Conv2d(1, args.num_channels, 3, stride=1, padding=1)
    self.conv2 = nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1,
                           padding=1)
    self.conv3 = nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1)
    self.conv4 = nn.Conv2d(args.num_channels, args.num_channels, 3, stride=1)

    self.bn1 = nn.BatchNorm2d(args.num_channels)
    self.bn2 = nn.BatchNorm2d(args.num_channels)
    self.bn3 = nn.BatchNorm2d(args.num_channels)
    self.bn4 = nn.BatchNorm2d(args.num_channels)

    self.fc1 = nn.Linear(args.num_channels * (self.board_x - 4) * (self.board_y - 4), 1024)
    self.fc_bn1 = nn.BatchNorm1d(1024)

    self.fc2 = nn.Linear(1024, 512)
    self.fc_bn2 = nn.BatchNorm1d(512)

    self.fc3 = nn.Linear(512, self.action_size)

    self.fc4 = nn.Linear(512, 1)

  def forward(self, s):
    """
    Controls forward pass of OthelloNNet

    Args:
      s: np.ndarray
        Array of size (batch_size x board_x x board_y)

    Returns:
      Probability distribution over actions at the current state and the value of the current state.
    """
    s = s.view(-1, 1, self.board_x, self.board_y)                # batch_size x 1 x board_x x board_y
    s = F.relu(self.bn1(self.conv1(s)))                          # batch_size x num_channels x board_x x board_y
    s = F.relu(self.bn2(self.conv2(s)))                          # batch_size x num_channels x board_x x board_y
    s = F.relu(self.bn3(self.conv3(s)))                          # batch_size x num_channels x (board_x-2) x (board_y-2)
    s = F.relu(self.bn4(self.conv4(s)))                          # batch_size x num_channels x (board_x-4) x (board_y-4)
    s = s.view(-1, self.args.num_channels * (self.board_x - 4) * (self.board_y - 4))

    s = F.dropout(F.relu(self.fc_bn1(self.fc1(s))), p=self.args.dropout, training=self.training)  # batch_size x 1024
    s = F.dropout(F.relu(self.fc_bn2(self.fc2(s))), p=self.args.dropout, training=self.training)  # batch_size x 512

    pi = self.fc3(s)  # batch_size x action_size
    v = self.fc4(s)   # batch_size x 1
    #################################################
    ## TODO for students: Please compute a probability distribution over 'pi' using log softmax (for numerical stability)
    # Fill out function and remove
    raise NotImplementedError("Calculate the probability distribution and the value")
    #################################################
    # Returns probability distribution over actions at the current state and the value of the current state.
    return ..., ...

# Add event to airtable
atform.add_event('Coding Exercise 2.2: Implement the NN OthelloNNet for Othello')

Click for solution

Section 2.3. Define the Value network

During training, the ground truth will be uploaded from the MCTS simulations available at checkpoint_x.path.tar.examples.

Coding Exercise 2.3: Implement the ValueNetwork

class ValueNetwork(NeuralNet):
  """
  Initiates the Value Network
  """

  def __init__(self, game):
    """
    Initialise network parameters

    Args:
      game: OthelloGame instance
        Instance of the OthelloGame class above;

    Returns:
      Nothing
    """
    self.nnet = OthelloNNet(game, args)
    self.board_x, self.board_y = game.getBoardSize()
    self.action_size = game.getActionSize()
    self.nnet.to(args.device)

  def train(self, games):
    """
    Function to train value network

    Args:
      games: list
        List of examples with each example is of form (board, pi, v)

    Returns:
      Nothing
    """
    optimizer = optim.Adam(self.nnet.parameters())
    for examples in games:
      for epoch in range(args.epochs):
        print('EPOCH ::: ' + str(epoch + 1))
        self.nnet.train()
        v_losses = []   # To store the losses per epoch
        batch_count = int(len(examples) / args.batch_size)  # len(examples)=200, batch-size=64, batch_count=3
        t = tqdm(range(batch_count), desc='Training Value Network')
        for _ in t:
          sample_ids = np.random.randint(len(examples), size=args.batch_size)  # Read the ground truth information from MCTS simulation using the loaded examples
          boards, pis, vs = list(zip(*[examples[i] for i in sample_ids]))  # Length of boards, pis, vis = 64
          boards = torch.FloatTensor(np.array(boards).astype(np.float64))
          target_vs = torch.FloatTensor(np.array(vs).astype(np.float64))

          # Predict
          # To run on GPU if available
          boards, target_vs = boards.contiguous().to(args.device), target_vs.contiguous().to(args.device)

          #################################################
          ## TODO for students:
          ## 1. Compute the value predicted by OthelloNNet() ##
          ## 2. First implement the loss_v() function below and then use it to update the value loss. ##
          # Fill out function and remove
          raise NotImplementedError("Compute the output")
          #################################################
          # Compute output
          _, out_v = ...
          l_v = ...  # Total loss

          # Record loss
          v_losses.append(l_v.item())
          t.set_postfix(Loss_v=l_v.item())

          # Compute gradient and do SGD step
          optimizer.zero_grad()
          l_v.backward()
          optimizer.step()

  def predict(self, board):
    """
    Function to perform prediction

    Args:
      board: np.ndarray
        Board of size n x n [6x6 in this case]

    Returns:
      v: OthelloNet instance
        Data of the OthelloNet class instance above;
    """
    # Timing
    start = time.time()

    # Preparing input
    board = torch.FloatTensor(board.astype(np.float64))
    board = board.contiguous().to(args.device)
    board = board.view(1, self.board_x, self.board_y)
    self.nnet.eval()
    with torch.no_grad():
        _, v = self.nnet(board)
    return v.data.cpu().numpy()[0]

  def loss_v(self, targets, outputs):
    """
    Calculates Mean squared error

    Args:
      targets: np.ndarray
        Ground Truth variables corresponding to input
      outputs: np.ndarray
        Predictions of Network

    Returns:
      MSE Loss calculated as: square of the difference between your model's predictions
      and the ground truth and average across the whole dataset
    """
    #################################################
    ## TODO for students: Please compute Mean squared error and return as output. ##
    # Fill out function and remove
    raise NotImplementedError("Calculate the loss")
    #################################################
    # Mean squared error (MSE)
    return ...

  def save_checkpoint(self, folder='checkpoint', filename='checkpoint.pth.tar'):
    """
    Code Checkpointing

    Args:
      folder: string
        Path specifying training examples
      filename: string
        File name of training examples

    Returns:
      Nothing
    """
    filepath = os.path.join(folder, filename)
    if not os.path.exists(folder):
      print("Checkpoint Directory does not exist! Making directory {}".format(folder))
      os.mkdir(folder)
    else:
      print("Checkpoint Directory exists! ")
    torch.save({'state_dict': self.nnet.state_dict(),}, filepath)
    print("Model saved! ")

  def load_checkpoint(self, folder='checkpoint', filename='checkpoint.pth.tar'):
    """
    Load code checkpoint

    Args:
      folder: string
        Path specifying training examples
      filename: string
        File name of training examples

    Returns:
      Nothing
    """
    # https://github.com/pytorch/examples/blob/master/imagenet/main.py#L98
    filepath = os.path.join(folder, filename)
    if not os.path.exists(filepath):
      raise ("No model in path {}".format(filepath))

    checkpoint = torch.load(filepath, map_location=args.device)
    self.nnet.load_state_dict(checkpoint['state_dict'])

# Add event to airtable
atform.add_event('Coding Exercise 2.3: Implement the ValueNetwork')

Click for solution

Section 2.4. Train the value network and observe the MSE loss progress

Important: Run this cell ONLY if you do not have access to the pretrained models in the rl_for_games repository.

if not os.listdir('nma_rl_games/alpha-zero/pretrained_models/models/'):
  set_seed(seed=SEED)
  game = OthelloGame(6)
  vnet = ValueNetwork(game)
  vnet.train(loaded_games)

Section 3: Use a trained value network to play games

Time estimate: ~25mins

Goal: Learn how to use a value function in order to make a player that works better than a random player.

Exercise:

  • Sample random valid moves and use the value function to rank them

  • Choose the best move as the action and play it Show that doing so beats the random player

Hint: You might need to change the sign of the value based on the player.

Video 3: Play games using a value function

Coding Exercise 3: Value-based player

model_save_name = 'ValueNetwork.pth.tar'
path = "nma_rl_games/alpha-zero/pretrained_models/models/"
set_seed(seed=SEED)
game = OthelloGame(6)
vnet = ValueNetwork(game)
vnet.load_checkpoint(folder=path, filename=model_save_name)
Random seed 2021 has been set.
class ValueBasedPlayer():
  """
  Simulate Value Based Player
  """

  def __init__(self, game, vnet):
    """
    Initialise value based player parameters

    Args:
      game: OthelloGame instance
        Instance of the OthelloGame class above;
      vnet: Value Network instance
        Instance of the Value Network class above;

    Returns:
      Nothing
    """
    self.game = game
    self.vnet = vnet

  def play(self, board):
    """
    Simulate game play

    Args:
      board: np.ndarray
        Board of size n x n [6x6 in this case]

    Returns:
      candidates: List
        Collection of tuples describing action and values of future predicted states
    """
    valids = self.game.getValidMoves(board, 1)
    candidates = []
    max_num_actions = 4
    va = np.where(valids)[0]
    va_list = va.tolist()
    random.shuffle(va_list)
    #################################################
    ## TODO for students: In the first part, please return the next board state using getNextState(), then predict
    ## the value of next state using value network, and finally add the value and action as a tuple to the candidate list.
    ## Note that you need to reverse the sign of the value. In zero-sum games the players flip every turn. In detail, we train
    ## a value function to think about the game from one player's (either black or white) perspective. In order to use the same
    ## value function to estimate how good the position is for the other player, we need to take the negative of the output of
    ## the function. E.g., if the value function is trained for white's perspective and says that white is likely to win the game
    ## from the current state with an output of 0.75, this similarly means that it would suggest that black is very unlikely (-0.75)
    ## to win the game from the current state.##
    # Fill out function and remove
    raise NotImplementedError("Implement the value-based player")
    #################################################
    for a in va_list:
      # Return next board state using getNextState() function
      nextBoard, _ = ...
      # Predict the value of next state using value network
      value = ...
      # Add the value and the action as a tuple to the candidate lists, note that you might need to change the sign of the value based on the player
      candidates += ...

      if len(candidates) == max_num_actions:
        break

    candidates.sort()

    return candidates[0][1]


# Add event to airtable
atform.add_event('Coding Exercise 3: Value-based player')

# Playing games between a value-based player and a random player
set_seed(seed=SEED)
num_games = 20
player1 = ValueBasedPlayer(game, vnet).play
player2 = RandomPlayer(game).play
arena = Arena.Arena(player1, player2, game, display=OthelloGame.display)
## Uncomment the code below to check your code!
# result = arena.playGames(num_games, verbose=False)
# print(f"\n\n{result}")
Random seed 2021 has been set.

Click for solution

(14, 6, 0)

Result of pitting a value-based player against a random player

print(f"Number of games won by player1 = {result[0]}, "
      f"Number of games won by player2 = {result[1]}, out of {num_games} games")
win_rate_player1 = result[0]/num_games # result[0] is the number of times that player 1 wins
print(f"\nWin rate for player1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Number of games won by player1 = 14, Number of games won by player2 = 6, out of 20 games

Win rate for player1 over 20 games: 70.0%

Section 4: Train a policy network from expert game data

Time estimate: ~20mins

Goal: How to train a policy network via supervised learning / behavioural cloning.

Exercise:

  • Train a network to predict the next move in an expert dataset by maximizing the log likelihood of the next action.

Video 4: Train a policy network

Coding Exercise 4: Implement PolicyNetwork

class PolicyNetwork(NeuralNet):
  """
  Initialise Policy Network
  """

  def __init__(self, game):
    """
    Initalise policy network paramaters

    Args:
      game: OthelloGame instance
        Instance of the OthelloGame class above;

    Returns:
      Nothing
    """
    self.nnet = OthelloNNet(game, args)
    self.board_x, self.board_y = game.getBoardSize()
    self.action_size = game.getActionSize()
    self.nnet.to(args.device)

  def train(self, games):
    """
    Function for Policy Network Training

    Args:
      games: list
        List of examples where each example is of form (board, pi, v)

    Return:
      Nothing
    """
    optimizer = optim.Adam(self.nnet.parameters())

    for examples in games:
      for epoch in range(args.epochs):
        print('EPOCH ::: ' + str(epoch + 1))
        self.nnet.train()
        pi_losses = []

        batch_count = int(len(examples) / args.batch_size)

        t = tqdm(range(batch_count), desc='Training Policy Network')
        for _ in t:
          sample_ids = np.random.randint(len(examples), size=args.batch_size)
          boards, pis, _ = list(zip(*[examples[i] for i in sample_ids]))
          boards = torch.FloatTensor(np.array(boards).astype(np.float64))
          target_pis = torch.FloatTensor(np.array(pis))

          # Predict
          boards, target_pis = boards.contiguous().to(args.device), target_pis.contiguous().to(args.device)

          #################################################
          ## TODO for students: ##
          ## 1. Compute the policy (pi) predicted by OthelloNNet() ##
          ## 2. Implement the loss_pi() function below and then use it to update the policy loss. ##
          # Fill out function and remove
          raise NotImplementedError("Compute the output")
          #################################################
          # Compute output
          out_pi, _ = ...
          l_pi = ...

          # Record loss
          pi_losses.append(l_pi.item())
          t.set_postfix(Loss_pi=l_pi.item())

          # Compute gradient and do SGD step
          optimizer.zero_grad()
          l_pi.backward()
          optimizer.step()

  def predict(self, board):
    """
    Function to perform prediction

    Args:
      board: np.ndarray
        Board of size n x n [6x6 in this case]

    Returns:
      Data from the OthelloNet class instance above;
    """
    # Timing
    start = time.time()

    # Preparing input
    board = torch.FloatTensor(board.astype(np.float64))
    board = board.contiguous().to(args.device)
    board = board.view(1, self.board_x, self.board_y)
    self.nnet.eval()
    with torch.no_grad():
      pi,_ = self.nnet(board)
    return torch.exp(pi).data.cpu().numpy()[0]

  def loss_pi(self, targets, outputs):
    """
    Calculates Negative Log Likelihood(NLL) of Targets

    Args:
      targets: np.ndarray
        Ground Truth variables corresponding to input
      outputs: np.ndarray
        Predictions of Network

    Returns:
      Negative Log Likelihood calculated as: When training a model, we aspire to find the minima of a
      loss function given a set of parameters (in a neural network, these are the weights and biases).
      Sum the loss function to all the correct classes. So, whenever the network assigns high confidence at
      the correct class, the NLL is low, but when the network assigns low confidence at the correct class,
      the NLL is high.
    """
    #################################################
    ## TODO for students: To implement the loss function, please compute and return the negative log likelihood of targets.
    ## For more information, here is a reference that connects the expression to the neg-log-prob: https://gombru.github.io/2018/05/23/cross_entropy_loss/
    # Fill out function and remove
    raise NotImplementedError("Compute the loss")
    #################################################
    return ...

  def save_checkpoint(self, folder='checkpoint', filename='checkpoint.pth.tar'):
    """
    Code Checkpointing

    Args:
      folder: string
        Path specifying training examples
      filename: string
        File name of training examples

    Returns:
      Nothing
    """
    filepath = os.path.join(folder, filename)
    if not os.path.exists(folder):
      print("Checkpoint Directory does not exist! Making directory {}".format(folder))
      os.mkdir(folder)
    else:
      print("Checkpoint Directory exists! ")
    torch.save({'state_dict': self.nnet.state_dict(),}, filepath)
    print("Model saved! ")

  def load_checkpoint(self, folder='checkpoint', filename='checkpoint.pth.tar'):
    """
    Load code checkpoint

    Args:
      folder: string
        Path specifying training examples
      filename: string
        File name of training examples

    Returns:
      Nothing
    """
    # https://github.com/pytorch/examples/blob/master/imagenet/main.py#L98
    filepath = os.path.join(folder, filename)
    if not os.path.exists(filepath):
      raise ("No model in path {}".format(filepath))
    checkpoint = torch.load(filepath, map_location=args.device)
    self.nnet.load_state_dict(checkpoint['state_dict'])


# Add event to airtable
atform.add_event('Coding Exercise 4: Implement PolicyNetwork')

Click for solution

Train the policy network

Important: Only run this cell if you do not have access to the pretrained models in the rl_for_games repository.

if not os.listdir('nma_rl_games/alpha-zero/pretrained_models/models/'):
  set_seed(seed=SEED)
  game = OthelloGame(6)
  pnet = PolicyNetwork(game)
  pnet.train(loaded_games)

Section 5: Use a trained policy network to play games

Time estimate: ~20mins

Goal: How to use a policy network to play games.

Exercise:

  • Use the policy network to give probabilities for the next move.

  • Build a player that takes the move given the maximum probability by the network.

  • Compare this to another player that samples moves according to the probability distribution output by the network.

Video 5: Play games using a policy network

Coding Exercise 5: Implement the PolicyBasedPlayer

model_save_name = 'PolicyNetwork.pth.tar'
path = "nma_rl_games/alpha-zero/pretrained_models/models/"
set_seed(seed=SEED)
game = OthelloGame(6)
pnet = PolicyNetwork(game)
pnet.load_checkpoint(folder=path, filename=model_save_name)
Random seed 2021 has been set.
class PolicyBasedPlayer():
  """
  Simulate Policy Based Player
  """

  def __init__(self, game, pnet, greedy=True):
    """
    Initialize Policy based player parameters

    Args:
      game: OthelloGame instance
        Instance of the OthelloGame class above;
      pnet: Policy Network instance
        Instance of the Policy Network class above
      greedy: Boolean
        If true, implement greedy approach
        Else, implement random sample policy based player

    Returns:
      Nothing
    """
    self.game = game
    self.pnet = pnet
    self.greedy = greedy

  def play(self, board):
    """
    Simulate game play

    Args:
      board: np.ndarray
        Board of size n x n [6x6 in this case]

    Returns:
      a: np.ndarray
        If greedy, implement greedy policy player
        Else, implement random sample policy based player
    """
    valids = self.game.getValidMoves(board, 1)
    #################################################
    ## TODO for students:  ##
    ## 1. Compute the action probabilities using policy network pnet()
    ## 2. Mask invalid moves using valids variable and the action probabilites computed above.
    ## 3. Compute the sum over valid actions and store them in sum_vap.
    # Fill out function and remove
    raise NotImplementedError("Define the play")
    #################################################
    action_probs = ...
    vap = ...  # Masking invalid moves
    sum_vap = ...

    if sum_vap > 0:
      vap /= sum_vap  # Renormalize
    else:
      # If all valid moves were masked we make all valid moves equally probable
      print("All valid moves were masked, doing a workaround.")
      vap = vap + valids
      vap /= np.sum(vap)

    if self.greedy:
      # Greedy policy player
      a = np.where(vap == np.max(vap))[0][0]
    else:
      # Sample-based policy player
      a = np.random.choice(self.game.getActionSize(), p=vap)

    return a


# Add event to airtable
atform.add_event('Coding Exercise 5: Implement the PolicyBasedPlayer')

# Playing games
set_seed(seed=SEED)
num_games = 20
player1 = PolicyBasedPlayer(game, pnet, greedy=True).play
player2 = RandomPlayer(game).play
arena = Arena.Arena(player1, player2, game, display=OthelloGame.display)
## Uncomment below to test!
# result = arena.playGames(num_games, verbose=False)
# print(f"\n\n{result}")
# win_rate_player1 = result[0] / num_games
# print(f"\nWin rate for player1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Random seed 2021 has been set.

Click for solution

model_save_name = 'PolicyNetwork.pth.tar'
path = "nma_rl_games/alpha-zero/pretrained_models/models/"
set_seed(seed=SEED)
game = OthelloGame(6)
pnet = PolicyNetwork(game)
pnet.load_checkpoint(folder=path, filename=model_save_name)
Random seed 2021 has been set.
 Win rate for player1 over 20 games: 80.0%

Comparing a policy based player versus a random player

There’s often randomness in the results as we are running the players for a low number of games (only 20 games due compute + time costs). So, when students are running the cells they might not get the expected result. To better measure the strength of players you can run more games!

set_seed(seed=SEED)
num_games = 20
game = OthelloGame(6)
player1 = PolicyBasedPlayer(game, pnet, greedy=False).play
player2 = RandomPlayer(game).play
arena = Arena.Arena(player1, player2, game, display=OthelloGame.display)
result = arena.playGames(num_games, verbose=False)
print(f"\n\n{result}")
win_rate_player1 = result[0]/num_games
print(f"Win rate for player1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Win rate for player1 over 20 games: 95.0%

Compare greedy policy based player versus value based player

set_seed(seed=SEED)
num_games = 20
game = OthelloGame(6)
player1 = PolicyBasedPlayer(game, pnet).play
player2 = ValueBasedPlayer(game, vnet).play
arena = Arena.Arena(player1, player2, game, display=OthelloGame.display)
result = arena.playGames(num_games, verbose=False)
print(f"\n\n{result}")
win_rate_player1 = result[0]/num_games
print(f"Win rate for player 1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Win rate for player 1 over 20 games: 55.0%

Compare greedy policy based player versus sample-based policy player

set_seed(seed=SEED)
num_games = 20
game = OthelloGame(6)
player1 = PolicyBasedPlayer(game, pnet).play # greedy player
player2 = PolicyBasedPlayer(game, pnet, greedy=False).play # sample-based player
arena = Arena.Arena(player1, player2, game, display=OthelloGame.display)
result = arena.playGames(num_games, verbose=False)
print(f"\n\n{result}")
win_rate_player1 = result[0]/num_games
print(f"Win rate for player 1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
 Win rate for player 1 over 20 games: 50.0%

Section 6: Plan using Monte Carlo Rollouts

Time estimate: ~15mins

Goal: Teach the students the core idea behind using simulated rollouts to understand the future and value actions.

Exercise:

  • Build a loop to run Monte Carlo simulations using the policy network.

  • Use this to obtain better estimates of the value of moves.

Video 6: Play using Monte-Carlo rollouts

Coding Exercise 6: MonteCarlo

class MonteCarlo():
  """
  Implementation of Monte Carlo Algorithm
  """

  def __init__(self, game, nnet, args):
    """
    Initialize Monte Carlo Parameters

    Args:
      game: OthelloGame instance
        Instance of the OthelloGame class above;
      nnet: OthelloNet instance
        Instance of the OthelloNNet class above;
      args: dictionary
        Instantiates number of iterations and episodes, controls temperature threshold, queue length,
        arena, checkpointing, and neural network parameters:
        learning-rate: 0.001, dropout: 0.3, epochs: 10, batch_size: 64,
        num_channels: 512

    Returns:
      Nothing
    """
    self.game = game
    self.nnet = nnet
    self.args = args

    self.Ps = {}  # Stores initial policy (returned by neural net)
    self.Es = {}  # Stores game.getGameEnded ended for board s

  # Call this rollout
  def simulate(self, canonicalBoard):
    """
    Helper function to simulate one Monte Carlo rollout

    Args:
      canonicalBoard: np.ndarray
        Canonical Board of size n x n [6x6 in this case]

    Returns:
      temp_v:
        Terminal State
    """
    s = self.game.stringRepresentation(canonicalBoard)
    init_start_state = s
    temp_v = 0
    isfirstAction = None

    for i in range(self.args.maxDepth): # maxDepth

      if s not in self.Es:
        self.Es[s] = self.game.getGameEnded(canonicalBoard, 1)
      if self.Es[s] != 0:
        # Terminal state
        temp_v= -self.Es[s]
        break

      self.Ps[s], v = self.nnet.predict(canonicalBoard)
      valids = self.game.getValidMoves(canonicalBoard, 1)
      self.Ps[s] = self.Ps[s] * valids  # Masking invalid moves
      sum_Ps_s = np.sum(self.Ps[s])

      if sum_Ps_s > 0:
        self.Ps[s] /= sum_Ps_s  # Renormalize
      else:
        # If all valid moves were masked make all valid moves equally probable
        # NB! All valid moves may be masked if either your NNet architecture is insufficient or you've get overfitting or something else.
        # If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process.
        log.error("All valid moves were masked, doing a workaround.")
        self.Ps[s] = self.Ps[s] + valids
        self.Ps[s] /= np.sum(self.Ps[s])

      #################################################
      ## TODO for students: Take a random action.
      ## 1. Take the random action.
      ## 2. Find the next state and the next player from the environment.
      ## 3. Get the canonical form of the next state.
      # Fill out function and remove
      raise NotImplementedError("Take the action, find the next state")
      #################################################
      a = ...
      next_s, next_player = self.game.getNextState(..., ..., ...)
      next_s = self.game.getCanonicalForm(..., ...)

      s = self.game.stringRepresentation(next_s)
      temp_v = v

    return temp_v


# Add event to airtable
atform.add_event('Coding Exercise 6: MonteCarlo')

Click for solution


Section 7: Use Monte Carlo simulations to play games

Time estimate: ~20mins

Goal: Teach students how to use simple Monte Carlo planning to play games.

Video 7: Play with planning

Coding Exercise 7: Monte-Carlo simulations

  • Incorporate Monte Carlo simulations into an agent.

  • Run the resulting player versus the random, value-based, and policy-based players.

# Load MC model from the repository
mc_model_save_name = 'MC.pth.tar'
path = "nma_rl_games/alpha-zero/pretrained_models/models/"
class MonteCarloBasedPlayer():
  """
  Simulate Player based on Monte Carlo Algorithm
  """

  def __init__(self, game, nnet, args):
    """
    Initialize Monte Carlo Parameters

    Args:
      game: OthelloGame instance
        Instance of the OthelloGame class above;
      nnet: OthelloNet instance
        Instance of the OthelloNNet class above;
      args: dictionary
        Instantiates number of iterations and episodes, controls temperature threshold, queue length,
        arena, checkpointing, and neural network parameters:
        learning-rate: 0.001, dropout: 0.3, epochs: 10, batch_size: 64,
        num_channels: 512

    Returns:
      Nothing
    """
    self.game = game
    self.nnet = nnet
    self.args = args
    #################################################
    ## TODO for students: Instantiate the Monte Carlo class.
    # Fill out function and remove
    raise NotImplementedError("Use Monte Carlo!")
    #################################################
    self.mc = ...
    self.K = self.args.mc_topk

  def play(self, canonicalBoard):
    """
    Simulate Play on Canonical Board

    Args:
      canonicalBoard: np.ndarray
        Canonical Board of size n x n [6x6 in this case]

    Returns:
      best_action: tuple
        (avg_value, action) i.e., Average value associated with corresponding action
        i.e., Action with the highest topK probability
    """
    self.qsa = []
    s = self.game.stringRepresentation(canonicalBoard)
    Ps, v = self.nnet.predict(canonicalBoard)
    valids = self.game.getValidMoves(canonicalBoard, 1)
    Ps = Ps * valids  # Masking invalid moves
    sum_Ps_s = np.sum(Ps)

    if sum_Ps_s > 0:
      Ps /= sum_Ps_s  # Renormalize
    else:
      # If all valid moves were masked make all valid moves equally probable
      # NB! All valid moves may be masked if either your NNet architecture is insufficient or you've get overfitting or something else.
      # If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process.
      log = logging.getLogger(__name__)
      log.error("All valid moves were masked, doing a workaround.")
      Ps = Ps + valids
      Ps /= np.sum(Ps)

    num_valid_actions = np.shape(np.nonzero(Ps))[1]

    if num_valid_actions < self.K:
      top_k_actions = np.argpartition(Ps,-num_valid_actions)[-num_valid_actions:]
    else:
      top_k_actions = np.argpartition(Ps,-self.K)[-self.K:]  # To get actions that belongs to top k prob
    #################################################
    ## TODO for students:
    ## 1. For each action in the top-k actions
    ## 2. Get the next state using getNextState() function. You can find the implementation of this function in Section 1 in OthelloGame() class.
    ## 3. Get the canonical form of the getNextState().
    # Fill out function and remove
    raise NotImplementedError("Loop for the top actions")
    #################################################
    for action in ...:
      next_s, next_player = self.game.getNextState(..., ..., ...)
      next_s = self.game.getCanonicalForm(..., ...)

      values = []

      # Do some rollouts
      for rollout in range(self.args.numMCsims):
        value = self.mc.simulate(canonicalBoard)
        values.append(value)

      # Average out values
      avg_value = np.mean(values)
      self.qsa.append((avg_value, action))

    self.qsa.sort(key=lambda a: a[0])
    self.qsa.reverse()
    best_action = self.qsa[0][1]
    return best_action

  def getActionProb(self, canonicalBoard, temp=1):
    """
    Helper function to get probabilities associated with each action

    Args:
      canonicalBoard: np.ndarray
        Canonical Board of size n x n [6x6 in this case]
      temp: Integer
        Signifies if game is in terminal state

    Returns:
      action_probs: List
        Probability associated with corresponding action
    """
    if self.game.getGameEnded(canonicalBoard, 1) != 0:
      return np.zeros((self.game.getActionSize()))

    else:
      action_probs = np.zeros((self.game.getActionSize()))
      best_action = self.play(canonicalBoard)
      action_probs[best_action] = 1

    return action_probs


# Add event to airtable
atform.add_event('Coding Exercise 7: MonteCarlo siumulations')

set_seed(seed=SEED)
game = OthelloGame(6)
# Run the resulting player versus the random player
rp = RandomPlayer(game).play
num_games = 20  # Feel free to change this number

n1 = NNet(game)  # nNet players
n1.load_checkpoint(folder=path, filename=mc_model_save_name)
args1 = dotdict({'numMCsims': 10, 'maxRollouts':5, 'maxDepth':5, 'mc_topk': 3})

## Uncomment below to check Monte Carlo agent!
# print('\n******MC player versus random player******')
# mc1 = MonteCarloBasedPlayer(game, n1, args1)
# n1p = lambda x: np.argmax(mc1.getActionProb(x))
# arena = Arena.Arena(n1p, rp, game, display=OthelloGame.display)
# MC_result = arena.playGames(num_games, verbose=False)
# print(f"\n\n{MC_result}")
# print(f"\nNumber of games won by player1 = {MC_result[0]}, "
#       f"number of games won by player2 = {MC_result[1]}, out of {num_games} games")
# win_rate_player1 = MC_result[0]/num_games
# print(f"\nWin rate for player1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Random seed 2021 has been set.

Click for solution

Number of games won by player1 = 11, number of games won by player2 = 9, out of 20 games

Win rate for player1 over 20 games: 55.0%

Monte-Carlo player against Value-based player

print('\n******MC player versus value-based player******')
set_seed(seed=SEED)
vp = ValueBasedPlayer(game, vnet).play # Value-based player
arena = Arena.Arena(n1p, vp, game, display=OthelloGame.display)
MC_result = arena.playGames(num_games, verbose=False)
print(f"\n\n{MC_result}")
print(f"\nNumber of games won by player1 = {MC_result[0]}, "
      f"number of games won by player2 = {MC_result[1]}, out of {num_games} games")
win_rate_player1 = MC_result[0]/num_games
print(f"\nWin rate for player1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Number of games won by player1 = 10, number of games won by player2 = 10, out of 20 games

Win rate for player1 over 20 games: 50.0%

Monte-Carlo player against Policy-based player

print('\n******MC player versus policy-based player******')
set_seed(seed=SEED)
pp = PolicyBasedPlayer(game, pnet).play # Policy player
arena = Arena.Arena(n1p, pp, game, display=OthelloGame.display)
MC_result = arena.playGames(num_games, verbose=False)
print(f"\n\n{MC_result}")
print(f"\nNumber of games won by player1 = {MC_result[0]}, "
      f"number of games won by player2 = {MC_result[1]}, out of {num_games} games")
win_rate_player1 = MC_result[0]/num_games
print(f"\nWin rate for player1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Number of games won by player1 = 10, number of games won by player2 = 10, out of 20 games

Win rate for player1 over 20 games: 50.0%

Section 8: Ethical aspects

Time estimate: ~5mins

Video 8: Unstoppable opponents


Summary

In this tutorial, you have learned how to implement a game loop and improve the performance of a random player. More specifically, you are now able to understand the format of two-players games. We learned about value-based and policy-based players, and we compared them with the MCTS method.

Video 9: Outro


Bonus 1: Plan using Monte Carlo Tree Search (MCTS)

*Time estimate: ~30mins

Goal: Teach students to understand the core ideas behind Monte Carlo Tree Search (MCTS).

Video 10: Plan with MCTS

Bonus Coding Exercise 1: MCTS planner

  • Plug together pre-built Selection, Expansion & Backpropagation code to complete an MCTS planner.

  • Deploy the MCTS planner to understand an interesting position, producing value estimates and action counts.

class MCTS():
  """
  This class handles MCTS (Monte Carlo Tree Search).
  """

  def __init__(self, game, nnet, args):
    """
    Initialize parameters of MCTS

    Args:
      game: OthelloGame instance
        Instance of the OthelloGame class above;
      nnet: OthelloNet instance
        Instance of the OthelloNNet class above;
      args: dictionary
        Instantiates number of iterations and episodes, controls temperature threshold, queue length,
        arena, checkpointing, and neural network parameters:
        learning-rate: 0.001, dropout: 0.3, epochs: 10, batch_size: 64,
        num_channels: 512

    Returns:
      Nothing
    """
    self.game = game
    self.nnet = nnet
    self.args = args
    self.Qsa = {}    # Stores Q values for s,a (as defined in the paper)
    self.Nsa = {}    # Stores #times edge s,a was visited
    self.Ns = {}     # Stores #times board s was visited
    self.Ps = {}     # Stores initial policy (returned by neural net)
    self.Es = {}     # Stores game.getGameEnded ended for board s
    self.Vs = {}     # Stores game.getValidMoves for board s

  def search(self, canonicalBoard):
    """
    This function performs one iteration of MCTS. It is recursively called
    till a leaf node is found. The action chosen at each node is one that
    has the maximum upper confidence bound as in the paper.
    Once a leaf node is found, the neural network is called to return an
    initial policy P and a value v for the state. This value is propagated
    up the search path. In case the leaf node is a terminal state, the
    outcome is propagated up the search path. The values of Ns, Nsa, Qsa are
    updated.
    NOTE: the return values are the negative of the value of the current
    state. This is done since v is in [-1,1] and if v is the value of a
    state for the current player, then its value is -v for the other player.

    Args:
      canonicalBoard: np.ndarray
        Canonical Board of size n x n [6x6 in this case]

    Returns:
        v: Float
          The negative of the value of the current canonicalBoard
    """
    s = self.game.stringRepresentation(canonicalBoard)

    if s not in self.Es:
      self.Es[s] = self.game.getGameEnded(canonicalBoard, 1)
    if self.Es[s] != 0:
      # Terminal node
      return -self.Es[s]

    if s not in self.Ps:
      # Leaf node
      self.Ps[s], v = self.nnet.predict(canonicalBoard)
      valids = self.game.getValidMoves(canonicalBoard, 1)
      self.Ps[s] = self.Ps[s] * valids  # Masking invalid moves
      sum_Ps_s = np.sum(self.Ps[s])
      if sum_Ps_s > 0:
        self.Ps[s] /= sum_Ps_s  # Renormalize
      else:
        # If all valid moves were masked make all valid moves equally probable
        # NB! All valid moves may be masked if either your NNet architecture is insufficient or you've get overfitting or something else.
        # If you have got dozens or hundreds of these messages you should pay attention to your NNet and/or training process.
        log = logging.getLogger(__name__)
        log.error("All valid moves were masked, doing a workaround.")
        self.Ps[s] = self.Ps[s] + valids
        self.Ps[s] /= np.sum(self.Ps[s])

      self.Vs[s] = valids
      self.Ns[s] = 0

      return -v

    valids = self.Vs[s]
    cur_best = -float('inf')
    best_act = -1

    #################################################
    ## TODO for students:
    ## Implement the highest upper confidence bound depending whether we observed the state-action pair which is stored in self.Qsa[(s, a)]. You can find the formula in the slide 52 in video 8 above.
    # Fill out function and remove
    raise NotImplementedError("Complete the for loop")
    #################################################
    # Pick the action with the highest upper confidence bound
    for a in range(self.game.getActionSize()):
      if valids[a]:
        if (s, a) in self.Qsa:
          u = ... + ... * ... * math.sqrt(...) / (1 + ...)
        else:
          u = ... * ... * math.sqrt(... + 1e-8)

        if u > cur_best:
          cur_best = u
          best_act = a

    a = best_act
    next_s, next_player = self.game.getNextState(canonicalBoard, 1, a)
    next_s = self.game.getCanonicalForm(next_s, next_player)

    v = self.search(next_s)

    if (s, a) in self.Qsa:
      self.Qsa[(s, a)] = (self.Nsa[(s, a)] * self.Qsa[(s, a)] + v) / (self.Nsa[(s, a)] + 1)
      self.Nsa[(s, a)] += 1

    else:
      self.Qsa[(s, a)] = v
      self.Nsa[(s, a)] = 1

    self.Ns[s] += 1
    return -v

  def getNsa(self):
    return self.Nsa

Click for solution


Bonus 2: Use MCTS to play games

Time estimate: ~10mins

Goal: Teach the students how to use the results of MCTS to play games.

Exercise:

  • Plug the MCTS planner into an agent.

  • Play games against other agents.

  • Explore the contributions of prior network, value function, number of simulations/time to play and explore/exploit parameters.

Video 11: Play with MCTS

Bonus Coding Exercise 2: Agent that uses an MCTS planner

  • Plug the MCTS planner into an agent.

  • Play games against other agents.

  • Explore the contributions of prior network, value function, number of simulations/time to play and explore/exploit parameters.

# Load MCTS model from the repository
mcts_model_save_name = 'MCTS.pth.tar'
path = "nma_rl_games/alpha-zero/pretrained_models/models/"
class MonteCarloTreeSearchBasedPlayer():
  """
  Simulate Player based on MCTS
  """

  def __init__(self, game, nnet, args):
    """
    Initialize parameters of MCTS

    Args:
      game: OthelloGame instance
        Instance of the OthelloGame class above;
      nnet: OthelloNet instance
        Instance of the OthelloNNet class above;
      args: dictionary
        Instantiates number of iterations and episodes, controls temperature threshold, queue length,
        arena, checkpointing, and neural network parameters:
        learning-rate: 0.001, dropout: 0.3, epochs: 10, batch_size: 64,
        num_channels: 512

    Returns:
      Nothing
    """
    self.game = game
    self.nnet = nnet
    self.args = args
    self.mcts = MCTS(game, nnet, args)

  def play(self, canonicalBoard, temp=1):
    """
    Simulate Play on Canonical Board

    Args:
      canonicalBoard: np.ndarray
        Canonical Board of size n x n [6x6 in this case]
      temp: Integer
        Signifies if game is in terminal state

    Returns:
      List of probabilities for all actions if temp is 0
      Best action based on max probability otherwise
    """
    for i in range(self.args.numMCTSSims):

      #################################################
      ## TODO for students:
      #  Run MCTS search function.
      #  Fill out function and remove
      raise NotImplementedError("Plug the planner")
      #################################################
      ...

    s = self.game.stringRepresentation(canonicalBoard)
    #################################################
    ## TODO for students:
    #  Call the Nsa function from MCTS class and store it in the self.Nsa
    #  Fill out function and remove
    raise NotImplementedError("Compute Nsa (number of times edge s,a was visited)")
    #################################################
    self.Nsa = ...
    self.counts = [self.Nsa[(s, a)] if (s, a) in self.Nsa else 0 for a in range(self.game.getActionSize())]

    if temp == 0:
      bestAs = np.array(np.argwhere(self.counts == np.max(self.counts))).flatten()
      bestA = np.random.choice(bestAs)
      probs = [0] * len(self.counts)
      probs[bestA] = 1
      return probs

    self.counts = [x ** (1. / temp) for x in self.counts]
    self.counts_sum = float(sum(self.counts))
    probs = [x / self.counts_sum for x in self.counts]
    return np.argmax(probs)

  def getActionProb(self, canonicalBoard, temp=1):
    """
    Helper function to get probabilities associated with each action

    Args:
      canonicalBoard: np.ndarray
        Canonical Board of size n x n [6x6 in this case]
      temp: Integer
        Signifies if game is in terminal state

    Returns:
      action_probs: List
        Probability associated with corresponding action
    """
    action_probs = np.zeros((self.game.getActionSize()))
    best_action = self.play(canonicalBoard)
    action_probs[best_action] = 1

    return action_probs

set_seed(seed=SEED)
game = OthelloGame(6)
rp = RandomPlayer(game).play  # All players
num_games = 20  # Games
n1 = NNet(game)  # nnet players
n1.load_checkpoint(folder=path, filename=mcts_model_save_name)
args1 = dotdict({'numMCTSSims': 50, 'cpuct':1.0})

## Uncomment below to check your agent!
# print('\n******MCTS player versus random player******')
# mcts1 = MonteCarloTreeSearchBasedPlayer(game, n1, args1)
# n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))
# arena = Arena.Arena(n1p, rp, game, display=OthelloGame.display)
# MCTS_result = arena.playGames(num_games, verbose=False)
# print(f"\n\n{MCTS_result}")
# print(f"\nNumber of games won by player1 = {MCTS_result[0]}, "
#       f"number of games won by player2 = {MCTS_result[1]}, out of {num_games} games")
# win_rate_player1 = MCTS_result[0]/num_games
# print(f"\nWin rate for player1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Random seed 2021 has been set.

Click for solution

Number of games won by player1 = 19, num of games won by player2 = 1, out of 20 games

Win rate for player1 over 20 games: 95.0%

MCTS player against Value-based player

print('\n******MCTS player versus value-based player******')
set_seed(seed=SEED)
vp = ValueBasedPlayer(game, vnet).play  # Value-based player
arena = Arena.Arena(n1p, vp, game, display=OthelloGame.display)
MC_result = arena.playGames(num_games, verbose=False)
print(f"\n\n{MC_result}")
print(f"\nNumber of games won by player1 = {MC_result[0]}, "
      f"number of games won by player2 = {MC_result[1]}, out of {num_games} games")
win_rate_player1 = MC_result[0]/num_games
print(f"\nWin rate for player1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Number of games won by player1 = 14, number of games won by player2 = 6, out of 20 games

Win rate for player1 over 20 games: 70.0%

MCTS player against Policy-based player

print('\n******MCTS player versus policy-based player******')
set_seed(seed=SEED)
pp = PolicyBasedPlayer(game, pnet).play  # Policy-based player
arena = Arena.Arena(n1p, pp, game, display=OthelloGame.display)
MC_result = arena.playGames(num_games, verbose=False)
print(f"\n\n{MC_result}")
print(f"\nNumber of games won by player1 = {MC_result[0]}, "
      f"number of games won by player2 = {MC_result[1]}, out of {num_games} games")
win_rate_player1 = MC_result[0]/num_games
print(f"\nWin rate for player1 over {num_games} games: {round(win_rate_player1*100, 1)}%")
Number of games won by player1 = 20, number of games won by player2 = 0, out of 20 games

Win rate for player1 over 20 games: 100.0%