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main.py
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main.py
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from players import player
from hex_skeleton import HexBoard
from trueskill import Rating, quality_1vs1, rate_1vs1
import numpy as np
import searcher
import matplotlib.pyplot as plt
player1_ratings = Rating()
player2_ratings = Rating()
def play(player1, player2, board,verbose = True):
global player1_ratings
global player2_ratings
turn = 0 # turn counter
# while the game is not finished
while not board.game_over:
turn += 1 # increase turn after a round of the game
searcher.d3_rt_lst.append(searcher.d3_run_time)
searcher.d4_rt_lst.append(searcher.d4_run_time)
searcher.d3_run_time = 0 # and reset run time
searcher.d4_run_time = 0
if verbose == True:
print("turn:{}".format(turn))
print("Player 1:{}[{}]".format(player1.playerType,player1.policy))
board.print()
player1.move(board,verbose=verbose) # player_1 moves
# if player _1 won game then :
if board.check_win(player1.color):
if verbose == True:
board.print()
print("The Game is Over. Player 1: {}[{}] won the game".format(player1.agent,
player1.policy))
player1_ratings,player2_ratings = rate_1vs1(player1_ratings, player2_ratings)
break
if verbose == True:
print("Player 2:{}[{}]".format(player2.agent, player2.policy))
board.print()
player2.move(board,verbose=verbose) # player_2 moves
# if player_2 won game then :
if board.check_win(player2.color):
if verbose == True:
board.print()
print("The Game is Over. Player 2: {}[{}] won the game".format(player2.agent,
player2.policy))
player2_ratings, player1_ratings = rate_1vs1(player2_ratings,player1_ratings)
break
def reset_counter():
searcher.d3_total_cutoff = 0
searcher.d4_total_cutoff = 0
searcher.d3_beta_cutoff = 0
searcher.d3_alpha_cutoff = 0
searcher.d4_beta_cutoff = 0
searcher.d4_alpha_cutoff = 0
#------1. Alpha Beta Experiments----------------------------------------------
# since no pie rule implemented, 1st player has advantage, so order matters
# therefore agents needs to be initalized in reversed play starting order as well
# Agents Matchup 1: [depth = 3, policy = random] vs. [depth = 3, policy = dijkstra]
# Agents Matchup 2: [depth = 3, policy = random] vs. [depth = 4, policy = dijkstra]
# Agents Matchup 3: [depth = 3, policy = dijkstra] vs. [depth = 4, policy = dijkstra]
print("[Initalized Player Matchup: Experiment 1]")
print("[---------------------------------------------------------]")
res_lst_1 = []
res_elo_1 = []
res_elo_2 = []
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
n_games = 25 # number of games
size = 5 # board size
board = HexBoard(size) # init board
player_1_lst = [
player(agent = 'AI',
color = HexBoard.BLUE,
policy = "random"),
player(agent = 'AI',
color = HexBoard.BLUE,
policy = "random"),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "alphabeta",
eval = "dijkstra",
depth = 3)
]
player_2_lst = [
player(agent = "AI",
color = HexBoard.BLUE,
policy = "alphabeta",
eval = "dijkstra",
depth = 3),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "alphabeta",
eval = "dijkstra",
depth = 4, switch = True),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "alphabeta",
eval = "dijkstra",
depth = 4, switch = True)
]
print("[Started - Experiment 1: Model Comparison Alpha Beta . . .]")
print("[---------------------------------------------------------]")
for i in range(len(player_1_lst)):
player_1 = player_1_lst[i]
player_2 = player_2_lst[i]
for j in range(n_games):
board = HexBoard(size)
play(player_1, player_2, board, verbose = False)
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
print(player1_ratings)
print(player2_ratings)
player1_ratings = Rating()
player2_ratings = Rating()
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
d3_avg_cutoff = searcher.d3_total_cutoff // n_games
d3_avg_beta = searcher.d3_beta_cutoff // n_games
d3_avg_alpha = searcher.d3_alpha_cutoff // n_games
d4_avg_cutoff = searcher.d4_total_cutoff // n_games
d4_avg_beta = searcher.d4_beta_cutoff // n_games
d4_avg_alpha = searcher.d4_alpha_cutoff // n_games
res_lst_1.extend([d3_avg_alpha, d3_avg_beta,d3_avg_cutoff ,
d4_avg_alpha, d4_avg_beta, d4_avg_cutoff ])
reset_counter()
d3_avg_cutoff = 0
d4_avg_cutoff = 0
d3_avg_beta = 0
d3_avg_alpha = 0
d4_avg_beta = 0
d4_avg_alpha = 0
# plot runtime
# set n_games = 1 and plot speed per turn and comment out first 2 player
# matchups ins player 1 and player 2 and run for each size in turn
#searcher.d3_rt_lst = [i for i in searcher.d3_rt_lst if i != 0]
#searcher.d4_rt_lst = [i for i in searcher.d4_rt_lst if i != 0]
# plt.figure(figsize=(8, 6))
# plt.plot(np.array(searcher.d3_rt_lst), linewidth=3)
# plt.plot(np.array(searcher.d4_rt_lst), linewidth=3)
# plt.xticks(np.arange(len(searcher.d3_rt_lst)), np.arange(1, len(searcher.d3_rt_lst)+1))
# plt.title('Runtime per Turn: depth 3 vs. depth 4', fontsize = 20)
# plt.xlabel('Turn', fontsize = 18)
# plt.ylabel('Time in Seconds', fontsize = 18)
# plt.legend(['p1: depth 3 ', 'p2: depth 4'],
# prop={'size': 18}, frameon=False)
# plt.show()
# searcher.d3_rt_lst.clear()
# searcher.d4_rt_lst.clear()
# Plot Cutoffs
label = ['M1: alpha beta d=3', 'M2: alpha beta d=4', 'M3:alpha beta d=3',
'M3: alpha beta d=4']
data = [
res_lst_1[0:3],
res_lst_1[9:12],
res_lst_1[12:15],
res_lst_1[15:18]
]
numpy_array = np.array(data)
transpose = numpy_array.T
transpose_list = transpose.tolist()
X = np.arange(4)
fig = plt.figure(figsize=(8, 6))
ax = fig.add_axes([0,0,1,1])
ax.bar(X + - 0.25, transpose_list[0], color = 'b', width = 0.25)
ax.bar(X + 0.00, transpose_list[1], color = 'g', width = 0.25)
ax.bar(X + 0.25, transpose_list[2], color = 'r', width = 0.25)
plt.xticks(X, label)
ax.set_xticklabels(label, rotation=0, fontsize=12, fontweight='bold')
ax.legend(['Alpha', 'Beta' , 'Total'], prop={'size': 20}, frameon=False)
plt.title('Histogram: Averaged alpha beta cutoffs', fontsize = 22)
plt.xlabel('Type of agent', fontsize = 20)
plt.ylabel('Number of cutoffs', fontsize = 20)
plt.show()
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[0:26], linewidth=3)
plt.plot(res_elo_2[0:26], linewidth=3)
plt.title('Random policy vs. Alpha beta depth 3', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: random ', 'p2: alpha beta'],
prop={'size': 18}, frameon=False)
plt.show()
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[26:52], linewidth=3)
plt.plot(res_elo_2[26:52], linewidth=3)
plt.title('Random policy vs. Alpha beta depth 4', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: random ', 'p2: alpha beta'],
prop={'size': 18}, frameon=False)
plt.show()
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[52:78], linewidth=3)
plt.plot(res_elo_2[52:78], linewidth=3)
plt.title('Alpha beta depth 3 vs. Alpha beta depth 4', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: depth 3 ', 'p2: depth 4'],
prop={'size': 18}, frameon=False)
plt.show()
print("[Initalized Reverse-Player Matchup: Experiment 1]")
# reverse player 1 and player 2 since order matters for outcome
player1_ratings = Rating()
player2_ratings = Rating()
res_elo_1.clear()
res_elo_2.clear()
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
player_1_lst = [
player(agent = "AI",
color = HexBoard.BLUE,
policy = "alphabeta",
eval = "dijkstra",
depth = 3),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "alphabeta",
eval = "dijkstra",
depth = 4, switch = True),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "alphabeta",
eval = "dijkstra",
depth = 4, switch = True)
]
player_2_lst = [
player(agent = 'AI',
color = HexBoard.BLUE,
policy = "random"),
player(agent = 'AI',
color = HexBoard.BLUE,
policy = "random"),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "alphabeta",
eval = "dijkstra",
depth = 3)
]
print("[Started - Experiment 1: Reverse Player Order Model Comparison Alpha Beta . . .]")
print("[---------------------------------------------------------]")
for i in range(len(player_1_lst)):
player_1 = player_1_lst[i]
player_2 = player_2_lst[i]
for j in range(n_games):
board = HexBoard(size)
play(player_1, player_2, board, verbose = False)
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
d3_avg_cutoff = searcher.d3_total_cutoff // n_games
d3_avg_beta = searcher.d3_beta_cutoff // n_games
d3_avg_alpha = searcher.d3_alpha_cutoff // n_games
d4_avg_cutoff = searcher.d4_total_cutoff // n_games
d4_avg_beta = searcher.d4_beta_cutoff // n_games
d4_avg_alpha = searcher.d4_alpha_cutoff // n_games
res_lst_1.extend([d3_avg_alpha, d3_avg_beta,d3_avg_cutoff ,
d4_avg_alpha, d4_avg_beta, d4_avg_cutoff ])
reset_counter()
d3_avg_cutoff = 0
d4_avg_cutoff = 0
d3_avg_beta = 0
d3_avg_alpha = 0
d4_avg_beta = 0
d4_avg_alpha = 0
print(player1_ratings)
print(player2_ratings)
player1_ratings = Rating()
player2_ratings = Rating()
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[0:26], linewidth=3)
plt.plot(res_elo_2[0:26], linewidth=3)
plt.title('Alpha beta depth 3 vs. Random policy', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: alpha beta ', 'p2: random'],
prop={'size': 18}, frameon=False)
plt.show()
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[26:52], linewidth=3)
plt.plot(res_elo_2[26:52], linewidth=3)
plt.title('Alpha beta depth 4 vs. Random policy ', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 15)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: alpha beta ', 'p2: random'],
prop={'size': 18}, frameon=False)
plt.show()
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[52:78], linewidth=3)
plt.plot(res_elo_2[52:78], linewidth=3)
plt.title(' Alpha beta depth 4 vs. Alpha beta depth 3', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: depth 4 ', 'p2: depth 3'],
prop={'size': 18}, frameon=False)
plt.show()
print("[Completed - Experiment 1: Model Comparison Alpha Beta.]")
print("[---------------------------------------------------------]")
#------2. Iterative Deepening and Transposition Table Experiments-------------
# Same Matchup as before but with ITDD should perform similar
print("[Initalized Player Matchup: Experiment 2]")
print("[---------------------------------------------------------]")
n_games = 25 # number of games
size = 5 # board size
board = HexBoard(size) # init board
player1_ratings = Rating()
player2_ratings = Rating()
res_elo_1 = []
res_elo_2 = []
player1_ratings = Rating()
player2_ratings = Rating()
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
player_1_lst = [
player(agent = 'AI',
color = HexBoard.BLUE,
policy = "random"),
player(agent = 'AI',
color = HexBoard.BLUE,
policy = "random"),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "iterdeep",
eval = "dijkstra",
depth = 3, use_tt=True)
]
player_2_lst = [
player(agent = "AI",
color = HexBoard.BLUE,
policy = "iterdeep",
eval = "dijkstra",
depth = 3, use_tt = True),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "iterdeep",
eval = "dijkstra",
depth = 4, use_tt = True,
switch = True),
player(agent = "AI",
color = HexBoard.RED,
policy = "iterdeep",
eval = "dijkstra",
depth = 4, use_tt = True,
switch = True)
]
print("[Started - Experiment 2: Model Comparison Table Enhancements . . .]")
print("[---------------------------------------------------------]")
for i in range(len(player_1_lst)):
player_1 = player_1_lst[i]
player_2 = player_2_lst[i]
for j in range(n_games):
board = HexBoard(size)
play(player_1, player_2, board, verbose = False)
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
print(player1_ratings)
print(player2_ratings)
player1_ratings = Rating()
player2_ratings = Rating()
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[0:26], linewidth=3)
plt.plot(res_elo_2[0:26], linewidth=3)
plt.title('Random policy vs. IDTT depth 3', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: random ', 'p2: alpha beta'],
prop={'size': 18}, frameon=False)
plt.show()
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[26:52], linewidth=3)
plt.plot(res_elo_2[26:52], linewidth=3)
plt.title('Random policy vs. IDDT depth 4', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: random ', 'p2: alpha beta'],
prop={'size': 18}, frameon=False)
plt.show()
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[52:78], linewidth=3)
plt.plot(res_elo_2[52:78], linewidth=3)
plt.title(' IDTT depth 3 vs. IDTT depth 4', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: depth 3 ', 'p2: depth 4'],
prop={'size': 18}, frameon=False)
plt.show()
print("[Started - Experiment 2: Reverse Player Order Model Comparison Table Enhancements . . .]")
print("[---------------------------------------------------------]")
# reverse player 1 and player 2 since order matters for outcome
for i in range(len(player_1_lst)):
player_1 = player_2_lst[i]
player_2 = player_1_lst[i]
for k in range(n_games):
board = HexBoard(size)
play(player_1, player_2, board, verbose = False)
print(player1_ratings)
print(player2_ratings)
print("[Completed - Experiment 2: Model Comparison Table Enhancements.]")
print("[---------------------------------------------------------]")
#------3. Monte Carlo Tree Search (MCTS)--------------------------------------
# As such the match up list is:
# Agents Matchup 1: [depth = 4, policy = idtt] vs. [policy = mcts, N=iter_max=100, C= sqrt(2)]
# Agents Matchup 2: [depth = 4, policy = idtt] vs. [policy = mcts, N=iter_max=1, C= sqrt(2)]
# Agents Matchup 3: [depth = 4, policy = idtt] vs. [policy = mcts, N=iter_max=1000, C = 1000]
print("[Started - Experiment 3: Model Comparison Monte Carlo Tree Search . . .]")
print("[---------------------------------------------------------]")
n_games = 25 # number of games
size = 5 # board size
board = HexBoard(size) # init board
res_elo_1 = []
res_elo_2 = []
player1_ratings = Rating()
player2_ratings = Rating()
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
player_1_lst = [
player(agent = 'AI',
color = HexBoard.BLUE,
policy = "iterdeep",
depth = 4, use_tt = True),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "iterdeep",
eval = "dijkstra",
depth = 4, use_tt = True),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "iterdeep",
eval = "dijkstra",
depth = 4, use_tt = True)
]
player_2_lst = [
player(agent = "AI",
color = HexBoard.BLUE,
policy = "mcts",
max_iter = 2500,
C = np.sqrt(2)),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "mcts",
max_iter = 1,
C = np.sqrt(2)),
player(agent = "AI",
color = HexBoard.BLUE,
policy = "mcts",
max_iter = 2500,
C = 1000),
]
for i in range(len(player_1_lst)):
player_1 = player_1_lst[i]
player_2 = player_2_lst[i]
for j in range(n_games):
board = HexBoard(size)
play(player_1, player_2, board, verbose = False)
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
print(player1_ratings)
print(player2_ratings)
player1_ratings = Rating()
player2_ratings = Rating()
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[0:26], linewidth=3)
plt.plot(res_elo_2[0:26], linewidth=3)
plt.title('IDTT depth 4 vs. MCTS N = 2500, C=' r'$\sqrt{2}$', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: IDTT', 'p2: MCTS'],
prop={'size': 18}, frameon=False)
plt.show()
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[26:52], linewidth=3)
plt.plot(res_elo_2[26:52], linewidth=3)
plt.title('IDTT depth 4 vs. MCTS N = 1, C=' r'$\sqrt{2}$', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: IDTT ', 'p2: MCTS'],
prop={'size': 18}, frameon=False)
plt.show()
plt.figure(figsize=(8, 6))
plt.plot(res_elo_1[52:78], linewidth=3)
plt.plot(res_elo_2[52:78], linewidth=3)
plt.title('IDTT depth 4 vs. MCTS N = 2500, C= 1000', fontsize = 20)
plt.xlabel('Number of Games', fontsize = 18)
plt.ylabel('Mean Elo', fontsize = 18)
plt.legend(['p1: IDTT ', 'p2: MCTS'],
prop={'size': 18}, frameon=False)
plt.show()