def Evaluate_min_max(player, state):
board = make_matrix(state)
# material_adv
mapped_val = {
'0': 0,
'1': 1, # White Pawn
'-1': -1, # Black Pawn
'2': 3, # White Knight
'-2': -3, # Black Knight
'3': 3, # White Bishop
'-3': -3, # Black Bishop
'4': 5, # White Rook
'-4': -5, # Black Rook
'5': 9, # White Queen
'-5': -9, # Black Queen
'6': 0, # White King
'-6': 0 # Black King
}
pieces_adv = [mapped_val[str(x)] for x in list(itertools.chain(*board))]
material_adv = np.sum(pieces_adv)
if player == 'white':
return material_adv
else:
return -material_adv
return material_adv
def Evaluate_center_control(player, env, state):
mapped_val = {
'0': 0,
'1': 1, # White Pawn
'-1': -1, # Black Pawn
'2': 3, # White Knight
'-2': -3, # Black Knight
'3': 3, # White Bishop
'-3': -3, # Black Bishop
'4': 5, # White Rook
'-4': -5, # Black Rook
'5': 9, # White Queen
'-5': -9, # Black Queen
'6': 0, # White King
'-6': 0 # Black King
}
board = make_matrix(state)
#center control
space_list = list(itertools.chain(*board))
center_list = [
space_list[27], space_list[28], space_list[35], space_list[36]
]
center_list_val = [mapped_val[str(x)] for x in center_list]
center_control = np.sum(center_list_val)
#print("Center Control: ", center_control)
return center_control
def calculate_center_control(state):
board = make_matrix(state)
#center control
space_list = list(itertools.chain(*board))
center_list = [
space_list[27], space_list[28], space_list[35], space_list[36]
]
center_list_val = [mapped_val[str(x)] for x in center_list]
center_control = np.sum(center_list_val)
print("Center Control: ", center_control)
def calculate_space(state):
board = make_matrix(state)
#Space https://en.wikipedia.org/wiki/Chess_strategy#Space
#attacked
att_space = 0
#ocupied
black_space = list(itertools.chain(*board))[:32]
occ = [x if x > 0 else 0 for x in black_space]
#space
space = np.sum(occ) + att_space
print("Space: ", space)
return space
def Evaluate_development(player, state):
board = make_matrix(state)
#Space https://en.wikipedia.org/wiki/Chess_strategy#Space
#attacked
att_space = 0
#ocupied
if player == 'white':
white_space = list(itertools.chain(*board))[33:57]
occ = [x if x > 0 else 0 for x in black_space]
else:
black_space = list(itertools.chain(*board))[8:32]
occ = [x if x < 0 else 0 for x in white_space]
#space
space = abs(np.sum(occ)) + att_space
#print("Space: ", space)
return space
def calculate_material_adv(state):
board = make_matrix(state)
# material_adv
mapped_val = {
'0': 0,
'1': 1, # White Pawn
'-1': -1, # Black Pawn
'2': 3, # White Knight
'-2': -3, # Black Knight
'3': 3, # White Bishop
'-3': -3, # Black Bishop
'4': 5, # White Rook
'-4': -5, # Black Rook
'5': 9, # White Queen
'-5': -9, # Black Queen
'6': 0, # White King
'-6': 0 # Black King
}
pieces_adv = [mapped_val[str(x)] for x in list(itertools.chain(*board))]
material_adv = np.sum(pieces_adv)
#print('Material Adv: ', material_adv)
return material_adv
from chessRL import make_matrix
env = gym.make('Chess-v0')
print(env.render())
engine = chess.engine.SimpleEngine.popen_uci("../stockfish")
scores_eps = []
done = False
state = env.reset()
cnt = 0
while not done:
board_txt = state.fen().split()[0]
board_encoded = ''.join(str(ord(c)) for c in board_txt)
obs = make_matrix(state)
if cnt == 0:
move = "e4"
action = state.push_san(move)
state,reward,done,_ = env.step(action)
elif cnt % 2 == 0:
legal_moves = [state.san(x) for x in env.legal_moves]
legal_moves_scores = []
for move in legal_moves:
action = state.push_san(move) # Make the move
obs = make_matrix(state)
mapped_val = {
'0':0,
def get_moves_by_similarity(state, legal_moves):
df = pd.read_excel(
r'C:\Users\Octavio\Desktop\Projetos Python\Chess-RL\data\pgn\chess_deep_memory_excel.xlsx'
)
matr = df.loc[:, 's1':]
#env = gym.make('Chess-v0')
#state = env.reset()
#cnt = 0
#string = "e4 c5 Nc3 Nc6 Qf3 e5"
#string = "e4 e5 Nf3 Nc6 Bb5 Nf6 O-O Nxe4 Re1 Nd6 Nxe5 Be7 Bf1 Nxe5 Rxe5 O-O d4 Bf6 Re1 Re8 Bf4 Rxe1"
#game = string.split(' ')
#for move in game:
# #move = state.san(action)
# action = state.push_san(move)
#
# #take action
# state,reward,done,_ = env.step(action)
# cnt +=1
#print(env.render())
#print(state.status())
board_txt = state.fen().split()[0]
board_encoded = ''.join(str(ord(c)) for c in board_txt)
obs = make_matrix(state)
board_state = [list(np.reshape(obs, [1, 64])[0])]
print("Board state: ", board_state)
moves = []
#legal_moves = [state.san(x) for x in env.legal_moves] ##
#position = [0,0,0,0,0,0,0,0]
print('COSINE DISTANCE')
cos_dist = cdist(board_state, matr[:], metric='cosine')
#print(cos_dist)
#print(df.iloc[np.argmax(cos_dist),:].next_move)
df['cos_sim'] = cos_dist[0]
df = df.sort_values('cos_sim', ascending=False)
top3 = list(set(list(df[['cos_sim']].iloc[:, :].cos_sim)))[-1:]
print(top3)
#n_moves = list(df[['next_move']].iloc[:20,:].next_move)
#n_moves = [x for x in n_moves if x in legal_moves]
df2 = df[df['cos_sim'].isin(top3)]
n_moves = list(df2[['next_move']].iloc[:, :].next_move)
print(len(n_moves))
for mv in n_moves:
moves.append(mv)
#print(n_moves)
#print(df.head(10))
print(" ")
print('EUCLIDEAN DISTANCE')
euc_dist = cdist(board_state, matr[:], metric='euclidean')
#print(euc_dist)
#print(df.iloc[np.argmax(euc_dist),:].next_move)
df['euc_sim'] = euc_dist[0]
df = df.sort_values('euc_sim', ascending=False)
#print(df.head(10))
#n_moves = list(df[['next_move']].iloc[:20,:].next_move)
#n_moves = [x for x in n_moves if x in legal_moves]
top3 = list(set(list(df[['euc_sim']].iloc[:, :].euc_sim)))[-1:]
print(top3)
df2 = df[df['euc_sim'].isin(top3)]
n_moves = list(df2[['next_move']].iloc[:, :].next_move)
print(len(n_moves))
#print(n_moves)
for mv in n_moves:
moves.append(mv)
print(" ")
print('jaccard DISTANCE')
dist = cdist(board_state, matr[:], metric='jaccard')
#print(dist)
#print(df.iloc[np.argmax(dist),:].next_move)
df['jacc_sim'] = dist[0]
df = df.sort_values('jacc_sim', ascending=False)
#print(df.head(10))
#n_moves = list(df[['next_move']].iloc[:20,:].next_move)
#n_moves = [x for x in n_moves if x in legal_moves]
top3 = list(set(list(df[['jacc_sim']].iloc[:, :].jacc_sim)))[-1:]
print(top3)
df2 = df[df['jacc_sim'].isin(top3)]
n_moves = list(df2[['next_move']].iloc[:, :].next_move)
print(len(n_moves))
#print(n_moves)
for mv in n_moves:
moves.append(mv)
print(" ")
print('correlation DISTANCE')
dist = cdist(board_state, matr[:], metric='correlation')
#print(dist)
#print(df.iloc[np.argmax(dist),:].next_move)
df['corr_sim'] = dist[0]
df = df.sort_values('corr_sim', ascending=False)
#print(df.head(10))
#n_moves = list(df[['next_move']].iloc[:20,:].next_move)
#n_moves = [x for x in n_moves if x in legal_moves]
top3 = list(set(list(df[['corr_sim']].iloc[:, :].corr_sim)))[-1:]
print(top3)
df2 = df[df['corr_sim'].isin(top3)]
n_moves = list(df2[['next_move']].iloc[:, :].next_move)
print(len(n_moves))
#print(n_moves)
for mv in n_moves:
moves.append(mv)
print(" ")
print('chebyshev DISTANCE')
dist = cdist(board_state, matr[:], metric='chebyshev')
#print(dist)
#print(df.iloc[np.argmax(dist),:].next_move)
df['cheb_sim'] = dist[0]
df = df.sort_values('cheb_sim', ascending=False)
#print(df.head(10))
#n_moves = list(df[['next_move']].iloc[:20,:].next_move)
#n_moves = [x for x in n_moves if x in legal_moves]
top3 = list(set(list(df[['cheb_sim']].iloc[:, :].cheb_sim)))[-1:]
print(top3)
df2 = df[df['cheb_sim'].isin(top3)]
n_moves = list(df2[['next_move']].iloc[:, :].next_move)
print(len(n_moves))
#print(n_moves)
for mv in n_moves:
moves.append(mv)
'''print(" ")
print("King Position")
king_now = board_state[0].index(-6)
def get_king_pos(row, king_now):
list_pieces = list(row['s1':'s64'])
idx = list_pieces.index(-6)
if idx == king_now:
return 2
elif idx >= (king_now-1) and idx <= (king_now+1):
return 1
else:
return 0
df['king_pos'] = df.apply(lambda row: get_king_pos(row, king_now) , axis=1)
df = df.sort_values('king_pos', ascending=False)
#print(df.head(10))
#n_moves = list(df[['next_move']].iloc[:20,:].next_move)
#n_moves = [x for x in n_moves if x in legal_moves]
top3 = [2]
df2 = df[df['king_pos'].isin(top3)]
n_moves = list(df2[['next_move']].iloc[:,:].next_move)
print(len(n_moves))
#print(n_moves)
for mv in n_moves:
moves.append(mv)'''
moves = list(set(moves))
moves = [x for x in moves if x in legal_moves]
return moves
def act(cnt,df,seq_len, model, tokenizer, obs, legal_moves, engine, state, opening: str=None, idx_open: int=0, debug = False, choice = 2):
#start
print("Choice: ",choice)
if cnt == 0:
if opening == None:
#random opening
idx = random.choice([x for x in range(0,len(df['moves']))])
moves = df['moves'][idx].split()
move = moves[0]
else:
moves = df[df['opening_name'] == opening]['moves'].iloc[idx_open].split()
move = moves[0]
return move
else:
if choice == 4:
if opening != None:
if (cnt <= int(df[df['opening_name'] == opening]['opening_ply'].iloc[idx_open])):
moves = df[df['opening_name'] == opening]['moves'].iloc[idx_open].split()
move = moves[cnt]
print("Option 0 opening")
return move
else:
print("Option 0 Material Adv")
result = engine.play(state, chess.engine.Limit(time=0.1))
move = state.san(result.move)
legal_moves_scores = []
geral_scores = []
for move in legal_moves:
action = state.push_san(move) # Make the move
obs = make_matrix(state)
#material adv
pieces_adv = [mapped_val[str(x)] for x in list(itertools.chain(*obs))]
material_adv = np.sum(pieces_adv)
#print('Material Adv: ', material_adv)
legal_moves_scores.append(material_adv)
info = engine.analyse(state, chess.engine.Limit(time=0.1))
mate = info["score"].white().mate()
if mate != None:
if mate < 0:
score = -((20-(-mate)) * 100)
else:
score = (20-mate) * 100
else:
score = info["score"].white().score()
score = score if score != None else 0
geral_scores.append(score)
state.pop() # Unmake the last move
#do the best move
#print(legal_moves_scores)
a = min(legal_moves_scores)
b = max(legal_moves_scores)
c = min(geral_scores)
d = max(geral_scores)
n_legal_score = [(x-a)/(b-a) if (b-a) != 0 else 0 for x in legal_moves_scores]
n_geral_score = [(x-c)/(d-c) if (d-c) != 0 else 0 for x in geral_scores]
zipped_lists = zip(n_legal_score, n_geral_score)
final_score = [x + y for (x, y) in zipped_lists]
#move = legal_moves[legal_moves_scores.index(max(legal_moves_scores))]
move = legal_moves[final_score.index(max(final_score))]
return move
elif choice == 5:
df2 = df.copy()
df2['similarities'] = df2['moves'].apply(lambda x: similar(x.split(), obs))
df2 = df2.sort_values(by=['similarities'], ascending = False)
#moves = moves[cnt-1:]
for i in range(0,len(df2['moves'])):
moves = df2['moves'][i].split()
if debug:
print("Similarity of , ", df2['similarities'][i] * 100, "%")
print(obs[-1])
if obs[-1] in moves:
try:
if debug:
#similarity
print("Option 1 Similarity")
print("Next move: ",moves[moves.index(obs[-1]) + 1])
print("game: ",moves[moves.index(obs[-1])-2:moves.index(obs[-1])+2])
move = moves[moves.index(obs[-1]) + 1]
if move in legal_moves:
return move
#break
except:
#ML predict
print("Option 1 Machine Learning")
board = ' '.join([str(elem) for elem in obs])
next_moves = predict(seq_len,model, tokenizer, board)
move = next_moves
move = [x.capitalize() if len(x) > 2 else x for x in move]
for i in move:
if i in legal_moves:
move = i
return move
#break
else:
#ML predict
#print("Machine Learning")
print("Option 1 Machine Learning")
board = ' '.join([str(elem) for elem in obs])
next_moves = predict(seq_len,model, tokenizer, board)
move = next_moves
move = [x.capitalize() if len(x) > 2 else x for x in move]
for i in move:
if i in legal_moves:
move = i
return move
move = random.choice(legal_moves)
return move
elif choice == 3:
if opening != None:
if (cnt <= int(df[df['opening_name'] == opening]['opening_ply'].iloc[idx_open])):
moves = df[df['opening_name'] == opening]['moves'].iloc[idx_open].split()
move = moves[cnt]
print("Option 0 opening")
return move
else:
print("Option 2 Space")
result = engine.play(state, chess.engine.Limit(time=0.1))
move = state.san(result.move)
legal_moves_scores = []
geral_scores = []
for move in legal_moves:
action = state.push_san(move) # Make the move
obs = make_matrix(state)
#Space https://en.wikipedia.org/wiki/Chess_strategy#Space
#attacked
att_space = 0
#ocupied
black_space = list(itertools.chain(*obs))[:32]
occ = [x if x>0 else 0 for x in black_space]
#space
space = np.sum(occ) + att_space
#print("Space: ", space)
legal_moves_scores.append(space)
info = engine.analyse(state, chess.engine.Limit(time=0.1))
mate = info["score"].white().mate()
if mate != None:
if mate < 0:
score = -((20-(-mate)) * 100)
else:
score = (20-mate) * 100
else:
score = info["score"].white().score()
score = score if score != None else 0
geral_scores.append(score)
state.pop() # Unmake the last move
#do the best move
#print(legal_moves_scores)
a = min(legal_moves_scores)
b = max(legal_moves_scores)
c = min(geral_scores)
d = max(geral_scores)
n_legal_score = [(x-a)/(b-a) if (b-a) != 0 else 0 for x in legal_moves_scores]
n_geral_score = [(x-c)/(d-c) if (d-c) != 0 else 0 for x in geral_scores]
zipped_lists = zip(n_legal_score, n_geral_score)
final_score = [x + y for (x, y) in zipped_lists]
print("Final score: ",final_score)
print("Legal moves: ",legal_moves)
move = legal_moves[final_score.index(max(final_score))]
#move = legal_moves[legal_moves_scores.index(max(legal_moves_scores))]
return move
elif choice == 0: #stockfish backup
print("Option 0 - Exploration Stockfish")
result = engine.play(state, chess.engine.Limit(time=0.1))
move = state.san(result.move)
return move
elif choice == 1: #Komodo
print("Option 1 - Exploration Komodo")
result = engine.play(state, chess.engine.Limit(time=0.1))
move = state.san(result.move)
return move
elif choice == 2: #Komodo Ribka
print("Option 2 - Exploration Ribka")
result = engine.play(state, chess.engine.Limit(time=0.1))
move = state.san(result.move)
return move
except:
black_rating = 0
board = first_game.board()
# Iterate through all moves and play them on a board.
for move in first_game.mainline_moves():
#print(env.render())
print(" ")
print("NEXT MOVE: ", move, ',', board.san(move))
print(" ")
if first_game.is_end():
#state = env.reset()
break
obs = make_matrix(board)
#X.append(obs)
#y.append(board.san(move))
#print(board.san(move))
state = board
#action = state.push_san(move)
player = 'white'
print("Material Advantage")
move1 = calculate_min_max_tree(state,
env,
player,
depth=0,
mode='material_adv',
engine=engine)
