Week 5 content of chess bot
This week, we'll focus on implementing self-play training to enhance your chess engine's capabilities. By having your bot play against itself, it can iteratively improve without requiring extensive human gameplay data.
This week, we'll implement self-play training to enable your bot to:
- Learn from games played against itself
- Automatically tune evaluation parameters
- Discover strong move sequences and strategies
- Build experience with varied positions
- Track improvements over time
Self-play training is a technique where an AI plays against versions of itself to improve performance. This approach:
- Creates a closed feedback loop for continuous improvement
- Enables parameter optimization without manual tweaking
- Discovers new strategies automatically
- Has been fundamental to breakthroughs like AlphaZero and Leela Chess Zero
1. Parameter Tuning
- Adjust evaluation weights based on game outcomes
- Fine-tune search depths for different game phases
- Optimize time management strategies
2. Learning Systems
- Record successful positions and moves
- Build position databases based on winning games
- Develop pattern recognition for tactics and strategies
```python
import chess
import random
import time
import json
import copy
from board import ChessBoard
class SelfPlayTrainer:
def init(self, bot_constructor, games_per_iteration=50, iterations=10):
"""
Create a self-play trainer for chess bots
Args:
bot_constructor: Function that returns a new ChessBot instance
games_per_iteration: Number of games to play in each training iteration
iterations: Number of training iterations to run
"""
self.bot_constructor = bot_constructor
self.games_per_iteration = games_per_iteration
self.iterations = iterations
self.best_bot = bot_constructor()
self.performance_history = []
def train(self):
"""Run the complete self-play training process"""
for iteration in range(self.iterations):
print(f"Starting training iteration {iteration+1}/{self.iterations}")
# Create challenger bot with variations
challenger_bot = self.create_challenger()
# Play training games
results = self.play_match(self.best_bot, challenger_bot)
# Analyze results
analysis = self.analyze_results(results)
print(f"Iteration {iteration+1} results: {analysis}")
# Update the best bot if challenger performed better
self.update_best_bot(challenger_bot, analysis)
# Store performance metrics
self.performance_history.append({
"iteration": iteration + 1,
"timestamp": time.time(),
"metrics": analysis
})
def create_challenger(self):
"""Create a challenger bot with slightly modified parameters"""
challenger = self.bot_constructor()
# If our bot has tunable parameters, modify them slightly
if hasattr(challenger, 'piece_values'):
for piece in challenger.piece_values:
# Random adjustment between 95% and 105% of current value
challenger.piece_values[piece] *= random.uniform(0.95, 1.05)
if hasattr(challenger, 'position_scores'):
# Similar modifications for positional scores
for piece in challenger.position_scores:
for i in range(len(challenger.position_scores[piece])):
challenger.position_scores[piece][i] *= random.uniform(0.95, 1.05)
# Modify other parameters as needed
return challenger
def play_match(self, bot1, bot2):
"""Play a series of games between two bots"""
results = []
for game_num in range(self.games_per_iteration):
# Alternate colors for fairness
if game_num % 2 == 0:
white_bot, black_bot = bot1, bot2
color_map = {1: "bot1", -1: "bot2", 0: "draw"}
else:
white_bot, black_bot = bot2, bot1
color_map = {-1: "bot1", 1: "bot2", 0: "draw"}
# Play a game
board = ChessBoard()
moves = []
result_code = self.play_game(board, white_bot, black_bot, moves)
# Record result
winner = color_map[result_code]
results.append({
"game_num": game_num,
"winner": winner,
"moves": moves,
"result_code": result_code
})
if (game_num + 1) % 10 == 0:
print(f"Played {game_num + 1}/{self.games_per_iteration} games")
return results
def play_game(self, board, white_bot, black_bot, moves):
"""Play a single game between two bots, return the result code"""
move_count = 0
position_history = {}
while not board.is_game_over():
# Track positions for threefold repetition detection
board_key = board.get_fen().split(' ')[0] # Just piece positions
position_history[board_key] = position_history.get(board_key, 0) + 1
# Detect draws by repetition or 50-move rule
if position_history[board_key] >= 3 or board.halfmove_clock >= 100:
return 0 # Draw
# Get current bot's move
current_bot = white_bot if board.turn == chess.WHITE else black_bot
try:
move = current_bot.get_move(board)
if move:
moves.append(move)
board.make_move(move)
else:
# No legal moves (should be caught by is_game_over, but just in case)
break
except Exception as e:
print(f"Error during move calculation: {e}")
# Forfeit the game if an error occurs
return -1 if board.turn == chess.WHITE else 1
move_count += 1
# Implement a move limit to prevent infinite games
if move_count > 200:
return 0 # Draw by excessive moves
# Determine game result
if board.is_checkmate():
return -1 if board.turn == chess.WHITE else 1 # Winner is opposite of current turn
else:
return 0 # Draw by stalemate, insufficient material, etc.
def analyze_results(self, results):
"""Analyze the match results"""
bot1_wins = sum(1 for r in results if r["winner"] == "bot1")
bot2_wins = sum(1 for r in results if r["winner"] == "bot2")
draws = sum(1 for r in results if r["winner"] == "draw")
# Calculate performance metrics
total_games = len(results)
bot1_win_rate = bot1_wins / total_games
bot2_win_rate = bot2_wins / total_games
draw_rate = draws / total_games
# Calculate average game length
game_lengths = [len(r["moves"]) for r in results]
avg_game_length = sum(game_lengths) / len(game_lengths) if game_lengths else 0
return {
"bot1_wins": bot1_wins,
"bot2_wins": bot2_wins,
"draws": draws,
"bot1_win_rate": bot1_win_rate,
"bot2_win_rate": bot2_win_rate,
"draw_rate": draw_rate,
"avg_game_length": avg_game_length
}
def update_best_bot(self, challenger, analysis):
"""Update the best bot if the challenger performed better"""
# If challenger (bot2) won more than current best (bot1), adopt its parameters
if analysis["bot2_win_rate"] > analysis["bot1_win_rate"]:
print("Challenger performed better - updating best bot")
# Copy challenger parameters to best bot
if hasattr(challenger, 'piece_values') and hasattr(self.best_bot, 'piece_values'):
self.best_bot.piece_values = copy.deepcopy(challenger.piece_values)
if hasattr(challenger, 'position_scores') and hasattr(self.best_bot, 'position_scores'):
self.best_bot.position_scores = copy.deepcopy(challenger.position_scores)
# Copy other parameters as needed
def save_best_bot(self, filepath):
"""Save the best bot's parameters to a file"""
params = {}
# Save piece values if they exist
if hasattr(self.best_bot, 'piece_values'):
params['piece_values'] = self.best_bot.piece_values
# Save position scores if they exist
if hasattr(self.best_bot, 'position_scores'):
params['position_scores'] = self.best_bot.position_scores
# Save other parameters
with open(filepath, 'w') as f:
json.dump(params, f, indent=2)
def save_training_history(self, filepath):
"""Save the training history to a file"""
history = {
"performance_history": self.performance_history,
"game_history": self.game_history[-100:] # Save only the last 100 games to save space
}
with open(filepath, 'w') as f:
json.dump(history, f, indent=2)
```
## Integrating Self-Play with Your Chess Bot
Now let's enhance our ChessBot class to work with the self-play framework:
```python
import chess
import random
import copy
from board import ChessBoard
class ChessBot:
def init(self, depth=3):
"""Initialize your chess bot with tunable parameters"""
# Parameters that can be optimized through self-play
self.depth = depth
# Piece values (can be tuned during self-play)
self.piece_values = {
chess.PAWN: 100,
chess.KNIGHT: 320,
chess.BISHOP: 330,
chess.ROOK: 500,
chess.QUEEN: 900,
chess.KING: 20000
}
# Position bonuses for pieces (simplified, can be expanded)
self.position_scores = {
chess.PAWN: [
0, 0, 0, 0, 0, 0, 0, 0,
50, 50, 50, 50, 50, 50, 50, 50,
10, 10, 20, 30, 30, 20, 10, 10,
5, 5, 10, 25, 25, 10, 5, 5,
0, 0, 0, 20, 20, 0, 0, 0,
5, -5,-10, 0, 0,-10, -5, 5,
5, 10, 10,-20,-20, 10, 10, 5,
0, 0, 0, 0, 0, 0, 0, 0
],
chess.KNIGHT: [
-50,-40,-30,-30,-30,-30,-40,-50,
-40,-20, 0, 0, 0, 0,-20,-40,
-30, 0, 10, 15, 15, 10, 0,-30,
-30, 5, 15, 20, 20, 15, 5,-30,
-30, 0, 15, 20, 20, 15, 0,-30,
-30, 5, 10, 15, 15, 10, 5,-30,
-40,-20, 0, 5, 5, 0,-20,-40,
-50,-40,-30,-30,-30,-30,-40,-50
],
# Add more position scores for other pieces
}
# For storing opening book moves learned from self-play
self.opening_book = {}
# Transposition table for search efficiency
self.transposition_table = {}
def get_move(self, board: ChessBoard):
"""Given the current board state, returns the chosen move"""
legal_moves = board.get_legal_moves()
if not legal_moves:
return None
# Check opening book first
book_move = self.get_book_move(board)
if book_move and book_move in legal_moves:
return book_move
# If no book move, use minimax search
best_move = None
best_value = float('-inf')
alpha = float('-inf')
beta = float('inf')
for move in legal_moves:
# Try the move
board.make_move(move)
# Evaluate position after move
value = -self.minimax(board, self.depth - 1, -beta, -alpha, False)
# Undo the move
board.undo_move()
# Update best move if needed
if value > best_value:
best_value = value
best_move = move
# Update alpha for alpha-beta pruning
alpha = max(alpha, value)
return best_move
def minimax(self, board, depth, alpha, beta, maximizing_player):
"""Minimax search with alpha-beta pruning"""
# Check transposition table
board_hash = self.get_board_hash(board)
if board_hash in self.transposition_table and self.transposition_table[board_hash]['depth'] >= depth:
return self.transposition_table[board_hash]['value']
# Base case: reached leaf node or terminal position
if depth == 0 or board.is_game_over():
value = self.evaluate_position(board)
# Store in transposition table
self.transposition_table[board_hash] = {'value': value, 'depth': depth}
return value
legal_moves = board.get_legal_moves()
if maximizing_player:
value = float('-inf')
for move in legal_moves:
board.make_move(move)
value = max(value, self.minimax(board, depth - 1, alpha, beta, False))
board.undo_move()
alpha = max(alpha, value)
if alpha >= beta:
break # Beta cutoff
else:
value = float('inf')
for move in legal_moves:
board.make_move(move)
value = min(value, self.minimax(board, depth - 1, alpha, beta, True))
board.undo_move()
beta = min(beta, value)
if alpha >= beta:
break # Alpha cutoff
# Store in transposition table
self.transposition_table[board_hash] = {'value': value, 'depth': depth}
return value
def evaluate_position(self, board):
"""Evaluate the current board position"""
if board.is_checkmate():
# Checkmate is the worst possible outcome
return -10000 if board.turn == chess.WHITE else 10000
if board.is_stalemate() or board.is_insufficient_material():
return 0 # Draw
# Material count
material_score = 0
for square in range(64):
piece = board.piece_at(square)
if piece:
value = self.piece_values[piece.piece_type]
# Apply position bonus
if piece.piece_type in self.position_scores:
position_idx = square if piece.color == chess.WHITE else 63 - square
value += self.position_scores[piece.piece_type][position_idx] / 10
material_score += value if piece.color == chess.WHITE else -value
# Consider side to move
perspective = 1 if board.turn == chess.WHITE else -1
return material_score * perspective
def get_book_move(self, board):
"""Get a move from the opening book"""
fen = board.get_fen().split(' ')[0] # Just position part
if fen in self.opening_book:
# Select from available book moves based on weights
moves = self.opening_book[fen]
total_weight = sum(weight for _, weight in moves)
if total_weight <= 0:
return None
# Choose move based on weight
r = random.random() * total_weight
cumulative = 0
for move, weight in moves:
cumulative += weight
if r <= cumulative:
return move
return None
def update_opening_book(self, game_result, moves, color):
"""Update opening book based on game result"""
# Only update book from winning games or draws
if (game_result == 1 and color == chess.WHITE) or (game_result == -1 and color == chess.BLACK) or game_result == 0:
# Replay the first 10-15 moves of the game
board = ChessBoard()
for i, move in enumerate(moves[:15]): # Consider first 15 moves as opening
fen = board.get_fen().split(' ')[0]
# Add move to opening book
if fen not in self.opening_book:
self.opening_book[fen] = []
# Check if move exists in book
move_found = False
for j, (book_move, weight) in enumerate(self.opening_book[fen]):
if book_move == move:
# Update weight based on result
new_weight = weight + (5 if game_result != 0 else 2)
self.opening_book[fen][j] = (book_move, new_weight)
move_found = True
break
if not move_found:
# Add new move
self.opening_book[fen].append((move, 10 if game_result != 0 else 5))
# Apply the move to advance the board
board.make_move(move)
def get_board_hash(self, board):
"""Get a hash of the current board position"""
return board.get_fen().split(' ')[0] # Just position part
```
Once you have your bot class and self-play framework set up, you can run the training process:
```python
def main():
# Function to create fresh bot instances
def create_bot():
return ChessBot(depth=3)
# Create the self-play trainer
trainer = SelfPlayTrainer(
bot_constructor=create_bot,
games_per_iteration=50, # Play 50 games per iteration
iterations=10 # Run for 10 iterations
)
# Run the training
print("Starting self-play training...")
best_bot = trainer.train()
# Save the best bot's parameters
trainer.save_best_bot("best_bot_params.json")
trainer.save_training_history("training_history.json")
print("Training complete!")
return best_bot
if name == "__main__":
main()
```
1. Parameter Space Exploration
- Start with small variations to avoid diverging too much
- Gradually increase exploration as training progresses
- Focus on parameters with highest impact (piece values, positional scores)
2. Opening Book Development
- Start with empty book and build through successful games
- Weight moves by win percentage
- Maintain move diversity to avoid narrow repertoire
3. Computational Efficiency
- Use shorter games for early iterations (e.g., limit to 100 moves)
- Implement early termination in clearly won/lost positions
- Parallelize self-play games if possible
4. Learning from Failures
- Don't just keep winning strategies; analyze losses
- Identify common tactical mistakes
- Develop counter-strategies to previously successful approaches
1. Set up a basic self-play framework:
- Implement the SelfPlayTrainer class
- Create a bot class that stores tunable parameters
2. Implement parameter variation and tracking:
- Add code to create challenger bots with varied parameters
- Track which parameter sets perform best
3. Run initial self-play iterations:
- Start with small training runs (10-20 games)
- Verify that parameter updates are happening correctly
4. Add opening book learning:
- Record successful opening sequences
- Build a database of promising first moves
5. Analyze and visualize results:
- Track bot improvement over iterations
- Identify which parameters have the greatest impact
6. Optional advanced features:
- Tournament-style evaluation between bot variations
- Parameter gradient estimation
- Advanced position evaluation learning
- [Chess Programming Wiki - Texel Tuning](https://www.chessprogramming.org/Texel%27s_Tuning_Method)
- [Self-Play Reinforcement Learning](https://medium.com/applied-data-science/how-to-train-ai-agents-using-self-play-for-multiplayer-games-applications-for-chess-35d3b1b91993)
- [AlphaZero's Approach to Self-Play](https://arxiv.org/abs/1712.01815)
- [Evolutionary Algorithms for Parameter Tuning](https://www.chessprogramming.org/Automatic_Tuning)
Remember that self-play training requires patience, as improvement may be gradual. Start with small experiments and scale up as you confirm your approach is working. Next week, we'll explore endgame techniques and specialized evaluation to further enhance your chess bot's capabilities!
