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-rw-r--r--dotsandboxes/agents/algorithms/MCTS.py151
-rw-r--r--dotsandboxes/agents/algorithms/alphaBeta.py105
-rw-r--r--dotsandboxes/agents/algorithms/ann.py170
3 files changed, 0 insertions, 426 deletions
diff --git a/dotsandboxes/agents/algorithms/MCTS.py b/dotsandboxes/agents/algorithms/MCTS.py
deleted file mode 100644
index 6c71ba9..0000000
--- a/dotsandboxes/agents/algorithms/MCTS.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import math
-from copy import deepcopy
-from time import perf_counter
-from random import choice
-
-from GameState import GameState
-
-# Based on https://github.com/DieterBuys/mcts-player/
-
-class GameController(object):
- def get_next_move(self, state):
- # when you get a new move, it is assumed that the game is not ended yet
- assert state.get_moves()
-
-
-class MCTSNode(object):
- """Monte Carlo Tree Node.
- Each node encapsulates a particular game state, the moves that
- are possible from that state and the strategic information accumulated
- by the tree search as it progressively samples the game space.
- """
-
- def __init__(self, state, parent=None, move=None):
- self.parent = parent
- self.move = move
- self.state = state
-
- self.plays = 0
- self.score = 0
-
- self.pending_moves = state.get_moves()
- self.children = []
-
- def select_child_ucb(self):
- # Note that each node's plays count is equal
- # to the sum of its children's plays
- def ucb(child):
- win_ratio = child.score / child.plays \
- + math.sqrt(2 * math.log(self.plays) / child.plays)
- return win_ratio
-
- return max(self.children, key=ucb)
-
- def expand_move(self, move):
- self.pending_moves.remove(move) # raises KeyError
-
- child_state = deepcopy(self.state)
- child_state.play_move(move)
-
- child = MCTSNode(state=child_state, parent=self, move=move)
- self.children.append(child)
- return child
-
- def get_score(self, result):
- # return result
- if result == 0.5:
- return result
-
- if self.state.player == 2:
- if self.state.next_turn_player == result:
- return 0.0
- else:
- return 1.0
- else:
- if self.state.next_turn_player == result:
- return 1.0
- else:
- return 0.0
-
- if self.state.next_turn_player == result:
- return 0.0
- else:
- return 1.0
-
- def __repr__(self):
- s = 'ROOT\n' if self.parent is None else ''
-
- children_moves = [c.move for c in self.children]
-
- s += """Score ratio: {score} / {plays}
-Pending moves: {pending_moves}
-Children's moves: {children_moves}
-State:
-{state}\n""".format(children_moves=children_moves, **self.__dict__)
-
- return s
-
-
-class MCTSGameController(GameController):
- """Game controller that uses MCTS to determine the next move.
- This is the class which implements the Monte Carlo Tree Search algorithm.
- It builds a game tree of MCTSNodes and samples the game space until a set
- time has elapsed.
- """
-
- def select(self):
- node = self.root_node
-
- # Descend until we find a node that has pending moves, or is terminal
- while node.pending_moves == set() and node.children != []:
- node = node.select_child_ucb()
-
- return node
-
- def expand(self, node):
- assert node.pending_moves != set()
-
- move = choice(tuple(node.pending_moves))
- return node.expand_move(move)
-
- def simulate(self, state, max_iterations=1000):
- state = deepcopy(state)
-
- move = state.get_random_move()
- while move is not None:
- state.play_move(move)
- move = state.get_random_move()
-
- max_iterations -= 1
- if max_iterations <= 0:
- return 0.5 # raise exception? (game too deep to simulate)
-
- return state.game_result
-
- def update(self, node, result):
- while node is not None:
- node.plays += 1
- node.score += node.get_score(result)
- node = node.parent
-
- def get_next_move(self, state, time_allowed=1.0):
- super(MCTSGameController, self).get_next_move(state)
-
- # Create new tree (TODO: Preserve some state for better performance?)
- self.root_node = MCTSNode(state)
- iterations = 0
-
- start_time = perf_counter()
- while perf_counter() < start_time + time_allowed:
- node = self.select()
-
- if node.pending_moves != set():
- node = self.expand(node)
-
- result = self.simulate(node.state)
- self.update(node, result)
-
- iterations += 1
-
- # Return most visited node's move
- return max(self.root_node.children, key=lambda n: n.plays).move
diff --git a/dotsandboxes/agents/algorithms/alphaBeta.py b/dotsandboxes/agents/algorithms/alphaBeta.py
deleted file mode 100644
index 8e041fe..0000000
--- a/dotsandboxes/agents/algorithms/alphaBeta.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from GameState import GameState
-class GameController(object):
- def get_next_move(self, state):
- # when you get a new move, it is assumed that the game is not ended yet
- assert state.get_moves()
-
-
-def alpha_beta(node, alpha, beta):
-
- # Based on https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#Pseudocode
- # node needs to support three operations: isTerminal(), value(), getChildren(), maximizingPlayer()
-
- if node.isTerminal():
- return node.value()
-
- if node.maximizingPlayer():
-
- v = float("-inf")
- for child in node.getChildren():
-
- v = max(v, alpha_beta(child, alpha, beta))
- alpha = max(alpha, v)
- if beta <= alpha:
- break
-
- else:
-
- v = float("inf")
- for child in node.getChildren():
-
- v = min(v, alpha_beta(child, alpha, beta))
- beta = min(beta, v)
- if beta <= alpha:
- break
-
- return v
-
-
-# A class for defining algorithms used (minimax and alpha-beta pruning)
-class AlphaBeta:
-
- def miniMax(State, Ply_num): # Function for the minimax algorithm
-
- for i in range(State.Current.dimY):
- for j in range(State.Current.dimX):
- if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
- State.Make(j, i, True)
- if Ply_num < 2:
- return (i, j)
-
- Minimum_Score = 1000
- i = 0
-
- j = 0
- for k, z in State.children.items():
- Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
- if Minimum_Score > Result:
- Minimum_Score = Result
- i = k[0]
- j = k[1]
-
- return (i, j)
-
- # Alpha-beta pruning function for taking care of Alpha values
- def Maximum(State, Ply_num, Alpha):
- if Ply_num == 0:
- return State.CurrentScore
-
- for i in range(State.Current.dimY):
- for j in range(State.Current.dimX):
- if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
- State.Make(j, i, False)
-
- Maximum_Score = -1000
- i = 0
- j = 0
- for k, z in State.children.items():
- Result = Algo.Minimum(z, Ply_num - 1, Maximum_Score)
- if Maximum_Score < Result:
- Maximum_Score = Result
- if Result > Alpha:
- return Result
-
- return Maximum_Score
-
- def Minimum(State, Ply_num, Beta): # Alpha-beta pruning function for taking care of Beta values
- if Ply_num == 0:
- return State.CurrentScore
-
- for i in range(State.Current.dimY):
- for j in range(State.Current.dimX):
- if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
- State.Make(j, i, True)
-
- Minimum_Score = 1000
- i = 0
- j = 0
- for k, z in State.children.items():
- Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
- if Minimum_Score > Result:
- Minimum_Score = Result
- if Result < Beta:
- return Result
-
- return Minimum_Score
diff --git a/dotsandboxes/agents/algorithms/ann.py b/dotsandboxes/agents/algorithms/ann.py
deleted file mode 100644
index 05ae647..0000000
--- a/dotsandboxes/agents/algorithms/ann.py
+++ /dev/null
@@ -1,170 +0,0 @@
-from numpy import *
-from math import sqrt
-from copy import deepcopy
-from time import time
-
-class ANN:
-
- """ANN with one hidden layer, one output and full connections in between consecutive layers.
- Initial weights are chosen from a normal distribution.
- Activation function is tanh."""
-
- INIT_SIGMA = 0.02
- REL_STOP_MARGIN = 0.01
- MAX_ITERATIONS = 1000000
- ACTIVATION = tanh
- D_ACTIVATION = lambda x: 1 - tanh(x)**2 # Derivative of tanh
- VEC_ACTIVATION = vectorize(ACTIVATION)
- VEC_D_ACTIVATION = vectorize(D_ACTIVATION)
- STEP_SIZE = 0.1
-
- def __init__(self, input_size, hidden_size):
-
- #self.input_size = input_size
- #self.hidden_size = hidden_size
- self.hidden_weights = random.normal(0, ANN.INIT_SIGMA, (hidden_size, input_size))
- self.output_weights = random.normal(0, ANN.INIT_SIGMA, hidden_size)
-
- def get_weights(self):
- return self.hidden_weights, self.output_weights
-
- def predict(self, input_vector):
-
- # Predicts the output for this input vector
- # input_vector will be normalized
-
- input_vector = input_vector/linalg.norm(input_vector)
- return ANN.ACTIVATION(dot(self.output_weights, ANN.VEC_ACTIVATION(dot(self.hidden_weights, input_vector))))
-
- @staticmethod
- def frob_norm(a, b):
-
- # Calculates the total Frobenius norm of both matrices A and B
- return sqrt(linalg.norm(a)**2 + linalg.norm(b)**2)
-
- def train(self, examples):
-
- #print("Training")
- start = time()
-
- # examples is a list of (input, output)-tuples
- # input will be normalized
- # We stop when the weights have converged within some relative margin
-
- for example in examples:
- example[0] = example[0]/linalg.norm(example[0])
-
- iteration = 0
- while True:
-
-
- # Store old weights to check for convergence later
- prev_hidden_weights = deepcopy(self.hidden_weights)
- prev_output_weights = deepcopy(self.output_weights)
-
- for k in range(len(examples)):
-
- input_vector, output = examples[k]
-
- # Calculate outputs
- hidden_input = dot(self.hidden_weights, input_vector)
- hidden_output = ANN.VEC_ACTIVATION(hidden_input)
- final_input = dot(self.output_weights, hidden_output)
- predicted_output = ANN.ACTIVATION(final_input)
-
- #print("Output:", output)
- #print("Predicted output:", predicted_output)
-
- # Used in calculations
- prediction_error = output - predicted_output
- output_derivative = ANN.D_ACTIVATION(final_input)
-
- # Adjust output weights and calculate requested hidden change
- requested_hidden_change = prediction_error*output_derivative*self.output_weights
- self.output_weights = self.output_weights + ANN.STEP_SIZE*prediction_error*hidden_output
-
- #print("After adjusting output weights:", ANN.ACTIVATION(dot(self.output_weights, hidden_output)))
-
- # Backpropagate requested hidden change to adjust hidden weights
- self.hidden_weights = self.hidden_weights + ANN.STEP_SIZE*outer(requested_hidden_change*(ANN.VEC_D_ACTIVATION(hidden_input)), input_vector)
-
- #print("After adjusting hidden weights:", ANN.ACTIVATION(dot(self.output_weights, ANN.VEC_ACTIVATION(dot(self.hidden_weights, input_vector)))))
-
- # Check stop criteria
- iteration += 1
- if iteration >= ANN.MAX_ITERATIONS:
- break
-
- # Check stop criteria
- if iteration >= ANN.MAX_ITERATIONS:
- break
- diff = ANN.frob_norm(self.hidden_weights - prev_hidden_weights, self.output_weights - prev_output_weights)
- base = ANN.frob_norm(self.hidden_weights, self.output_weights)
- #if base > 0 and diff/base < ANN.REL_STOP_MARGIN:
- # break
-
- print(time() - start)
- print("Stopped training after %s iterations."%iteration)
-
-# TESTING
-
-def print_difference(ann1, ann2):
-
- # Prints the differences in weights in between two ANN's with identical topology
-
- hidden_weights1, output_weights1 = ann1.get_weights()
- hidden_weights2, output_weights2 = ann2.get_weights()
- hidden_diff = hidden_weights1 - hidden_weights2
- output_diff = output_weights1 - output_weights2
-
- print(hidden_diff)
- print(output_diff)
- print("Frobenius norms:")
- print("Hidden weights difference:", linalg.norm(hidden_diff))
- print("Output weights difference:", linalg.norm(output_diff))
- print("Both:", ANN.frob_norm(hidden_diff, output_diff))
-
-def RMSE(ann, examples):
-
- total = 0
- for input_vector, output in examples:
- total += (output - ann.predict(input_vector))**2
- return sqrt(total/len(examples))
-
-def generate_examples(amount, input_size, evaluate):
- # evaluate is a function mapping an input vector onto a numerical value
- examples = []
- inputs = random.normal(0, 100, (amount, input_size))
- for i in range(amount):
- input_vector = inputs[i]
- examples.append([input_vector, evaluate(input_vector)])
- return examples
-
-def test():
-
- # Test the ANN by having it model another ANN with identical topology but unknown weights
-
- input_size = 5
- hidden_size = 3
- real = ANN(input_size, hidden_size)
- model = ANN(input_size, hidden_size)
-
- # Generate training data
- training_data = generate_examples(10000, input_size, real.predict)
- validation_data = generate_examples(10000, input_size, real.predict)
-
- # Print initial difference, train, then print new difference
- print("Initial difference:")
- print_difference(real, model)
- print("Initial RMSE (on training data):", RMSE(model, training_data))
- print("Initial RMSE (on validation data):", RMSE(model, validation_data))
- model.train(training_data)
- print("After training:")
- print_difference(real, model)
- print("After training RMSE (on training data):", RMSE(model, training_data))
- print("After training RMSE (on validation data):", RMSE(model, validation_data))
-
-if __name__ == "__main__":
- test()
-
-
generated by cgit v1.2.3 (git 2.46.0) at 2024-11-24 17:31:04 +0000