diff --git a/week4/material/MCTS_functions.py b/week4/material/MCTS_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb86b7dcfd99677838ad28e6551641eace55435c
--- /dev/null
+++ b/week4/material/MCTS_functions.py
@@ -0,0 +1,13 @@
+import random
+import time
+
+from tictactoe_game_environment import TicTacToeGameEnvironment as gm
+
+def selection_policy(node, target_player):
+    pass
+
+def random_playout(initial_node):
+    pass
+
+def mcts(root_node, target_player, max_time=1):
+    pass
\ No newline at end of file
diff --git a/week4/material/agent_programs.py b/week4/material/agent_programs.py
new file mode 100644
index 0000000000000000000000000000000000000000..d98fa76b5af420c59eaea32c458c592b4e14c7cb
--- /dev/null
+++ b/week4/material/agent_programs.py
@@ -0,0 +1,104 @@
+import random
+import time
+
+from une_ai.models import GraphNode, MCTSGraphNode
+
+from tictactoe_game_environment import TicTacToeGameEnvironment
+from minimax_functions import minimax, minimax_alpha_beta, optimised_minimax
+from MCTS_functions import mcts
+
+def agent_program_random(percepts, actuators):
+    try:
+        game_board = percepts['game-board-sensor']
+        player_turn = percepts['turn-taking-indicator']
+        game_state = {
+            'game-board': game_board.copy(),
+            'player-turn': player_turn
+        }
+    except KeyError:
+        print("Error. You may have not fully implemented yet the class TicTacToeGameEnvironment")
+        return []
+    
+    legal_moves = TicTacToeGameEnvironment.get_legal_actions(game_state)
+    if len(legal_moves) > 0:
+        return [random.choice(legal_moves)]
+    
+    return []
+
+def agent_program_minimax(percepts, actuators, max_depth=4):
+    game_board = percepts['game-board-sensor']
+    player_turn = percepts['turn-taking-indicator']
+    game_state = {
+        'game-board': game_board.copy(),
+        'player-turn': player_turn
+    }
+
+    
+    if not TicTacToeGameEnvironment.is_terminal(game_state):
+        state_node = GraphNode(game_state, None, None, 0)
+        tic = time.time()
+        _, best_move = minimax(state_node, player_turn, max_depth)
+        toc = time.time()
+        print("[Minimax (player {0})] Elapsed (sec): {1:.6f}".format(player_turn, toc-tic))
+        if best_move is not None:
+            return [best_move]
+    
+    return []
+
+def agent_program_minimax_alpha_beta(percepts, actuators, max_depth=4):
+    game_board = percepts['game-board-sensor']
+    player_turn = percepts['turn-taking-indicator']
+    game_state = {
+        'game-board': game_board.copy(),
+        'player-turn': player_turn
+    }
+    
+    if not TicTacToeGameEnvironment.is_terminal(game_state):
+        state_node = GraphNode(game_state, None, None, 0)
+        tic = time.time()
+        _, best_move = minimax_alpha_beta(state_node, player_turn, float("-Inf"), float("+Inf"), max_depth)
+        toc = time.time()
+        print("[Minimax Alpha-Beta (player {0})] Elapsed (sec): {1:.6f}".format(player_turn, toc-tic))
+        if best_move is not None:
+            return [best_move]
+    
+    return []
+
+def agent_program_mcts(percepts, actuators, max_time=1):
+    game_board = percepts['game-board-sensor']
+    player_turn = percepts['turn-taking-indicator']
+    game_state = {
+        'game-board': game_board.copy(),
+        'player-turn': player_turn
+    }
+
+    if not TicTacToeGameEnvironment.is_terminal(game_state):
+        tic = time.time()
+        root_node = MCTSGraphNode(game_state, None, None)
+        best_move = mcts(root_node, player_turn, max_time)
+        toc = time.time()
+        print("[MTCS (player {0})] Elapsed (sec): {1:.6f}".format(player_turn, toc-tic))
+        if best_move is not None:
+            return [best_move]
+    
+    return []
+
+def agent_program_optimised_minimax(percepts, actuators, tt, max_depth=4):
+    game_board = percepts['game-board-sensor']
+    player_turn = percepts['turn-taking-indicator']
+    game_state = {
+        'game-board': game_board.copy(),
+        'player-turn': player_turn
+    }
+
+    
+    if not TicTacToeGameEnvironment.is_terminal(game_state):
+        state_node = GraphNode(game_state, None, None, 0)
+        tic = time.time()
+        _, best_move = optimised_minimax(state_node, player_turn, tt, max_depth)
+        toc = time.time()
+        print("[Optimised Minimax (player {0})] Elapsed (sec): {1:.6f}".format(player_turn, toc-tic))
+        if best_move is not None:
+            return [best_move]
+    
+    return []
\ No newline at end of file
diff --git a/week4/material/minimax_functions.py b/week4/material/minimax_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e43ffa80708244535aac08c3880c1cc49f8c5ac
--- /dev/null
+++ b/week4/material/minimax_functions.py
@@ -0,0 +1,11 @@
+from tictactoe_game_environment import TicTacToeGameEnvironment as gm
+from une_ai.models import GraphNode
+
+def minimax(node, player, depth):
+    return 0, None
+
+def minimax_alpha_beta(node, player, alpha, beta, depth):
+    return 0, None
+
+def optimised_minimax(node, player, tt, depth):
+    return 0, None
\ No newline at end of file
diff --git a/week4/material/tictactoe_app.py b/week4/material/tictactoe_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c19b54e81d7c049468f87a3dd38f50055452180
--- /dev/null
+++ b/week4/material/tictactoe_app.py
@@ -0,0 +1,23 @@
+from une_ai.tictactoe import TicTacToeGame
+from une_ai.tictactoe import TicTacToePlayer
+
+from tictactoe_game_environment import TicTacToeGameEnvironment
+from tictactoe_ttable import TicTacToeTTable
+
+from agent_programs import agent_program_random
+from agent_programs import agent_program_minimax, agent_program_minimax_alpha_beta, agent_program_optimised_minimax
+from agent_programs import agent_program_mcts
+
+if __name__ == '__main__':
+    # Creating the two players
+    # To change their behaviour, change the second parameter
+    # of the constructor with the desired agent program function
+    player_X = TicTacToePlayer('X', agent_program_random)
+    player_O = TicTacToePlayer('O', agent_program_random)
+
+    # DO NOT EDIT THE FOLLOWING INSTRUCTIONS!
+    environment = TicTacToeGameEnvironment()
+    environment.add_player(player_X)
+    environment.add_player(player_O)
+
+    game = TicTacToeGame(player_X, player_O, environment)
\ No newline at end of file
diff --git a/week4/material/tictactoe_game_environment.py b/week4/material/tictactoe_game_environment.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bbdbf883361437ca3d2af4d8dd37c1eae1b3e77
--- /dev/null
+++ b/week4/material/tictactoe_game_environment.py
@@ -0,0 +1,135 @@
+import numpy as np
+from scipy.signal import convolve2d
+from une_ai.models import GameEnvironment, GridMap, Agent
+
+class IllegalMove(Exception):
+    pass
+
+class TicTacToeGameEnvironment(GameEnvironment):
+
+    def __init__(self, board_size=3):
+        super().__init__("Tic Tac Toe")
+
+    # TODO
+    # implement the abstract method add_player
+    # the GameEnvironment superclass uses a dictionary self._players 
+    # to store the players of the game.
+    # For this game, we must limit the players to 2 players and
+    # The first added player will be X and the second O
+    def add_player(self, player):
+        assert isinstance(player, Agent), "The parameter player must be an instance of a subclass of the class Agent"
+        assert len(self._players) < 2, "It is not possible to add more than 2 players for this game."
+
+        pass
+
+    # TODO
+    # implement the abstract method get_game_state
+    # the method must return the current state of the game
+    # as a dictionary with the following keys:
+    # 'game-board' -> a copy of the game board (as 3x3 GridMap)
+    # 'player-turn' -> 'X' or 'O' depending on the current player turn
+    # You may first create properties in the constructor function __init__
+    # to store the game board and the current turn
+    def get_game_state(self):
+        pass
+    
+    # TODO
+    # implement the abstract method get_percepts
+    # this method returns a dictionary with keys the sensors of the agent
+    # and values the percepts gathered for that sensor at time t
+    # the sensors are:
+    # 'game-board-sensor' -> the 'game-board' value from the current game state
+    # 'turn-taking-indicator' -> the 'player-turn' value from the current game state
+    def get_percepts(self):
+        pass
+
+    # TODO
+    # implement the abstract method get_legal_actions
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state as input and it returns the list of
+    # legal actions in that game state
+    # An action is legal in a given game state if the game board cell 
+    # for that action is free from marks
+    def get_legal_actions(game_state):
+        pass
+
+    # TODO
+    # implement the abstract method transition_result
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state and an action to perform as input and it returns
+    # the new game state.
+    def transition_result(game_state, action):
+        pass
+    
+    # TODO
+    # implement the abstract method state_transition
+    # this method takes as input the agent's actuators
+    # and it changes the game environment state based
+    # on the values of the agent's actuators
+    # This agent has only one actuator, 'marker'
+    # the value of this actuator is a tuple with the x and y
+    # coordinates where the agent will place its marker on the game board
+    # We can implement this method by re-using the static method
+    # transition_result we just implemented
+    def state_transition(self, agent_actuators):
+        assert agent_actuators['marker'] is not None, "During a turn, the player must have set the 'marker' actuator value to a coordinate (x, y) of the game board where to place the marker."
+        
+        pass
+    
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It returns the turn of the player given a game state.
+    def turn(game_state):
+        assert 'player-turn' in game_state.keys(), "Invalid game state. A game state must have the key 'player-turn'"
+
+        return game_state['player-turn']
+    
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state as input and it returns the winner ('X' or 'O') if there is any
+    # or None if there is no winner (a tie or a non-terminal state)
+    # This method is already provided to you. You should look at its implementation
+    # and try to understand how it is finding a winner with the convolution operation
+    def get_winner(game_state):
+        game_board = game_state['game-board']
+
+        horizontal_kernel = np.array([[ 1, 1, 1]])
+        vertical_kernel = np.transpose(horizontal_kernel)
+        diag_kernel = np.eye(3, dtype=np.uint8)
+        flipped_diag_kernel = np.fliplr(diag_kernel)
+        detection_kernels = [horizontal_kernel, vertical_kernel, diag_kernel, flipped_diag_kernel]
+
+        for marker in ['X', 'O']:
+            player_markers = game_board.get_map() == marker
+            for kernel in detection_kernels:
+                convolved_values = convolve2d(player_markers, kernel, mode="valid")
+                if (convolved_values == 3).any():
+                    return marker
+        
+        return None
+
+    # TODO
+    # implement the abstract method is_terminal
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state as input and it returns True if the game state
+    # is terminal and False otherwise.
+    # In this game, a state is terminal if there are no more legal actions
+    # or if there is a winner.
+    def is_terminal(game_state):
+        pass
+
+    # TODO
+    # implement the abstract method payoff
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state and the player name ('X' or 'O') as input and it returns
+    # the payoff value for that player in the given game state
+    # In this scenario, we are only considering terminal states with a winner
+    # if there is not a winner yet (or there is a tie) we return 0
+    # In other games the payoff function may be more complex
+    def payoff(game_state, player_name):
+        pass
+    
\ No newline at end of file
diff --git a/week4/material/tictactoe_ttable.py b/week4/material/tictactoe_ttable.py
new file mode 100644
index 0000000000000000000000000000000000000000..80303dec3f37091f181d9234190ed0ea2f803ac2
--- /dev/null
+++ b/week4/material/tictactoe_ttable.py
@@ -0,0 +1,40 @@
+import random
+
+from une_ai.models import TTable
+
+class TicTacToeTTable(TTable):
+
+    def __init__(self, saving_directory='./transposition-tables/', instance_id=None, verbose=False):
+        super().__init__(saving_directory, instance_id, verbose)
+    
+    def generate_zobrist_table(self):
+        random_int = lambda: random.randint(0, pow(2, 64))
+        
+        markers = ['X', 'O'] 
+        board_size = 3
+        table = {}
+        for i in range(board_size):
+            for j in range(board_size):
+                for k in markers:
+                    table["{0}-{1}-{2}".format(i, j, k)] = random_int()
+        
+        table["player-turn-X"] = random_int()
+        table["player-turn-O"] = random_int()
+        
+        return table
+    
+    def compute_hash(self, state):
+        zobrist_table = self.get_zobrist_table()
+
+        player = state['player-turn']
+
+        h = 0
+        h ^= zobrist_table['player-turn-{0}'.format(player)]
+        game_board = state['game-board']
+        for i in range(game_board.get_width()):
+            for j in range(game_board.get_height()):
+                board_value = game_board.get_item_value(i, j)
+                if (board_value is not None):
+                    h ^= zobrist_table["{0}-{1}-{2}".format(i, j, board_value)]
+        
+        return str(h)
\ No newline at end of file
diff --git a/week4/solution/MCTS_functions.py b/week4/solution/MCTS_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b0b09c994908bb1b3443dd6784585d3f0e7167a
--- /dev/null
+++ b/week4/solution/MCTS_functions.py
@@ -0,0 +1,72 @@
+import random
+import time
+import math
+
+from tictactoe_game_environment import TicTacToeGameEnvironment as gm
+
+def selection_policy(node, target_player):
+    # In this case, we select a node that was not explored yet
+    # or, if all nodes were explored, the one with highest wins
+    # Better version may use different policies (e.g. see UCT policy)
+    successors = node.get_successors()
+    best_uct_score = 0
+    best_node = None
+    for s in successors:
+        if s.n() > 0:
+            uct = (s.wins(target_player) / s.n()) + math.sqrt(2) * math.sqrt(math.log(s.get_parent_node().n()) / s.n())
+            if best_node is None or uct > best_uct_score:
+                best_uct_score = uct
+                best_node = s
+        else:
+            # unexplored child, choose it
+            return s
+    
+    return best_node
+
+def random_playout(initial_node):
+    current_playout_state = initial_node.get_state()
+    while not gm.is_terminal(current_playout_state):    
+        possible_moves = gm.get_legal_actions(current_playout_state)
+        action = random.choice(possible_moves)
+        current_playout_state = gm.transition_result(current_playout_state, action)
+    
+    return gm.get_winner(current_playout_state)
+
+def mcts(root_node, target_player, max_time=1):
+    start_time = time.time()
+
+    # Performing simulations until the time is up
+    while (time.time() - start_time) < max_time:
+        # SELECTION PHASE
+        current_node = root_node
+        while not gm.is_terminal(current_node.get_state()) and not current_node.is_leaf_node():
+            current_node = selection_policy(current_node, target_player)
+        
+        # EXPANSION PHASE
+        selected_node = current_node
+        legal_moves = gm.get_legal_actions(selected_node.get_state())
+        for a in legal_moves:
+            if not selected_node.was_action_expanded(a):
+                successor_state = gm.transition_result(selected_node.get_state(), a)
+                selected_node.add_successor(successor_state, a)
+        
+        # SIMULATION PHASE
+        winner = random_playout(selected_node)
+        if winner is None:
+            # we consider a tie as a win
+            # if we don't do that, we might find an optimal opponent
+            # always 1 step ahead of us and the only best option
+            # for us is to achieve a tie
+            winner = target_player
+
+        # BACKPROPAGATION PHASE
+        selected_node.backpropagate(winner)
+    
+    # Time is up, choosing the child of the root node with highest wins
+    best_node = None
+    max_wins = 0
+    for successor in root_node.get_successors():
+        if best_node is None or successor.wins(target_player)/successor.n() > max_wins:
+            best_node = successor
+            max_wins = successor.wins(target_player) / successor.n()
+    return best_node.get_action()
\ No newline at end of file
diff --git a/week4/solution/agent_programs.py b/week4/solution/agent_programs.py
index f2f39265ef66aa64e2d8eb912a3a4ef5322900c8..d98fa76b5af420c59eaea32c458c592b4e14c7cb 100644
--- a/week4/solution/agent_programs.py
+++ b/week4/solution/agent_programs.py
@@ -1,141 +1,104 @@
 import random
 import time
-from une_ai.tictactoe import TicTacToeGameEnvironment as gm
 
-def player_marker(player_name):
-    if player_name == 'MAX':
-        return 'X'
-    else:
-        return 'Y'
+from une_ai.models import GraphNode, MCTSGraphNode
 
-def minimax(game_state, depth, player, is_maximising):
-    move_best = None
-    
-    if is_maximising:
-        value = float('-Inf')
-    else:
-        value = float('+Inf')
-    if depth == 0 or gm.is_terminal(game_state):
-        value = gm.payoff(game_state, player)
-        return value, move_best
-    
-    legal_actions = gm.get_legal_actions(game_state)
-    for action in legal_actions:
-        new_state = gm.transition_result(game_state, action)
-        value_new, move_new = minimax(new_state, depth - 1, player, not is_maximising)
-        if (is_maximising and value_new > value) or (not is_maximising and value_new < value):
-            value = value_new
-            move_best = action
-
-    return value, move_best
+from tictactoe_game_environment import TicTacToeGameEnvironment
+from minimax_functions import minimax, minimax_alpha_beta, optimised_minimax
+from MCTS_functions import mcts
 
-def minimax_alpha_beta(game_state, depth, player, is_maximising, alpha, beta):
-    move_best = None
-    legal_actions = gm.get_legal_actions(game_state)
-    if is_maximising:
-        value = float('-Inf')
-    else:
-        value = float('+Inf')
-    if depth == 0 or gm.is_terminal(game_state):
-        value = gm.payoff(game_state, player)
-        return value, move_best
+def agent_program_random(percepts, actuators):
+    try:
+        game_board = percepts['game-board-sensor']
+        player_turn = percepts['turn-taking-indicator']
+        game_state = {
+            'game-board': game_board.copy(),
+            'player-turn': player_turn
+        }
+    except KeyError:
+        print("Error. You may have not fully implemented yet the class TicTacToeGameEnvironment")
+        return []
     
-    for action in legal_actions:
-        new_state = gm.transition_result(game_state, action)
-        value_new, move_new = minimax_alpha_beta(new_state, depth - 1, player, not is_maximising, alpha, beta)
-        if is_maximising:
-            if value_new > value:
-                value = value_new
-                move_best = action
-            alpha = max(value, alpha)
-            if value >= beta:
-                break
-        else:
-            if value_new < value:
-                value = value_new
-                move_best = action
-            beta = min(value, beta)
-            if value <= alpha:
-                break
-        
-
-    return value, move_best
-
-def random_game_simulation(game_state, player, next_move):
-    new_game_state = gm.transition_result(game_state, next_move)
-    if gm.is_terminal(new_game_state):
-        value = gm.payoff(new_game_state, player)
-        return value
-    else:
-        new_legal_actions = gm.get_legal_actions(new_game_state)
-        next_move_rnd = random.choice(new_legal_actions)
-        value = random_game_simulation(new_game_state, player, next_move_rnd)
+    legal_moves = TicTacToeGameEnvironment.get_legal_actions(game_state)
+    if len(legal_moves) > 0:
+        return [random.choice(legal_moves)]
     
-    return value
+    return []
 
-def montecarlo_simulation(game_state, player, N):
-    if gm.is_terminal(game_state):
-        return None
+def agent_program_minimax(percepts, actuators, max_depth=4):
+    game_board = percepts['game-board-sensor']
+    player_turn = percepts['turn-taking-indicator']
+    game_state = {
+        'game-board': game_board.copy(),
+        'player-turn': player_turn
+    }
 
-    legal_actions = gm.get_legal_actions(game_state)
-    k = len(legal_actions)
-    payoffs = [None]*k
-    for i in range(0, k):
-        total_payoff = 0
-        for j in range(0, N):
-            cur_payoff = random_game_simulation(game_state, player, legal_actions[i])
-            total_payoff += cur_payoff
-        
-        avg_payoff = total_payoff / N
-        payoffs[i] = avg_payoff
     
-    avg_payoff_best = float('-Inf')
-    move_best = None
-    for i, avg in enumerate(payoffs):
-        if move_best is None or avg > avg_payoff_best:
-            avg_payoff_best = avg
-            move_best = legal_actions[i]
-    
-    return move_best
-
-def max_agent_program_random(percepts, actuators):
-    game_state = percepts['game-board-sensor']
-    free_pos = game_state.find_value(None)
-    if len(free_pos) > 0:
-        move = random.choice(free_pos)
-        actions = ['mark-{0}-{1}'.format(move[0], move[1])]
-        return actions
+    if not TicTacToeGameEnvironment.is_terminal(game_state):
+        state_node = GraphNode(game_state, None, None, 0)
+        tic = time.time()
+        _, best_move = minimax(state_node, player_turn, max_depth)
+        toc = time.time()
+        print("[Minimax (player {0})] Elapsed (sec): {1:.6f}".format(player_turn, toc-tic))
+        if best_move is not None:
+            return [best_move]
     
     return []
 
-def max_agent_program_minimax(percepts, actuators):
-    game_state = percepts['game-board-sensor']
-    free_pos = game_state.find_value(None)
-    if len(free_pos) > 0:
-        _, best_move = minimax(game_state, 'MAX')
-        return [best_move]
+def agent_program_minimax_alpha_beta(percepts, actuators, max_depth=4):
+    game_board = percepts['game-board-sensor']
+    player_turn = percepts['turn-taking-indicator']
+    game_state = {
+        'game-board': game_board.copy(),
+        'player-turn': player_turn
+    }
+    
+    if not TicTacToeGameEnvironment.is_terminal(game_state):
+        state_node = GraphNode(game_state, None, None, 0)
+        tic = time.time()
+        _, best_move = minimax_alpha_beta(state_node, player_turn, float("-Inf"), float("+Inf"), max_depth)
+        toc = time.time()
+        print("[Minimax Alpha-Beta (player {0})] Elapsed (sec): {1:.6f}".format(player_turn, toc-tic))
+        if best_move is not None:
+            return [best_move]
     
     return []
 
-def max_agent_program(percepts, actuators):
-    game_state = percepts['game-board-sensor']
-    free_pos = game_state.find_value(None)
-    if len(free_pos) > 0:
-        _, best_move = minimax_alpha_beta(game_state, 100, 'X', True, float("-Inf"), float("+Inf")) #, float('-Inf'), float('+Inf'))
-        #best_move = montecarlo_simulation(game_state, 'X', 100)
-        return [best_move]
+def agent_program_mcts(percepts, actuators, max_time=1):
+    game_board = percepts['game-board-sensor']
+    player_turn = percepts['turn-taking-indicator']
+    game_state = {
+        'game-board': game_board.copy(),
+        'player-turn': player_turn
+    }
+
+    if not TicTacToeGameEnvironment.is_terminal(game_state):
+        tic = time.time()
+        root_node = MCTSGraphNode(game_state, None, None)
+        best_move = mcts(root_node, player_turn, max_time)
+        toc = time.time()
+        print("[MTCS (player {0})] Elapsed (sec): {1:.6f}".format(player_turn, toc-tic))
+        if best_move is not None:
+            return [best_move]
     
     return []
 
-def min_agent_program(percepts, actuators):
-    game_state = percepts['game-board-sensor']
-    free_pos = game_state.find_value(None)
-    if len(free_pos) > 0:
-        #move = random.choice(free_pos)
-        #actions = ['mark-{0}-{1}'.format(move[0], move[1])]
-        #return actions
-        #_, best_move = minimax_alpha_beta(game_state, 100, 'O', True, float("-Inf"), float("+Inf")) #, float('-Inf'), float('+Inf'))
-        best_move = montecarlo_simulation(game_state, 'O', 100)
-        return [best_move]
+def agent_program_optimised_minimax(percepts, actuators, tt, max_depth=4):
+    game_board = percepts['game-board-sensor']
+    player_turn = percepts['turn-taking-indicator']
+    game_state = {
+        'game-board': game_board.copy(),
+        'player-turn': player_turn
+    }
+
+    
+    if not TicTacToeGameEnvironment.is_terminal(game_state):
+        state_node = GraphNode(game_state, None, None, 0)
+        tic = time.time()
+        _, best_move = optimised_minimax(state_node, player_turn, tt, max_depth)
+        toc = time.time()
+        print("[Optimised Minimax (player {0})] Elapsed (sec): {1:.6f}".format(player_turn, toc-tic))
+        if best_move is not None:
+            return [best_move]
     
     return []
\ No newline at end of file
diff --git a/week4/solution/minimax_functions.py b/week4/solution/minimax_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c3868002fab2f5e3f8b3e5d978fbe99ac23e6d1
--- /dev/null
+++ b/week4/solution/minimax_functions.py
@@ -0,0 +1,107 @@
+from tictactoe_game_environment import TicTacToeGameEnvironment as gm
+from une_ai.models import GraphNode
+
+def minimax(node, player, depth):
+    move_best = None
+
+    game_state = node.get_state()
+    player_turn = game_state['player-turn']
+    is_maximising = player_turn == player
+    
+    if is_maximising:
+        value = float('-Inf')
+    else:
+        value = float('+Inf')
+    if depth <= 0 or gm.is_terminal(game_state):
+        value = gm.payoff(game_state, player)
+        return value, move_best
+    
+    legal_actions = gm.get_legal_actions(game_state)
+    for action in legal_actions:
+        new_state = gm.transition_result(game_state, action)
+        child_node = GraphNode(new_state, node, action, 1)
+        value_new, _ = minimax(child_node, player, depth - 1)
+        if (is_maximising and value_new > value) or (not is_maximising and value_new < value):
+            value = value_new
+            move_best = action
+
+    return value, move_best
+
+def minimax_alpha_beta(node, player, alpha, beta, depth):
+    game_state = node.get_state()
+    move_best = None
+    legal_actions = gm.get_legal_actions(game_state)
+
+    player_turn = game_state['player-turn']
+    is_maximising = player_turn == player
+
+    if is_maximising:
+        value = float('-Inf')
+    else:
+        value = float('+Inf')
+    if depth <= 0 or gm.is_terminal(game_state):
+        value = gm.payoff(game_state, player)
+        return value, move_best
+    
+    for action in legal_actions:
+        new_state = gm.transition_result(game_state, action)
+        child_node = GraphNode(new_state, node, action, 1)
+        value_new, _ = minimax_alpha_beta(child_node, player, alpha, beta, depth - 1)
+        if is_maximising:
+            if value_new > value:
+                value = value_new
+                move_best = action
+            alpha = max(value, alpha)
+            if value >= beta:
+                break
+        else:
+            if value_new < value:
+                value = value_new
+                move_best = action
+            beta = min(value, beta)
+            if value <= alpha:
+                break
+        
+    return value, move_best
+
+def optimised_minimax(node, player, tt, depth):
+    game_state = node.get_state()
+    player_turn = game_state['player-turn']
+    is_maximising = player_turn == player
+
+    # using transposition table
+    tt_entry = tt.lookup(node)
+    if tt_entry is not None and tt_entry['depth'] >= depth:
+        return tt_entry['value'], tt_entry["move_best"]
+    
+    move_best = None
+    
+    if is_maximising:
+        value = float('-Inf')
+    else:
+        value = float('+Inf')
+    if depth <= 0 or gm.is_terminal(game_state):
+        value = gm.payoff(game_state, player)
+        return value, move_best
+    
+    legal_actions = gm.get_legal_actions(game_state)
+    for action in legal_actions:
+        new_state = gm.transition_result(game_state, action)
+        child_node = GraphNode(new_state, node, action, 1)
+        value_new, _ = optimised_minimax(child_node, player, tt, depth - 1)
+        
+        if (is_maximising and value_new > value) or (not is_maximising and value_new < value):
+            value = value_new
+            move_best = action
+
+    # storing value in transposition table
+    if tt_entry is None or tt_entry['depth'] <= depth:
+        entry_dict = {
+            "value": int(value),
+            "depth": depth,
+            "move_best": move_best
+        }
+
+        tt.store_node(node, entry_dict)
+
+    return value, move_best
\ No newline at end of file
diff --git a/week4/solution/tictactoe.py b/week4/solution/tictactoe.py
deleted file mode 100644
index 12f665044f591ca9b827016daad8586bd925801f..0000000000000000000000000000000000000000
--- a/week4/solution/tictactoe.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from une_ai.tictactoe import TicTacToeGame
-from agent_programs import max_agent_program, min_agent_program
-
-if __name__ == '__main__':
-    game = TicTacToeGame(
-        max_agent_program,
-        min_agent_program
-    )
\ No newline at end of file
diff --git a/week4/solution/tictactoe_app.py b/week4/solution/tictactoe_app.py
new file mode 100644
index 0000000000000000000000000000000000000000..bded234d1570eb658a78558802bcebfc7461a7f1
--- /dev/null
+++ b/week4/solution/tictactoe_app.py
@@ -0,0 +1,30 @@
+from une_ai.tictactoe import TicTacToeGame
+from une_ai.tictactoe import TicTacToePlayer
+
+from tictactoe_game_environment import TicTacToeGameEnvironment
+from tictactoe_ttable import TicTacToeTTable
+
+from agent_programs import agent_program_random
+from agent_programs import agent_program_minimax, agent_program_minimax_alpha_beta, agent_program_optimised_minimax
+from agent_programs import agent_program_mcts
+
+if __name__ == '__main__':
+    # Creating the transposition table
+    tt = TicTacToeTTable(instance_id='6879eb26-1ba1-11ee-8577-9e02763478a3')
+    print("Initialised a Transposition table with instance id {0}".format(tt.get_instance_id()))
+
+    wrapped_minimax_tt = lambda perc, act: agent_program_optimised_minimax(perc, act, tt, max_depth=4)
+
+    # Creating the two players
+    # To change their behaviour, change the second parameter
+    # of the constructor with the desired agent program function
+    player_X = TicTacToePlayer('X', wrapped_minimax_tt)
+    player_O = TicTacToePlayer('O', lambda perc, act: agent_program_minimax_alpha_beta(perc, act, 10))
+
+    # DO NOT EDIT THE FOLLOWING INSTRUCTIONS!
+    environment = TicTacToeGameEnvironment()
+    environment.add_player(player_X)
+    environment.add_player(player_O)
+
+    game = TicTacToeGame(player_X, player_O, environment)
+
diff --git a/week4/solution/tictactoe_game_environment.py b/week4/solution/tictactoe_game_environment.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad36b9ed2016cb5e01d689783ce53595c047771a
--- /dev/null
+++ b/week4/solution/tictactoe_game_environment.py
@@ -0,0 +1,192 @@
+import numpy as np
+from scipy.signal import convolve2d
+from une_ai.models import GameEnvironment, GridMap, Agent
+
+class IllegalMove(Exception):
+    pass
+
+class TicTacToeGameEnvironment(GameEnvironment):
+
+    def __init__(self, board_size=3):
+        super().__init__("Tic Tac Toe")
+
+        self._board_size = board_size
+        self._game_board = GridMap(board_size, board_size, None)
+        self._player_turn = 'X' # X always starts
+
+    # TODO
+    # implement the abstract method add_player
+    # the GameEnvironment superclass uses a dictionary self._players 
+    # to store the players of the game.
+    # For this game, we must limit the players to 2 players and
+    # The first added player will be X and the second O
+    def add_player(self, player):
+        assert isinstance(player, Agent), "The parameter player must be an instance of a subclass of the class Agent"
+        assert len(self._players) < 2, "It is not possible to add more than 2 players for this game."
+        
+        if len(self._players) == 0:
+            marker = 'X'
+        else:
+            marker = 'O'
+
+        self._players[marker] = player
+
+        return marker
+
+    # TODO
+    # implement the abstract method get_game_state
+    # the method must return the current state of the game
+    # as a dictionary with the following keys:
+    # 'game-board' -> a copy of the game board (as 3x3 GridMap)
+    # 'player-turn' -> 'X' or 'O' depending on the current player turn
+    # You may first create properties in the constructor function __init__
+    # to store the game board and the current turn
+    def get_game_state(self):
+        gs = {
+            'game-board': self._game_board.copy(),
+            'player-turn': self._player_turn
+        }
+        return gs
+    
+    # TODO
+    # implement the abstract method get_percepts
+    # this method returns a dictionary with keys the sensors of the agent
+    # and values the percepts gathered for that sensor at time t
+    # the sensors are:
+    # 'game-board-sensor' -> the 'game-board' value from the current game state
+    # 'turn-taking-indicator' -> the 'player-turn' value from the current game state
+    def get_percepts(self):
+        gs = self.get_game_state()
+        return {
+            'game-board-sensor': gs['game-board'], 
+            'turn-taking-indicator': gs['player-turn']
+        }
+    
+    # TODO
+    # implement the abstract method get_legal_actions
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state as input and it returns the list of
+    # legal actions in that game state
+    # An action is legal in a given game state if the game board cell 
+    # for that action is free from marks
+    def get_legal_actions(game_state):
+        legal_actions = []
+        game_board = game_state['game-board']
+        empty_cells = game_board.find_value(None)
+        for empty_cell in empty_cells:
+            legal_actions.append('mark-{0}-{1}'.format(empty_cell[0], empty_cell[1]))
+        
+        return legal_actions
+
+    # TODO
+    # implement the abstract method transition_result
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state and an action to perform as input and it returns
+    # the new game state.
+    def transition_result(game_state, action):
+        legal_actions = TicTacToeGameEnvironment.get_legal_actions(game_state)
+        if action not in legal_actions:
+            raise(IllegalMove('The action {0} is not a legal move for the given game state {1}.'.format(action, game_state.get_map())))
+        
+        marker = TicTacToeGameEnvironment.turn(game_state)
+        
+        tokens = action.split('-')
+        x, y = (int(tokens[1]), int(tokens[2]))
+        new_game_board = game_state['game-board'].copy()
+        new_game_board.set_item_value(x, y, marker)
+
+        new_game_state = {
+            'game-board': new_game_board,
+            'player-turn': 'O' if game_state['player-turn'] == 'X' else 'X'
+        }
+
+        return new_game_state
+    
+    # TODO
+    # implement the abstract method state_transition
+    # this method takes as input the agent's actuators
+    # and it changes the game environment state based
+    # on the values of the agent's actuators
+    # This agent has only one actuator, 'marker'
+    # the value of this actuator is a tuple with the x and y
+    # coordinates where the agent will place its marker on the game board
+    # We can implement this method by re-using the static method
+    # transition_result we just implemented
+    def state_transition(self, agent_actuators):
+        assert agent_actuators['marker'] is not None, "During a turn, the player must have set the 'marker' actuator value to a coordinate (x, y) of the game board where to place the marker."
+
+        x, y = agent_actuators['marker']
+        gs = self.get_game_state()
+        action = 'mark-{0}-{1}'.format(x, y)
+        new_gs = TicTacToeGameEnvironment.transition_result(gs, action)
+
+        self._player_turn = new_gs['player-turn']
+        self._game_board = new_gs['game-board'].copy()
+    
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It returns the turn of the player given a game state.
+    def turn(game_state):
+        assert 'player-turn' in game_state.keys(), "Invalid game state. A game state must have the key 'player-turn'"
+
+        return game_state['player-turn']
+    
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state as input and it returns the winner ('X' or 'O') if there is any
+    # or None if there is no winner (a tie or a non-terminal state)
+    # This method is already provided to you. You should look at its implementation
+    # and try to understand how it is finding a winner with the convolution operation
+    def get_winner(game_state):
+        game_board = game_state['game-board']
+
+        horizontal_kernel = np.array([[ 1, 1, 1]])
+        vertical_kernel = np.transpose(horizontal_kernel)
+        diag_kernel = np.eye(3, dtype=np.uint8)
+        flipped_diag_kernel = np.fliplr(diag_kernel)
+        detection_kernels = [horizontal_kernel, vertical_kernel, diag_kernel, flipped_diag_kernel]
+
+        for marker in ['X', 'O']:
+            player_markers = game_board.get_map() == marker
+            for kernel in detection_kernels:
+                convolved_values = convolve2d(player_markers, kernel, mode="valid")
+                if (convolved_values == 3).any():
+                    return marker
+        
+        return None
+
+    # TODO
+    # implement the abstract method is_terminal
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state as input and it returns True if the game state
+    # is terminal and False otherwise.
+    # In this game, a state is terminal if there are no more legal actions
+    # or if there is a winner.
+    def is_terminal(game_state):
+        # game is over if the board is full
+        remaining_actions = TicTacToeGameEnvironment.get_legal_actions(game_state)
+        winner = TicTacToeGameEnvironment.get_winner(game_state)
+
+        return len(remaining_actions) == 0 or winner is not None
+
+    # TODO
+    # implement the abstract method payoff
+    # This method is a static method (i.e. we do not have access to self
+    # and it can only be accessed via the class TicTacToeGameEnvironment)
+    # It takes a game_state and the player name ('X' or 'O') as input and it returns
+    # the payoff value for that player in the given game state
+    # In this scenario, we are only considering terminal states with a winner
+    # if there is not a winner yet (or there is a tie) we return 0
+    # In other games the payoff function may be more complex
+    def payoff(game_state, player_name):
+        winner = TicTacToeGameEnvironment.get_winner(game_state)
+        if winner is None:
+            return 0
+        elif winner == player_name:
+            return 1
+        else:
+            return -1
+    
\ No newline at end of file
diff --git a/week4/solution/tictactoe_ttable.py b/week4/solution/tictactoe_ttable.py
new file mode 100644
index 0000000000000000000000000000000000000000..80303dec3f37091f181d9234190ed0ea2f803ac2
--- /dev/null
+++ b/week4/solution/tictactoe_ttable.py
@@ -0,0 +1,40 @@
+import random
+
+from une_ai.models import TTable
+
+class TicTacToeTTable(TTable):
+
+    def __init__(self, saving_directory='./transposition-tables/', instance_id=None, verbose=False):
+        super().__init__(saving_directory, instance_id, verbose)
+    
+    def generate_zobrist_table(self):
+        random_int = lambda: random.randint(0, pow(2, 64))
+        
+        markers = ['X', 'O'] 
+        board_size = 3
+        table = {}
+        for i in range(board_size):
+            for j in range(board_size):
+                for k in markers:
+                    table["{0}-{1}-{2}".format(i, j, k)] = random_int()
+        
+        table["player-turn-X"] = random_int()
+        table["player-turn-O"] = random_int()
+        
+        return table
+    
+    def compute_hash(self, state):
+        zobrist_table = self.get_zobrist_table()
+
+        player = state['player-turn']
+
+        h = 0
+        h ^= zobrist_table['player-turn-{0}'.format(player)]
+        game_board = state['game-board']
+        for i in range(game_board.get_width()):
+            for j in range(game_board.get_height()):
+                board_value = game_board.get_item_value(i, j)
+                if (board_value is not None):
+                    h ^= zobrist_table["{0}-{1}-{2}".format(i, j, board_value)]
+        
+        return str(h)
\ No newline at end of file
diff --git a/week4/solution/transposition-tables/6879eb26-1ba1-11ee-8577-9e02763478a3/transposition-table.json b/week4/solution/transposition-tables/6879eb26-1ba1-11ee-8577-9e02763478a3/transposition-table.json
new file mode 100644
index 0000000000000000000000000000000000000000..3528ad1a239201d420b7b0b4bf943e35f77c3b8e
--- /dev/null
+++ b/week4/solution/transposition-tables/6879eb26-1ba1-11ee-8577-9e02763478a3/transposition-table.json
@@ -0,0 +1 @@
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diff --git a/week4/solution/transposition-tables/6879eb26-1ba1-11ee-8577-9e02763478a3/zobrist-table.json b/week4/solution/transposition-tables/6879eb26-1ba1-11ee-8577-9e02763478a3/zobrist-table.json
new file mode 100644
index 0000000000000000000000000000000000000000..1f3b4d62242805d4f4ec97e4d107a1b823107b9e
--- /dev/null
+++ b/week4/solution/transposition-tables/6879eb26-1ba1-11ee-8577-9e02763478a3/zobrist-table.json
@@ -0,0 +1 @@
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