From 1da4eefc6f217e25fc1fb4d90fd271f9a3a5fc1f Mon Sep 17 00:00:00 2001
From: Jon <vitale.jonathan@ymail.com>
Date: Sun, 4 Jun 2023 14:02:48 +1000
Subject: [PATCH] Add solution for workshop week 4
---
week4/solution/agent_programs.py | 141 +++++++++++++++++++++++++++++++
week4/solution/tictactoe.py | 8 ++
2 files changed, 149 insertions(+)
create mode 100644 week4/solution/agent_programs.py
create mode 100644 week4/solution/tictactoe.py
diff --git a/week4/solution/agent_programs.py b/week4/solution/agent_programs.py
new file mode 100644
index 0000000..f2f3926
--- /dev/null
+++ b/week4/solution/agent_programs.py
@@ -0,0 +1,141 @@
+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'
+
+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
+
+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
+
+ 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)
+
+ return value
+
+def montecarlo_simulation(game_state, player, N):
+ if gm.is_terminal(game_state):
+ return None
+
+ 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
+
+ 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]
+
+ 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]
+
+ 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]
+
+ return []
\ No newline at end of file
diff --git a/week4/solution/tictactoe.py b/week4/solution/tictactoe.py
new file mode 100644
index 0000000..12f6650
--- /dev/null
+++ b/week4/solution/tictactoe.py
@@ -0,0 +1,8 @@
+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
--
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