mirror of
https://github.com/peter-tanner/Algorithms-Agents-and-Artificial-Intelligence-project-final.git
synced 2024-11-30 07:00:17 +08:00
126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
import csv
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from datetime import datetime
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import pathlib
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import pickle
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from time import time
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import numpy as np
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from copy import deepcopy
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from types import FunctionType
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from typing import Callable, Dict, List
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from etc.gamestate import GameState, Winner
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from etc.util import debug_print
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from play_config import ENABLE_SNAPSHOTS, GAMESTATE_PARAMETER_SNAPSHOT_OUTPUT_DIR, INITIAL_UNCERTAINTY_RANGE, N_GRAY_AGENT, N_GREEN_AGENT,\
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P_GRAY_AGENT_FRIENDLY, P_GREEN_AGENT_BLUE, P_GREEN_AGENT_CONNECTION
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def rand_rounds(gs: GameState) -> GameState:
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blue_gamestate = deepcopy(gs)
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gs.blue_agent.dumb_influence(gs)
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red_gamestate = deepcopy(gs)
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gs.red_agent.dumb_influence(gs)
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# gs.draw_green_network()
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# gs.green_round()
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# gs.draw_green_network()
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# spy = bool(gs.rand.getrandbits(1))
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# spy = False
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gs.green_round()
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gs.red_agent.update_short_term_mem(red_gamestate, gs)
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gs.blue_agent.update_short_term_mem(blue_gamestate, gs)
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gs.draw_green_network()
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gs.iteration += 1
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return gs
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def intelligent_rounds(gs: GameState) -> GameState:
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blue_gamestate = deepcopy(gs)
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gs.blue_agent.smart_influence(gs)
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red_gamestate = deepcopy(gs)
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gs.red_agent.smart_influence(gs)
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gs.green_round()
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gs.red_agent.update_short_term_mem(red_gamestate, gs)
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gs.blue_agent.update_short_term_mem(blue_gamestate, gs)
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gs.draw_green_network()
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gs.iteration += 1
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return gs
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def round(round_func: FunctionType) -> List[GameState]:
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state_buffer: List[GameState] = []
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# gs: GameState = GameState.short_init((100, 0.05, 0.5), (10, 0.5))
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gs: GameState = GameState(
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green_agents=(N_GREEN_AGENT, P_GREEN_AGENT_CONNECTION, P_GREEN_AGENT_BLUE),
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gray_agents=(N_GRAY_AGENT, P_GRAY_AGENT_FRIENDLY),
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uncertainty_interval=INITIAL_UNCERTAINTY_RANGE,
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seed=None, graphics=False)
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state_buffer.append(deepcopy(gs))
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debug_print("INITIAL CONDITIONS.")
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debug_print(gs)
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debug_print("STARTING GAME.")
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# gs.draw_green_network()
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while gs.winner == Winner.NO_WINNER:
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gs = round_func(gs)
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# debug_print(gs.red_agent.red_followers, gs.blue_agent.blue_energy, len(list(gs.green_agents)))
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debug_print(gs)
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gs.update_winner()
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gs.draw_green_network()
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state_buffer.append(deepcopy(gs))
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# print(gs)
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gs.close()
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# state_buffer.append(deepcopy(gs))
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print(f"{gs.iteration} WINNER {gs.winner}")
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return state_buffer
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# Calibrator
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def training_rounds(win_file_blue, win_file_red, round_func: Callable, training_iterations: int) -> None:
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# state_buffer: List[GameState] = round(rand_rounds)
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# exit()
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ending_states: Dict[Winner, int] = {
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Winner.BLUE_NO_ENERGY: 0,
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Winner.BLUE_WIN: 0,
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Winner.RED_NO_FOLLOWERS: 0,
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Winner.RED_WIN: 0,
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}
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blue_win_round_len = []
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red_win_round_len = []
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for x in range(0, training_iterations):
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t = time()
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print(f"Game {x}")
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state_buffer: List[GameState] = round(round_func)
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ending_state: GameState = state_buffer[-1]
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ending_states[ending_state.winner] += 1
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if ending_state.winner == Winner.BLUE_NO_ENERGY or ending_state.winner == Winner.RED_WIN:
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red_win_round_len.append(ending_state.iteration)
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blue_win_round_len.append(-ending_state.iteration)
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elif ending_state.winner == Winner.RED_NO_FOLLOWERS or ending_state.winner == Winner.BLUE_WIN:
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red_win_round_len.append(-ending_state.iteration)
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blue_win_round_len.append(ending_state.iteration)
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print(f"dt={time() - t} s")
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print(ending_states)
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print(blue_win_round_len)
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print(red_win_round_len)
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with open(win_file_blue, "w") as f:
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writer = csv.writer(f)
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writer.writerow(blue_win_round_len)
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with open(win_file_red, "w") as f:
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writer = csv.writer(f)
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writer.writerow(red_win_round_len)
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if __name__ == "__main__":
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N_RANDOM_ROUNDS = 120
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N_INTELLIGENT_ROUNDS = 300
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training_rounds("rand_blue_win.csv", "rand_red_win.csv", rand_rounds, N_RANDOM_ROUNDS)
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training_rounds("intel_blue_win.csv", "intel_red_win.csv", intelligent_rounds, N_INTELLIGENT_ROUNDS)
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