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smb1Py_runner_task_path_testing.py
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from functools import partial
import random
from game_runner_neat import GameRunner
from runnerConfiguration import RunnerConfig, IOData
from baseGame import EvalGame
from games.smb1Py.py_mario_bros.PythonSuperMario_master.smb_game import SMB1Game
from games.smb1Py.py_mario_bros.PythonSuperMario_master.source.states.segmentGenerator import SegmentGenerator,GenerationOptions
from games.smb1Py.py_mario_bros.PythonSuperMario_master.source.states.segment import SegmentState
import os
import neat
import multiprocessing
from training_data import TrainingDataManager
try:
import cPickle as pickle
except:
import pickle
from games.smb1Py.py_mario_bros.PythonSuperMario_master.source import tools
from games.smb1Py.py_mario_bros.PythonSuperMario_master.source import constants as c
import run_states as run_states
steps_threshold = 800;
def task_obstruction_score(obstructions):
return -obstructions[0];
def getFitness(inputs):
obstructions = inputs['task_obstructions'];
return task_obstruction_score(obstructions) + inputs['player_state'] + inputs['task_reached']*50 - inputs['steps']*0.1;
def getRunning(inputs):
return (not(inputs['done']) and (not inputs['stillness_time'] > steps_threshold));
def generate_data(instructions:list[tuple[GenerationOptions,int]],shuffle=True):
data = []
for options,quantity in instructions:
data += SegmentGenerator.generateBatch(options,quantity);
if shuffle:
random.shuffle(data);
return data;
if __name__ == "__main__":
import gc
NAME = "smb1Py";
multiprocessing.freeze_support();
run_state = run_states.CONTINUE;
currentRun = 10;
override_config = True;
manual_continue_generation = None;
diff_inputs = False;
diff_outputs = False;
output_map = None;
prevData = ['player_state',
IOData('vel','array',array_size=[2]),
IOData('task_position_offset','array',array_size=[2]),
IOData('pos','array',array_size=[2])];
inputOptions = c.COLLISION_GRID;
reRunGeneration = 1523;
# reRunId = 88;
customGenome = None;
##TRAINING_DATA##
set_data = True;
add_data = False;
start_data_index = 0
additional_data_indices = [3];
configs = [
GenerationOptions(num_blocks=0,ground_height=(7,9),valid_task_blocks=c.FLOOR,valid_start_blocks=c.FLOOR), #0
GenerationOptions(num_blocks=0,ground_height=(7,9),valid_task_blocks=c.INNER,valid_start_blocks=c.FLOOR), #1
GenerationOptions(num_blocks=(1,3),ground_height=(7,9),valid_task_blocks=c.INNER,valid_start_blocks=c.FLOOR), #2
GenerationOptions(num_blocks=(0,4),ground_height=(7,9),task_batch_size=(1,4)), #3
GenerationOptions(num_blocks=(0,8),ground_height=(7,10),task_batch_size=(1,4)), #4
GenerationOptions(num_blocks=(0,4),ground_height=7,task_batch_size=(1,4),num_gaps=(1,2),gap_width=(1,2)), #5
GenerationOptions(num_blocks=(0,6),ground_height=7,task_batch_size=(1,4),num_gaps=(1,2),gap_width=(1,4)), #6
GenerationOptions(num_blocks=(0,4),ground_height=7,task_batch_size=(1,4),num_gaps=(1,2),gap_width=(1,3),allow_gap_under_start=True), #7
GenerationOptions(num_blocks=(0,6),ground_height=(7,9),task_batch_size=(1,3),num_enemies={c.ENEMY_TYPE_GOOMBA:1},valid_enemy_positions=c.GROUNDED), #8
GenerationOptions(size=(20,15),inner_size=(14,9),num_blocks=(0,8),ground_height=(7,10),task_batch_size=(1,4)), #9
];
orders = [(configs[4],70),(configs[2],20),(configs[5],30),(configs[6],20),(configs[7],10),(configs[9],30)];
tdManager = TrainingDataManager(NAME,-currentRun);
if (run_state == run_states.NEW or set_data):
# data = generate_data(orders);
state:SegmentState = SegmentGenerator.generate(GenerationOptions())[0]; # type: ignore
x,y = state.task;
path = [(x,y),(x+c.TILE_SIZE*2,y),(x+c.TILE_SIZE*4,y),(x+c.TILE_SIZE*6,y),(x+c.TILE_SIZE*8,y),(x+c.TILE_SIZE*10,y)];
print(path[0]);
print(state.task);
state.task_path = path;
tdManager.set_data([state]);
if add_data:
for idx in additional_data_indices:
tdManager.add_data(SegmentGenerator.generateBatch(configs[idx],20));
inputData = [
'player_state',
IOData('vel','array',array_size=[2]),
IOData('task_position_offset','array',array_size=[2]),
IOData('pos','array',array_size=[2])];
config_suffix = "-nogrid"
if inputOptions == c.FULL:
inputData += [
IOData('collision_grid','array',[15,15]),
IOData('enemy_grid','array',[15,15]),
IOData('box_grid','array',[15,15]),
IOData('brick_grid','array',[15,15]),
IOData('powerup_grid','array',[15,15])]
config_suffix = "-full"
if inputOptions == c.COLLISION_GRID:
inputData.append(IOData('collision_grid','array',[15,15]))
config_suffix = "-blockgrid"
runConfig = RunnerConfig(
getFitness,
getRunning,
logging=True,
parallel=False,
gameName=NAME,
returnData=inputData,
num_trials=1,
num_generations=None,
training_data=tdManager);
runConfig.reporters = [tdManager];
runConfig.tile_scale = 2;
runConfig.view_distance = 3.75;
runConfig.task_obstruction_score = task_obstruction_score;
runConfig.external_render = False;
runConfig.parallel_processes = 6;
runConfig.chunkFactor = 24;
runConfig.saveFitness = True;
runConfig.logPath = f'logs\\smb1Py\\run-{currentRun}-log.txt';
runConfig.fitness_collection_type='delta_max';
print(runConfig.gameName);
game = EvalGame(SMB1Game);
runner = GameRunner(game,runConfig);
config_path = os.path.join(os.path.dirname(__file__), 'configs','config-pygame-smb1' + config_suffix);
config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
neat.DefaultSpeciesSet, neat.DefaultStagnation,
config_path);
config_transfer = None;
if override_config:
config_transfer = (config, runConfig.get_input_transfer(prevData) if diff_inputs else None, output_transfer if diff_outputs else None)
if (run_state == run_states.EVAL_CUSTOM):
customGenome = neat.genome.DefaultGenome(0);
customGenome.configure_new(config.genome_config);
customGenome.add_connection(config.genome_config,-1,2,-1,True);
customGenome.add_connection(config.genome_config,-1,3,1,True);
if (run_state == run_states.CONTINUE):
winner = runner.continue_run('run_' + str(currentRun),manual_generation=manual_continue_generation,manual_config_override=config_transfer);
print('\nBest genome:\n{!s}'.format(winner));
else:
local_dir = os.path.dirname(__file__)
if (run_state == run_states.NEW):
winner = runner.run(config,'run_' + str(currentRun));
print('\nBest genome:\n{!s}'.format(winner))
if (run_state == run_states.RERUN):
runner.replay_best(reRunGeneration,config,'run_' + str(currentRun),net=True,randomReRoll=True);
if (run_state == run_states.RERUN_ALL):
runner.replay_generation(reRunGeneration,'run_' + str(currentRun));
if (run_state == run_states.RERUN_ID):
runner.render_genome_by_id(reRunId,reRunGeneration,config,'run_' + str(currentRun),net=True);
if (run_state == run_states.EVAL_CUSTOM):
runner.render_custom_genome_object(customGenome,config,net=True)