def train(
population: Population,
games: list,
iterations: int,
debug: bool = False,
duration: int = 0,
unused_cpu: int = 0,
):
"""Train the population on the requested number of iterations."""
from environment.env_training import TrainingEnv
population.log("\n===> TRAINING <===\n")
game_config = deepcopy(population.config)
if duration > 0: game_config.game.duration = duration
trainer = TrainingEnv(
unused_cpu=unused_cpu, # Use two cores less to keep laptop usable
game_config=game_config,
games=games,
)
trainer.evaluate_and_evolve(
pop=population,
n=iterations,
parallel=not debug,
save_interval=1 if debug else 10,
)
def blueprint(
population: Population,
games: list,
debug: bool = False,
duration: int = 0,
unused_cpu: int = 0,
):
"""Create a blueprint evaluation for the given population on the first 5 games."""
from environment.env_visualizing import VisualizingEnv
population.log("\n===> CREATING BLUEPRINTS <===\n")
population.log(f"Creating blueprints for games: {games}")
game_config = deepcopy(population.config)
if duration > 0: game_config.game.duration = duration
visualizer = VisualizingEnv(
game_config=game_config,
games=games,
unused_cpu=unused_cpu,
)
visualizer.blueprint_genomes(
pop=population,
parallel=not debug,
)
def trace_most_fit(
population: Population,
genome: Genome,
games: list,
debug: bool = False,
duration: int = 0,
unused_cpu: int = 0,
):
"""Create a trace evaluation for the given genome on the provided games."""
from environment.env_visualizing import VisualizingEnv
game_config = deepcopy(population.config)
if duration > 0: game_config.game.duration = duration
population.log("\n===> CREATING GENOME TRACE <===\n")
population.log(f"Creating traces for games: {games}")
visualizer = VisualizingEnv(
game_config=game_config,
games=games,
unused_cpu=unused_cpu,
)
for g in games: # TODO: Bug in warm-up of network if multiple games evaluated
visualizer.set_games([g])
visualizer.trace_genomes(
pop=population,
given_genome=genome,
parallel=not debug,
)
def evaluate_population(pop: Population, cfg: Config, cpu: int, experiment_id: int):
"""Evaluate the given population."""
pop.log(f"{pop.name} - Evaluating the population...")
_, game_ids_eval = get_game_ids(experiment_id=experiment_id)
multi_env = get_multi_env(pop=pop, game_config=cfg)
multi_env.set_games(game_ids_eval, noise=False)
pool = mp.Pool(mp.cpu_count() - cpu)
manager = mp.Manager()
return_dict = manager.dict()
pbar = tqdm(total=len(pop.population), desc="Evaluating")
def update(*_):
pbar.update()
for genome_id, genome in pop.population.items():
pool.apply_async(func=multi_env.eval_genome, args=((genome_id, genome), return_dict), callback=update)
pool.close() # Close the pool
pool.join() # Postpone continuation until everything is finished
pbar.close()
# Calculate the fitness from the given return_dict
pop.log(f"{pop.name} - Calculating fitness scores...")
fitness = calc_pop_fitness(
fitness_cfg=pop.config.evaluation,
game_cfg=cfg.game,
game_obs=return_dict,
gen=pop.generation,
)
for i, genome in pop.population.items():
genome.fitness = fitness[i]
# Get the fittest genome
best = None
for g in pop.population.values():
if best is None or g.fitness > best.fitness: best = g
pop.best_genome = best
# Save the results
pop.save()
# Visualize most fit genome
visualize_genome(
debug=True,
genome=best,
population=pop,
)
# Trace the most fit genome
trace_most_fit(
debug=False,
games=game_ids_eval,
genome=best,
population=pop,
unused_cpu=cpu,
)
def visualize_best_genome(pop: Population):
"""Visualize the population's fittest genome."""
pop.log(
f"Most fit genome: {pop.best_genome.key} with fitness: {round(pop.best_genome.fitness, 3)}"
)
name = f"genome_{pop.best_genome.key}"
sf = get_subfolder(f'population/storage/{pop.folder_name}/{pop}/',
'images')
sf = get_subfolder(sf, f'gen{pop.generation:05d}')
draw_net(config=pop.config.genome,
genome=pop.best_genome,
debug=True,
filename=f'{sf}{name}',
view=False)
def evaluate_same_games_and_evolve(
self,
games: list,
pop: Population,
n: int = 1,
parallel=True,
save_interval: int = 1,
):
"""
Evaluate the population on the same games.
:param games: List of games used for training
:param pop: Population object
:param n: Number of generations
:param parallel: Parallel the code (disable parallelization for debugging purposes)
:param save_interval: Indicates how often a population gets saved
"""
multi_env = get_multi_env(pop=pop, game_config=self.game_config)
msg = f"Repetitive evaluating games: {games}"
pop.log(msg, print_result=False)
multi_env.set_games(games)
# Iterate and evaluate over the games
saved = True
for iteration in range(n):
single_evaluation(
multi_env=multi_env,
parallel=parallel,
pop=pop,
unused_cpu=self.unused_cpu,
)
# Save the population
if (iteration + 1) % save_interval == 0:
pop.save()
saved = True
else:
saved = False
# Make sure that last iterations saves
if not saved: pop.save()
def trace(
population: Population,
games: list,
debug: bool = False,
duration: int = 0,
unused_cpu: int = 0,
):
"""Create a trace evaluation for the given population on the provided games."""
from environment.env_visualizing import VisualizingEnv
population.log("\n===> CREATING TRACES <===\n")
population.log(f"Creating traces for games: {games}")
game_config = deepcopy(population.config)
if duration > 0: game_config.game.duration = duration
visualizer = VisualizingEnv(
game_config=game_config,
games=games,
unused_cpu=unused_cpu,
)
visualizer.trace_genomes(pop=population, parallel=not debug)
def evaluate(
population: Population,
games: list,
genomes: list = None,
debug: bool = False,
duration: int = 0,
overwrite: bool = False,
unused_cpu: int = 0,
):
"""Evaluate the given population on the evaluation game-set."""
from environment.env_evaluation import EvaluationEnv
population.log("\n===> EVALUATING <===\n")
game_config = deepcopy(population.config)
if duration > 0: game_config.game.duration = duration
evaluator = EvaluationEnv(
game_config=game_config,
games=games,
unused_cpu=unused_cpu,
)
if genomes is None:
genomes = sorted(
[g for g in population.population.values()],
key=lambda x: x.fitness if x.fitness else 0,
reverse=True,
)[:max(
int(population.config.population.parent_selection *
len(population.population)), 1)]
evaluator.evaluate_genome_list(
genome_list=genomes, # Evaluate the ten best performing genomes
pop=population,
parallel=not debug,
overwrite=overwrite,
)
def visualize_score(pop: Population, pop_cache: Population, genome: Genome, d: int, range_width: int):
"""Visualize the score of the evaluated population."""
pop_cache.log(f"{pop_cache.name} - Fetching fitness scores...")
dim = (2 * range_width + 1)
pbar = tqdm(range((dim ** 2) * 3), desc="Fetching fitness scores")
genome_key = 0
# Fetching scores of reset-mutations
reset_scores = np.zeros((dim, dim))
for a in range(dim):
for b in range(dim):
reset_scores[a, b] = pop_cache.population[genome_key].fitness
genome_key += 1
pbar.update()
# Fetching scores of update-mutations
update_scores = np.zeros((dim, dim))
for a in range(dim):
for b in range(dim):
update_scores[a, b] = pop_cache.population[genome_key].fitness
genome_key += 1
pbar.update()
# Fetching scores of candidate-mutations
candidate_scores = np.zeros((dim, dim))
for a in range(dim):
for b in range(dim):
candidate_scores[a, b] = pop_cache.population[genome_key].fitness
genome_key += 1
pbar.update()
pbar.close()
# Visualize the result
pop_cache.log(f"{pop_cache.name} - Visualizing the result...")
# GRU-node needed for labels
genome.update_rnn_nodes(config=pop.config.genome)
gru_node = None
for node in genome.nodes.values():
if type(node) == GruNodeGene:
gru_node = node
# Create the points and retrieve data for the plot
points = [[x, y] for x in range(dim) for y in range(dim)]
points_normalized = [[(p1 + -range_width) / d, (p2 + -range_width) / d] for p1, p2 in points]
values_reset = [reset_scores[p[0], p[1]] for p in points]
values_update = [update_scores[p[0], p[1]] for p in points]
values_candidate = [candidate_scores[p[0], p[1]] for p in points]
# K-nearest neighbours with hops of 0.01 is performed
grid_x, grid_y = np.mgrid[-range_width / d:range_width / d:0.01, -range_width / d:range_width / d:0.01]
# Perform k-NN
data_reset = griddata(points_normalized, values_reset, (grid_x, grid_y), method='nearest')
data_update = griddata(points_normalized, values_update, (grid_x, grid_y), method='nearest')
data_candidate = griddata(points_normalized, values_candidate, (grid_x, grid_y), method='nearest')
# Create the plots
plt.figure(figsize=(15, 5))
plt.subplot(131)
plt.imshow(data_reset.T,
vmin=0,
vmax=1,
extent=(-range_width / d, range_width / d, -range_width / d, range_width / d),
origin='lower')
plt.title('Reset-gate mutation')
plt.xlabel(r'$\Delta W_{hr}$' + f' (init={round(gru_node.weight_hh[0, 0], 3)})')
plt.ylabel(r'$\Delta W_{xr}$' + f' (init={round(gru_node.weight_xh[0, 0], 3)})')
plt.subplot(132)
plt.imshow(data_update.T,
vmin=0,
vmax=1,
extent=(-range_width / d, range_width / d, -range_width / d, range_width / d),
origin='lower')
plt.title('Update-gate mutation')
plt.xlabel(r'$\Delta W_{hz}$' + f' (init={round(gru_node.weight_hh[1, 0], 3)})')
plt.ylabel(r'$\Delta W_{xz}$' + f' (init={round(gru_node.weight_xh[1, 0], 3)})')
plt.subplot(133)
plt.imshow(data_candidate.T,
vmin=0,
vmax=1,
extent=(-range_width / d, range_width / d, -range_width / d, range_width / d),
origin='lower')
plt.title('Candidate-state mutation')
plt.xlabel(r'$\Delta W_{hh}$' + f' (init={round(gru_node.weight_hh[2, 0], 3)})')
plt.ylabel(r'$\Delta W_{xh}$' + f' (init={round(gru_node.weight_xh[2, 0], 3)})')
# Store the plot
plt.tight_layout()
path = f"population{'_backup' if pop.use_backup else ''}/storage/{pop.folder_name}/{pop}/"
path = get_subfolder(path, 'images')
path = get_subfolder(path, 'gru_analysis')
plt.savefig(f"{path}{genome.key}.png")
plt.savefig(f"{path}{genome.key}.eps", format="eps")
plt.close()
# Create overview
pop_cache.log("Overview of results:")
log = dict()
# Overview: reset-mutation
max_index_reset, max_value_reset, min_index_reset, min_value_reset = None, 0, None, 1
for index, x in np.ndenumerate(reset_scores):
if x < min_value_reset: min_index_reset, min_value_reset = index, x
if x > max_value_reset: max_index_reset, max_value_reset = index, x
pop_cache.log(f"\tReset-gate mutation:")
pop_cache.log(f"\t > Maximum fitness: {round(max_value_reset, 2)} for index {max_index_reset!r}")
pop_cache.log(f"\t > Average fitness: {round(np.average(reset_scores), 2)}")
pop_cache.log(f"\t > Minimum fitness: {round(min_value_reset, 2)} for index {min_index_reset!r}")
log['Reset-gate maximum fitness'] = f"{round(max_value_reset, 2)} for index {max_index_reset!r}"
log['Reset-gate average fitness'] = f"{round(np.average(reset_scores), 2)}"
log['Reset-gate minimum fitness'] = f"{round(min_value_reset, 2)} for index {min_index_reset!r}"
# Overview: update-mutation
max_index_update, max_value_update, min_index_update, min_value_update = None, 0, None, 1
for index, x in np.ndenumerate(update_scores):
if x < min_value_update: min_index_update, min_value_update = index, x
if x > max_value_update: max_index_update, max_value_update = index, x
pop_cache.log(f"\tUpdate-gate mutation:")
pop_cache.log(f"\t > Maximum fitness: {round(max_value_update, 2)} for index {max_index_update!r}")
pop_cache.log(f"\t > Average fitness: {round(np.average(update_scores), 2)}")
pop_cache.log(f"\t > Minimum fitness: {round(min_value_update, 2)} for index {min_index_update!r}")
log['Update-gate maximum fitness'] = f"{round(max_value_update, 2)} for index {max_index_update!r}"
log['Update-gate average fitness'] = f"{round(np.average(update_scores), 2)}"
log['Update-gate minimum fitness'] = f"{round(min_value_update, 2)} for index {min_index_update!r}"
# Overview: candidate-mutation
max_index_candidate, max_value_candidate, min_index_candidate, min_value_candidate = None, 0, None, 1
for index, x in np.ndenumerate(candidate_scores):
if x < min_value_candidate: min_index_candidate, min_value_candidate = index, x
if x > max_value_candidate: max_index_candidate, max_value_candidate = index, x
pop_cache.log(f"\tCandidate-state mutation:")
pop_cache.log(f"\t > Maximum fitness: {round(max_value_candidate, 2)} for index {max_index_candidate!r}")
pop_cache.log(f"\t > Average fitness: {round(np.average(candidate_scores), 2)}")
pop_cache.log(f"\t > Minimum fitness: {round(min_value_candidate, 2)} for index {min_index_candidate!r}")
log['Candidate-state maximum fitness'] = f"{round(max_value_candidate, 2)} for index {max_index_candidate!r}"
log['Candidate-state average fitness'] = f"{round(np.average(candidate_scores), 2)}"
log['Candidate-state minimum fitness'] = f"{round(min_value_candidate, 2)} for index {min_index_candidate!r}"
update_dict(f'{path}{genome.key}.txt', log)
if args.genome:
visualize_genome(
debug=True,
genome=chosen_genome if chosen_genome else pop.best_genome,
population=pop,
)
if args.gru_analysis:
gru_analysis(
experiment_id=args.experiment,
genome=chosen_genome,
population=pop,
unused_cpu=args.unused_cpu,
)
if args.live:
live(
debug=args.debug,
game_id=game_ids_eval[0],
# game_id=30001,
duration=args.duration,
genome=chosen_genome if chosen_genome else pop.best_genome,
population=pop,
speedup=4,
)
except Exception as e:
pop.log(traceback.format_exc(), print_result=False)
raise e
finally:
process_killer('main.py') # Close all the terminated files
def train(
population: Population,
game_config: Config,
games: list,
iterations: int,
unused_cpu: int = 0,
save_interval: int = 10,
):
"""Train the population on the requested number of iterations. Manual adaptation of main's train()."""
population.log("\n===> TRAINING <===\n")
multi_env = get_multi_env(pop=population, game_config=game_config)
msg = f"Repetitive evaluating on games: {games} for {iterations} iterations"
population.log(msg, print_result=False)
# Iterate and evaluate over the games
saved = True
for iteration in range(iterations):
# Set and randomize the games
multi_env.set_games(games, noise=True)
# Prepare the generation's reporters for the generation
population.reporters.start_generation(gen=population.generation,
logger=population.log)
# Fetch the dictionary of genomes
genomes = list(iteritems(population.population))
# Initialize the evaluation-pool
pool = mp.Pool(mp.cpu_count() - unused_cpu)
manager = mp.Manager()
return_dict = manager.dict()
for genome in genomes:
pool.apply_async(func=multi_env.eval_genome,
args=(genome, return_dict))
pool.close() # Close the pool
pool.join() # Postpone continuation until everything is finished
# Calculate the fitness from the given return_dict
fitness = calc_pop_fitness(
fitness_cfg=population.config.evaluation,
game_cfg=game_config.game,
game_obs=return_dict,
gen=population.generation,
)
for i, genome in genomes:
genome.fitness = fitness[i]
# Gather and report statistics
best = None
for g in itervalues(population.population):
if best is None or g.fitness > best.fitness: best = g
population.reporters.post_evaluate(population=population.population,
species=population.species,
best_genome=best,
logger=population.log)
# Update the population's best_genome
genomes = sorted(population.population.items(),
key=lambda x: x[1].fitness,
reverse=True)
population.best_fitness[population.generation] = genomes[0][1].fitness
population.best_genome_hist[population.generation] = genomes[0]
population.best_genome = best
# Let population evolve
population.evolve()
# Update the genomes such all have one hidden node
for g in population.population.values():
n_hidden, _ = g.size()
while n_hidden < 1:
g.mutate_add_connection(population.config.genome)
n_hidden, _ = g.size()
# End generation
population.reporters.end_generation(population=population.population,
name=str(population),
species_set=population.species,
logger=population.log)
# Save the population
if (iteration + 1) % save_interval == 0:
population.save()
saved = True
else:
saved = False
# Make sure that last iterations saves
if not saved: population.save()
def main(
fitness,
prob_gru: float,
prob_gru_nr: float,
prob_gru_nu: float,
prob_simple_rnn: float,
train_iterations=0,
version=0,
unused_cpu=1,
):
"""
Run a population's configuration.
:param fitness: Fitness function used to evaluate the population
:param prob_gru: Probability of mutating towards a GRU-node
:param prob_gru_nr: Probability of mutating towards a GRU-NR-node
:param prob_gru_nu: Probability of mutating towards a GRU-NU-node
:param prob_simple_rnn: Probability of mutating towards a SimpleRNN-node
:param train_iterations: Number of training generations
:param version: Version of the model
:param unused_cpu: Number of CPUs not used during training
"""
# Re-configure the config-file
cfg = Config()
cfg.bot.angular_dir = []
cfg.bot.delta_dist_enabled = False
cfg.bot.dist_enabled = True
cfg.game.duration = 60 # 60 seconds should be enough to reach the target from each starting orientation
cfg.genome.node_add_prob = 0 # Do not change number of hidden nodes
cfg.genome.node_disable_prob = 0 # Do not change number of hidden nodes
cfg.population.pop_size = 512
cfg.population.compatibility_thr = 1. # Very small since all architectures have strictly one hidden node
# Let inputs apply to configuration
cfg.genome.rnn_prob_gru = prob_gru
cfg.genome.rnn_prob_gru_nr = prob_gru_nr
cfg.genome.rnn_prob_gru_nu = prob_gru_nu
cfg.genome.rnn_prob_simple_rnn = prob_simple_rnn
cfg.evaluation.fitness = fitness
cfg.update()
# Create the population
folder = get_folder(experiment_id=4)
name = get_name(cfg=cfg, version=version)
pop = Population(
name=name,
config=cfg,
folder_name=folder,
use_backup=False,
)
# Make sure that all of the genomes in the initial population have exactly one hidden node
if pop.generation == 0:
for g in pop.population.values():
g.mutate_add_node(pop.config.genome)
# Give overview of population
gru = cfg.genome.rnn_prob_gru
gru_nr = cfg.genome.rnn_prob_gru_nr
gru_nu = cfg.genome.rnn_prob_gru_nu
rnn = cfg.genome.rnn_prob_simple_rnn
msg = f"\n\n\n\n\n===> RUNNING EXPERIMENT 4 FOR THE FOLLOWING CONFIGURATION: <===" \
f"\n\t> fitness: {cfg.evaluation.fitness}" \
f"\n\t> GRU enabled: {gru > 0} (probability={round(gru, 2)})" \
f"\n\t> GRU-NR enabled: {gru_nr > 0} (probability={round(gru_nr, 2)})" \
f"\n\t> GRU-NU enabled: {gru_nu > 0} (probability={round(gru_nu, 2)})" \
f"\n\t> SRU enabled: {rnn > 0} (probability={round(rnn, 2)})" \
f"\n\t> Saving under folder: {folder}" \
f"\n\t> Training iterations: {train_iterations}\n"
pop.log(msg)
# Set games used for evaluation
games_train, games_eval = get_game_ids(experiment_id=4)
# Execute the requested segments
try:
train(
game_config=cfg,
games=games_train,
iterations=train_iterations,
population=pop,
unused_cpu=unused_cpu,
)
# Evaluate the trained population
evaluate(
games=games_eval,
population=pop,
unused_cpu=unused_cpu,
)
training_overview(population=pop, )
visualize_genome(
genome=pop.best_genome,
population=pop,
)
# Perform GRU-analysis if population is NEAT-GRU
if gru > 0:
gru_analysis(
population=pop,
unused_cpu=unused_cpu,
experiment_id=4,
)
monitor(
game_id=games_eval[0],
population=pop,
genome=pop.best_genome,
)
except Exception as e:
pop.log(traceback.format_exc(), print_result=False)
raise e
finally:
process_killer('run_population.py') # Close all the terminated files
def main(
fitness,
prob_gru: float,
prob_sru: float,
prob_lstm: float,
version=0,
unused_cpu=1,
):
"""
Run a population's configuration.
:param fitness: Fitness function used to evaluate the population
:param prob_gru: Probability of mutating towards a GRU-node
:param prob_sru: Probability of mutating towards a SRU-node
:param prob_lstm: Probability of mutating towards a LSTM-node
:param version: Version of the model
:param unused_cpu: Number of CPUs not used during training
"""
# Re-configure the config-file
cfg = Config()
cfg.bot.angular_dir = []
cfg.bot.delta_dist_enabled = False
cfg.bot.dist_enabled = True
cfg.game.duration = 60 # 60 seconds should be enough to reach the target from each starting orientation
cfg.population.pop_size = 512
# Let inputs apply to configuration
cfg.genome.rnn_prob_gru = prob_gru
cfg.genome.rnn_prob_simple_rnn = prob_sru
cfg.genome.rnn_prob_lstm = prob_lstm
cfg.evaluation.fitness = fitness
cfg.update()
# Copy population over from experiment1
name = get_name(cfg=cfg, version=version)
path_exp1 = f'population/storage/experiment1/{name}/'
if not os.path.exists(path_exp1):
raise Exception(
f"Experiment 1 must be executed first for population {name}, terminating experiment 2..."
)
# Population exists in experiment1, copy over to experiment2 (change experiment1 population's folder and save)
pop = Population(name=name,
config=cfg,
folder_name=get_folder(experiment_id=1),
use_backup=False)
assert pop.generation > 0 # Population is not new (redundant check)
folder = get_folder(experiment_id=2)
pop.folder_name = folder
pop.save() # Overrides pre-existing populations!
# Copy over all generations as well, since these are used during population evaluation
path = f"population{'_backup' if pop.use_backup else ''}/storage/{pop.folder_name}/{pop}/"
copy_tree(f"{path_exp1}generations", f"{path}generations")
# Give overview of population
gru = cfg.genome.rnn_prob_gru
sru = cfg.genome.rnn_prob_simple_rnn
lstm = cfg.genome.rnn_prob_lstm
msg = f"\n\n\n\n\n===> RUNNING EXPERIMENT 2 FOR THE FOLLOWING CONFIGURATION: <===" \
f"\n\t> fitness: {cfg.evaluation.fitness}" \
f"\n\t> GRU enabled: {gru > 0} (probability={round(gru, 2)})" \
f"\n\t> SRU enabled: {sru > 0} (probability={round(sru, 2)})" \
f"\n\t> LSTM enabled: {lstm > 0} (probability={round(lstm, 2)})" \
f"\n\t> Saving under folder: {folder}\n"
pop.log(msg)
# Set games used for evaluation
_, games_eval = get_game_ids(experiment_id=2)
# Execute the requested segments
try:
# Evaluate the trained population
evaluate(
games=games_eval,
population=pop,
unused_cpu=unused_cpu,
)
except Exception as e:
pop.log(traceback.format_exc(), print_result=False)
raise e
finally:
process_killer('run_population.py') # Close all the terminated files
def main(topology_id: int,
batch_size: int = 1000,
train_batch: int = 3,
min_finished: float = MIN_FINISHED,
unused_cpu: int = 2,
save_pop: bool = False,
use_backup: bool = False):
"""Run a population infinitely long and store all its good genomes."""
# Get the CSV used to store the results in
csv_path, csv_name, added = get_csv_path(topology_id, use_backup=use_backup, batch_size=batch_size)
# Create the population
name = csv_name if save_pop else 'dummy'
cfg = get_config()
folder = get_folder(experiment_id=6)
pop = Population(
name=name,
config=cfg,
folder_name=folder,
use_backup=use_backup,
overwrite=True, # Every iteration, create a new population from scratch
)
# Replace the population's initial population with the requested topologies genomes
for g_id in pop.population.keys(): pop.population[g_id] = get_genome(topology_id, g_id=g_id, cfg=cfg)
pop.species.speciate(config=pop.config,
population=pop.population,
generation=pop.generation,
logger=pop.log)
# Set games and environment used for training and evaluation
pop.log(f"\n\n\n===> RUNNING EXPERIMENT 6 <===\n")
games_train, games_eval = get_game_ids(experiment_id=6)
train_env = get_multi_env(config=cfg)
eval_env = get_multi_env(config=cfg)
eval_env.set_games(games_eval, noise=False)
# Keep training and evolving the network until the complete CSV is filled
last_saved = pop.generation
try:
while added < batch_size:
t = time.localtime()
pop.log(f"\n\n===> Selective genome creation at {added / batch_size * 100}%, "
f"storing in csv '{csv_path.split('/')[-1]}' "
f"({t.tm_hour:02d}h-{t.tm_min:02d}m-{t.tm_sec:02d}s) <===")
# Train the population
pop.log("\n===> Training <===")
for _ in tqdm(range(train_batch), desc="Training"):
train_env.set_games(games_train, noise=True)
genomes = list(iteritems(pop.population))
# Initialize the evaluation-pool
pool = mp.Pool(mp.cpu_count() - unused_cpu)
manager = mp.Manager()
return_dict = manager.dict()
for genome in genomes:
pool.apply_async(func=train_env.eval_genome, args=(genome, return_dict))
pool.close() # Close the pool
pool.join() # Postpone continuation until everything is finished
# Calculate the fitness from the given return_dict
fitness = calc_pop_fitness(
fitness_cfg=pop.config.evaluation,
game_cfg=cfg.game,
game_obs=return_dict,
gen=pop.generation,
)
for i, genome in genomes:
genome.fitness = fitness[i]
# Update the population's best_genome
best = None
for g in itervalues(pop.population):
if best is None or g.fitness > best.fitness: best = g
genomes = sorted(pop.population.items(), key=lambda x: x[1].fitness, reverse=True)
pop.best_fitness[pop.generation] = genomes[0][1].fitness
pop.best_genome_hist[pop.generation] = genomes[0]
pop.best_genome = best
pop.log(f"Best training fitness: {best.fitness}")
# Let population evolve
pop.evolve()
# Constraint each of the population's new genomes to the given topology
for g in pop.population.values():
enforce_topology(g, topology_id=topology_id)
# Save the population after training
if pop.generation - last_saved >= 100:
pop.save()
last_saved = pop.generation
# Evaluate the current population as was done in experiment6
pop.log("\n===> EVALUATING <===")
genomes = list(iteritems(pop.population))
pool = mp.Pool(mp.cpu_count() - unused_cpu)
manager = mp.Manager()
return_dict = manager.dict()
for genome in genomes:
pool.apply_async(func=eval_env.eval_genome, args=(genome, return_dict))
pool.close() # Close the pool
pool.join() # Postpone continuation until everything is finished
# Calculate the fitness from the given return_dict
finished = calc_finished_ratio(
fitness_cfg=cfg.evaluation,
game_obs=return_dict,
)
best = None
for i, genome in genomes:
genome.fitness = finished[i]
if best is None or finished[i] > best.fitness: best = genome
# Give evaluation overview of population
pop.log(f"Best evaluation finish ratio: {round(best.fitness, 2)}")
best_str = str(best).replace("\n", "\n\t")
best_str += "\n\t" + str(best.nodes[2]).replace("\n", "\n\t")
pop.log(f"Best genome: \n\t{best_str}")
sids = list(iterkeys(pop.species.species))
sids.sort()
msg = f"\nPopulation '{name}' has {len(pop.species.species):d} species:" \
f"\n\t specie age size finished stag " \
f"\n\t======== ===== ====== ========== ======"
pop.log(msg) if pop.log else print(msg)
for sid in sids:
s = pop.species.species[sid]
a = pop.generation - s.created
n = len(s.members)
sf = [g.fitness for g in s.members.values() if g.fitness]
f = "--" if len(sf) == 0 else f"{max(sf):.2f}"
st = pop.generation - s.last_improved
msg = f"\t{sid:^8} {a:^5} {n:^6} {f:^10} {st:^6}"
pop.log(msg) if pop.log else print(msg)
# Write the result to CSV
with open(csv_path, 'a', newline='') as f:
writer = csv.writer(f)
for _, g in genomes:
# Only write the genomes that exceed the minimum 'finished ratio' threshold!
if g.fitness >= min_finished:
writer.writerow(get_genome_parameters(g, topology_id=topology_id))
added += 1
finally:
# Remove the dummy population if it exists
pop.save()
path = f"population{'_backup' if use_backup else ''}/storage/{pop.folder_name}/dummy/"
if os.path.exists(path):
shutil.rmtree(path)
def create_genomes(genome: Genome, pop: Population, d: int, range_width: int):
"""Create mutations of the provided genome and inject these in the given population.."""
pop.log(f"{pop.name} - Setting up genomes...")
pbar = tqdm(range(((range_width + range_width + 1) ** 2) * 3), desc="Generating genomes")
genome_key = 0
gru_node_id = None
for node_id, node in genome.nodes.items():
if type(node) == GruNodeGene:
gru_node_id = node_id
break
# Create the reset-mutated genomes
for a in range(-range_width, range_width + 1):
w_xh = np.asarray([[a / d], [0], [0]])
for b in range(-range_width, range_width + 1):
w_hh = np.asarray([[b / d], [0], [0]])
# Add the specified genome to the population
new_genome = copy.deepcopy(genome)
new_genome.nodes[gru_node_id].weight_xh_full += w_xh
new_genome.nodes[gru_node_id].weight_hh += w_hh
new_genome.fitness = None
new_genome.key = genome_key
pop.population[genome_key] = new_genome
genome_key += 1
pbar.update()
# Create the update-mutated genomes
for a in range(-range_width, range_width + 1):
w_xh = np.asarray([[0], [a / d], [0]])
for b in range(-range_width, range_width + 1):
w_hh = np.asarray([[0], [b / d], [0]])
# Add the specified genome to the population
new_genome = copy.deepcopy(genome)
new_genome.nodes[gru_node_id].weight_xh_full += w_xh
new_genome.nodes[gru_node_id].weight_hh += w_hh
new_genome.fitness = None
new_genome.key = genome_key
pop.population[genome_key] = new_genome
genome_key += 1
pbar.update()
# Create the candidate-mutated genomes
for a in range(-range_width, range_width + 1):
w_xh = np.asarray([[0], [0], [a / d]])
for b in range(-range_width, range_width + 1):
w_hh = np.asarray([[0], [0], [b / d]])
# Add the specified genome to the population
new_genome = copy.deepcopy(genome)
new_genome.nodes[gru_node_id].weight_xh_full += w_xh
new_genome.nodes[gru_node_id].weight_hh += w_hh
new_genome.fitness = None
new_genome.key = genome_key
pop.population[genome_key] = new_genome
genome_key += 1
pbar.update()
pbar.close()
assert len(pop.population) == (((range_width - -range_width + 1) ** 2) * 3)
pop.save()
def main(pop_name: str,
version: int,
unused_cpu: int = 2,
use_backup: bool = False):
# Check if valid population name
if pop_name not in SUPPORTED:
raise Exception(f"Population '{pop_name}' not supported!")
# Create the population
cfg = get_config()
cfg.population.specie_elitism = 1
folder = get_folder(experiment_id=7)
pop = Population(
name=f'{pop_name}/v{version}',
config=cfg,
folder_name=folder,
use_backup=use_backup,
)
# Replace the population's initial population with the requested topologies genomes
if pop.generation == 0:
for g_id in pop.population.keys():
pop.population[g_id] = get_topology(pop_name, gid=g_id, cfg=cfg)
pop.species.speciate(config=pop.config,
population=pop.population,
generation=pop.generation,
logger=pop.log)
pop.log(f"\n\n\n===> RUNNING EXPERIMENT 7 <===\n")
# Set games and environment used for training and evaluation
games_train, games_eval = get_game_ids(experiment_id=7)
train_env = get_multi_env(config=cfg)
eval_env = get_multi_env(config=cfg)
eval_env.set_games(games_eval, noise=False)
solution_found = False
while not solution_found:
# Train the population for a single iteration
pop.log("\n===> TRAINING <===")
train_env.set_games(games_train, noise=True)
# Prepare the generation's reporters for the generation
pop.reporters.start_generation(gen=pop.generation, logger=pop.log)
# Fetch the dictionary of genomes
genomes = list(iteritems(pop.population))
# Initialize the evaluation-pool
pool = mp.Pool(mp.cpu_count() - unused_cpu)
manager = mp.Manager()
return_dict = manager.dict()
for genome in genomes:
pool.apply_async(func=train_env.eval_genome,
args=(genome, return_dict))
pool.close() # Close the pool
pool.join() # Postpone continuation until everything is finished
# Calculate the fitness from the given return_dict
fitness = calc_pop_fitness(
fitness_cfg=pop.config.evaluation,
game_cfg=cfg.game,
game_obs=return_dict,
gen=pop.generation,
)
for i, genome in genomes:
genome.fitness = fitness[i]
# Update the population's best_genome
best = None
for g in itervalues(pop.population):
if best is None or g.fitness > best.fitness: best = g
pop.reporters.post_evaluate(population=pop.population,
species=pop.species,
best_genome=best,
logger=pop.log)
# Update the population's best_genome
genomes = sorted(pop.population.items(),
key=lambda x: x[1].fitness,
reverse=True)
pop.best_fitness[pop.generation] = genomes[0][1].fitness
pop.best_genome_hist[pop.generation] = genomes[0]
pop.best_genome = best
# Let population evolve
evolve(pop, pop_name)
# End generation
pop.reporters.end_generation(population=pop.population,
name=str(pop),
species_set=pop.species,
logger=pop.log)
# Test if evaluation finds a solution for the new generation, impossible if fitness < 0.7
if pop.best_genome.fitness > 0.7 or pop.generation % 10 == 0:
pop.log("\n===> EVALUATING <===")
genomes = list(iteritems(pop.population))
pool = mp.Pool(mp.cpu_count() - unused_cpu)
manager = mp.Manager()
return_dict = manager.dict()
for genome in genomes:
pool.apply_async(func=eval_env.eval_genome,
args=(genome, return_dict))
pool.close() # Close the pool
pool.join() # Postpone continuation until everything is finished
# Calculate the fitness from the given return_dict
finished = calc_finished_ratio(
fitness_cfg=cfg.evaluation,
game_obs=return_dict,
)
best = None
for i, genome in genomes:
genome.fitness = finished[i]
if best is None or finished[i] > best.fitness: best = genome
pop.log(f"Best genome:\n{best}\n{best.nodes[2]}")
# Solution is found
if best.fitness == 1:
pop.best_genome = best
pop.log(f"Solution found!")
solution_found = True # End the outer while-loop
# Save the population with their evaluation results
pop.save()
def evaluate_and_evolve(
self,
pop: Population,
n: int = 1,
parallel=True,
save_interval: int = 1,
):
"""
Evaluate the population on the same set of games.
:param pop: Population object
:param n: Number of generations
:param parallel: Parallel the code (disable parallelization for debugging purposes)
:param save_interval: Indicates how often a population gets saved
"""
multi_env = get_multi_env(pop=pop, game_config=self.game_config)
msg = f"Repetitive evaluating on games: {self.games} for {n} iterations"
pop.log(msg, print_result=False)
# Iterate and evaluate over the games
saved = True
for iteration in range(n):
# Set and randomize the games
multi_env.set_games(self.games, noise=True)
# Prepare the generation's reporters for the generation
pop.reporters.start_generation(gen=pop.generation, logger=pop.log)
# Fetch the dictionary of genomes
genomes = list(iteritems(pop.population))
if parallel:
# Initialize the evaluation-pool
pool = mp.Pool(mp.cpu_count() - self.unused_cpu)
manager = mp.Manager()
return_dict = manager.dict()
for genome in genomes:
pool.apply_async(func=multi_env.eval_genome,
args=(genome, return_dict))
pool.close() # Close the pool
pool.join(
) # Postpone continuation until everything is finished
else:
return_dict = dict()
for genome in tqdm(genomes, desc="sequential training"):
multi_env.eval_genome(genome, return_dict)
# Calculate the fitness from the given return_dict
fitness = calc_pop_fitness(
fitness_cfg=pop.config.evaluation,
game_cfg=self.game_config.game,
game_obs=return_dict,
gen=pop.generation,
)
for i, genome in genomes:
genome.fitness = fitness[i]
# Gather and report statistics
best = None
for g in itervalues(pop.population):
if best is None or g.fitness > best.fitness: best = g
pop.reporters.post_evaluate(population=pop.population,
species=pop.species,
best_genome=best,
logger=pop.log)
# Update the population's best_genome
genomes = sorted(pop.population.items(),
key=lambda x: x[1].fitness,
reverse=True)
pop.best_fitness[pop.generation] = genomes[0][1].fitness
pop.best_genome_hist[pop.generation] = genomes[0]
pop.best_genome = best
# Let population evolve
pop.evolve()
# End generation
pop.reporters.end_generation(population=pop.population,
name=str(pop),
species_set=pop.species,
logger=pop.log)
# Save the population
if (iteration + 1) % save_interval == 0:
pop.save()
saved = True
else:
saved = False
# Make sure that last iterations saves
if not saved: pop.save()
def single_evaluation(multi_env, parallel: bool, pop: Population,
unused_cpu: int):
"""
Perform a single evaluation-iteration.
:param multi_env: Environment used to execute the game-simulation in
:param parallel: Boolean indicating if training happens in parallel or not
:param pop: Population used to evaluate on
:param unused_cpu: Number of CPU-cores not used during evaluation
"""
# Prepare the generation's reporters for the generation
pop.reporters.start_generation(gen=pop.generation, logger=pop.log)
# Fetch the dictionary of genomes
genomes = list(iteritems(pop.population))
if parallel:
pbar = tqdm(total=len(genomes), desc="parallel training")
# Initialize the evaluation-pool
pool = mp.Pool(mp.cpu_count() - unused_cpu)
manager = mp.Manager()
return_dict = manager.dict()
def cb(*_):
"""Update progressbar after finishing a single genome's evaluation."""
pbar.update()
for genome in genomes:
pool.apply_async(func=multi_env.eval_genome,
args=(genome, return_dict),
callback=cb)
pool.close() # Close the pool
pool.join() # Postpone continuation until everything is finished
pbar.close() # Close the progressbar
else:
return_dict = dict()
for genome in tqdm(genomes, desc="sequential training"):
multi_env.eval_genome(genome, return_dict)
# Calculate the fitness from the given return_dict
try:
fitness = calc_pop_fitness(
fitness_config=pop.config.evaluation,
game_observations=return_dict,
game_params=multi_env.get_game_params(),
generation=pop.generation,
)
for i, genome in genomes:
genome.fitness = fitness[i]
except Exception: # TODO: Fix! Sometimes KeyError in fitness (path problem)
pop.log(
f"Exception at fitness calculation: \n{traceback.format_exc()}",
print_result=False)
warnings.warn(
f"Exception at fitness calculation: \n{traceback.format_exc()}")
# Set fitness to zero for genomes that have no fitness set yet
for i, genome in genomes:
if not genome.fitness: genome.fitness = 0.0
# Gather and report statistics
best = None
for g in itervalues(pop.population):
if best is None or g.fitness > best.fitness: best = g
pop.reporters.post_evaluate(population=pop.population,
species=pop.species,
best_genome=best,
logger=pop.log)
# Update the population's best_genome
genomes = sorted(pop.population.items(),
key=lambda x: x[1].fitness,
reverse=True)
pop.best_genome_hist[
pop.generation] = genomes[:pop.config.population.genome_elitism]
if pop.best_genome is None or best.fitness > pop.best_genome.fitness:
pop.best_genome = best
# Let population evolve
pop.evolve()
# End generation
pop.reporters.end_generation(population=pop.population,
name=str(pop),
species_set=pop.species,
logger=pop.log)
def main(
fitness,
prob_gru: float,
prob_sru: float,
prob_lstm: float,
train_iterations=0,
version=0,
unused_cpu=1,
):
"""
Run a population's configuration.
:param fitness: Fitness function used to evaluate the population
:param prob_gru: Probability of mutating towards a GRU-node
:param prob_sru: Probability of mutating towards a SRU-node
:param prob_lstm: Probability of mutating towards a LSTM-node
:param train_iterations: Number of training generations
:param version: Version of the model
:param unused_cpu: Number of CPUs not used during training
"""
# Re-configure the config-file
cfg = Config()
cfg.bot.angular_dir = []
cfg.bot.delta_dist_enabled = False
cfg.bot.dist_enabled = True
cfg.game.duration = 60 # 60 seconds should be enough to reach the target from each starting orientation
cfg.population.pop_size = 512
# Let inputs apply to configuration
cfg.genome.rnn_prob_gru = prob_gru
cfg.genome.rnn_prob_simple_rnn = prob_sru
cfg.genome.rnn_prob_lstm = prob_lstm
cfg.evaluation.fitness = fitness
cfg.update()
# Create the population
folder = get_folder(experiment_id=1)
name = get_name(cfg=cfg, version=version)
pop = Population(
name=name,
config=cfg,
folder_name=folder,
use_backup=False,
)
# Give overview of population
gru = cfg.genome.rnn_prob_gru
sru = cfg.genome.rnn_prob_simple_rnn
lstm = cfg.genome.rnn_prob_lstm
msg = f"\n\n\n\n\n===> RUNNING EXPERIMENT 1 FOR THE FOLLOWING CONFIGURATION: <===" \
f"\n\t> fitness: {cfg.evaluation.fitness}" \
f"\n\t> GRU enabled: {gru > 0} (probability={round(gru, 2)})" \
f"\n\t> SRU enabled: {sru > 0} (probability={round(sru, 2)})" \
f"\n\t> LSTM enabled: {lstm > 0} (probability={round(lstm, 2)})" \
f"\n\t> Saving under folder: {folder}" \
f"\n\t> Training iterations: {train_iterations}\n"
pop.log(msg)
# Set games used for evaluation
games_train, games_eval = get_game_ids(experiment_id=1)
# Execute the requested segments
try:
train(
debug=False,
games=games_train,
iterations=train_iterations,
population=pop,
unused_cpu=unused_cpu,
)
# Evaluate the trained population
evaluate(
games=games_eval,
population=pop,
unused_cpu=unused_cpu,
)
training_overview(population=pop, )
visualize_genome(
genome=pop.best_genome,
population=pop,
)
except Exception as e:
pop.log(traceback.format_exc(), print_result=False)
raise e
finally:
process_killer('run_population.py') # Close all the terminated files
