def args_gnubg(args):
model_agent0 = args.model_agent0
model_type = args.type
hidden_units_agent0 = args.hidden_units_agent0
n_episodes = args.episodes
host = args.host
port = args.port
difficulty = args.difficulty
if path_exists(model_agent0):
# assert os.path.exists(model_agent0), print("The path {} doesn't exists".format(model_agent0))
if model_type == 'nn':
net0 = TDGammon(hidden_units=hidden_units_agent0,
lr=0.1,
lamda=None,
init_weights=False)
else:
net0 = TDGammonCNN(lr=0.0001)
net0.load(checkpoint_path=model_agent0,
optimizer=None,
eligibility_traces=False)
gnubg_interface = GnubgInterface(host=host, port=port)
gnubg_env = GnubgEnv(gnubg_interface,
difficulty=difficulty,
model_type=model_type)
evaluate_vs_gnubg(agent=TDAgentGNU(WHITE,
net=net0,
gnubg_interface=gnubg_interface),
env=gnubg_env,
n_episodes=n_episodes)
def args_gnubg(args):
model_agent0 = args.model_agent0
model_type = args.type
hidden_units_agent0 = args.hidden_units_agent0
n_episodes = args.episodes
host = args.host
port = args.port
difficulty = args.difficulty
iterations = args.iterations
experiment = "/saved_models/" + model_agent0
folder = os.getcwd() + experiment
directory = os.fsencode(folder)
max_it_sizes = 0
for file in os.listdir(directory):
filename = os.fsdecode(file)
# Find by chosen iteration amount
if iterations:
if filename.endswith("{}.tar".format(str(iterations))):
final_file = filename
break
# Otherwise the biggest iteration amount
else:
if filename.endswith(".tar"):
size = filename.split('_')[-1][:-4]
if int(size) > max_it_sizes:
max_it_sizes = int(size)
if filename.endswith("{}.tar".format(str(max_it_sizes))):
final_file = filename
if path_exists(folder + '/' + final_file):
# assert os.path.exists(model_agent0), print("The path {} doesn't exists".format(model_agent0))
if model_type == 'nn':
net0 = TDGammon(hidden_units=hidden_units_agent0,
lr=0.1,
lamda=None,
init_weights=False)
else:
net0 = TDGammonCNN(lr=0.0001)
net0.load(checkpoint_path=folder + '/' + final_file,
optimizer=None,
eligibility_traces=False)
gnubg_interface = GnubgInterface(host=host, port=port)
gnubg_env = GnubgEnv(gnubg_interface,
difficulty=difficulty,
model_type=model_type)
evaluate_vs_gnubg(agent=TDAgentGNU(WHITE,
net=net0,
gnubg_interface=gnubg_interface),
env=gnubg_env,
n_episodes=n_episodes,
difficulty=difficulty,
model=model_agent0)
def args_plot(args, parser):
'''
This method is used to plot the number of time an agent wins when it plays against an opponent.
Instead of evaluating the agent during training (it can require some time and slow down the training), I decided to plot the wins separately, loading the different
model saved during training.
For example, suppose I run the training for 100 games and save my model every 10 games.
Later I will load these 10 models, and for each of them, I will compute how many times the agent would win against an opponent.
:return: None
'''
src = args.save_path
hidden_units = args.hidden_units
n_episodes = args.episodes
opponents = args.opponent.split(',')
host = args.host
port = args.port
difficulties = args.difficulty.split(',')
model_type = args.type
if path_exists(src):
# assert os.path.exists(src), print("The path {} doesn't exists".format(src))
for d in difficulties:
if d not in [
'beginner', 'intermediate', 'advanced', 'world_class'
]:
parser.error(
"--difficulty should be (one or more of) 'beginner','intermediate', 'advanced' ,'world_class'"
)
dst = args.dst
if 'gnubg' in opponents and (not host or not port):
parser.error(
"--host and --port are required when 'gnubg' is specified in --opponent"
)
for root, dirs, files in os.walk(src):
global_step = 0
files = sorted(files)
writer = SummaryWriter(dst)
for file in files:
if ".tar" in file:
print("\nLoad {}".format(os.path.join(root, file)))
if model_type == 'nn':
net = TDGammon(hidden_units=hidden_units,
lr=0.1,
lamda=None,
init_weights=False)
env = gym.make('gym_backgammon:backgammon-v0')
else:
net = TDGammonCNN(lr=0.0001)
env = gym.make('gym_backgammon:backgammon-pixel-v0')
net.load(checkpoint_path=os.path.join(root, file),
optimizer=None,
eligibility_traces=False)
if 'gnubg' in opponents:
tag_scalar_dict = {}
gnubg_interface = GnubgInterface(host=host, port=port)
for difficulty in difficulties:
gnubg_env = GnubgEnv(gnubg_interface,
difficulty=difficulty,
model_type=model_type)
wins = evaluate_vs_gnubg(agent=TDAgentGNU(
WHITE,
net=net,
gnubg_interface=gnubg_interface),
env=gnubg_env,
n_episodes=n_episodes)
tag_scalar_dict[difficulty] = wins[WHITE]
writer.add_scalars('wins_vs_gnubg/', tag_scalar_dict,
global_step)
with open(root + '/results.txt', 'a') as f:
print("{};".format(file) + str(tag_scalar_dict),
file=f)
if 'random' in opponents:
tag_scalar_dict = {}
agents = {
WHITE: TDAgent(WHITE, net=net),
BLACK: RandomAgent(BLACK)
}
wins = evaluate_agents(agents, env, n_episodes)
tag_scalar_dict['random'] = wins[WHITE]
writer.add_scalars('wins_vs_random/', tag_scalar_dict,
global_step)
global_step += 1
writer.close()