def get_pop_elos_agents_stats(model_dir, flags):
pop = load_pop(model_dir)
main_pop = []
elos = []
agents = []
stats = []
for p in pop:
if "nemesis" in p:
flags["has_nemesis"] = True
continue
elif "survivor" in p:
flags["has_survivor"] = True
continue
main_pop.append(p)
stat = load_stats(model_dir, p)
elos.append(last_elo(stat))
agents.append(last_model_path(model_dir, p, stat))
stats.append(stat)
return list(zip(main_pop, elos, agents, stats))
else:
#print(i_stats)
i_stats["elo"][str(i_stats["last_eval_steps"]
)] = last_elo(i_stats) + elo_changes[i]
i_stats["last_elo_change_steps"] = i_stats["num_steps"]
if __name__ == "__main__":
# Setup command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("model_dir",
type=str,
help="Base directory for agent models")
args = parser.parse_args()
print(args, flush=True)
if not (args.model_dir[-1] == '/' or args.model_dir[-1] == '\\'):
args.model_dir = args.model_dir + "/"
if not os.path.isdir(args.model_dir):
raise FileNotFoundError(
"Base directory for agent models is not a folder")
pop = train.load_pop(args.model_dir)
print("Consolidating training population:", pop, flush=True)
all_stats = []
for p in pop:
all_stats.append(train.load_stats(args.model_dir, p))
consolidate_results(pop, all_stats)
make_elo_changes(pop, all_stats)
for p, s in zip(pop, all_stats):
train.save_stats(args.model_dir, p, s)
def ai_matchmaking_eval(args):
PLAYER_1=0
PLAYER_2=1
if args.model_dir != '/':
args.model_dir += '/'
pop = load_pop(args.model_dir)
all_stats = {}
for p in pop:
all_stats[p] = load_stats(args.model_dir, p)
all_opps = list(all_stats.keys())
all_elos = []
for opp in all_opps:
all_elos.append(int(avg_elo(all_stats[opp], avg_len=args.avg_len)))
if args.agent_dir[-1] != '/':
args.agent_dir += '/'
if not os.path.exists(args.agent_dir):
os.mkdir(args.agent_dir)
if ".txt" in args.agent_id:
with open(args.agent_dir + args.agent_id, 'r') as name_file:
args.agent_id = name_file.readlines()[0]
agent_stats = load_stats(args.agent_dir, args.agent_id)
agent = PPO.load(curr_model_path(args.agent_dir, args.agent_id, agent_stats))
image_based = agent_stats["image_based"]
env_p = agent_stats["env_p"]
args.num_envs = min(args.num_envs, len(all_opps))
env_idx_to_opp_idx = [0 for _ in range(args.num_envs)]
try:
env_stack = make_ai_matchmaker_eval_stack(args.game_path, args.base_port, image_based, args.level_path, env_p, args.num_envs)
for n in range(args.N_games):
print("Agent Elo before iteration", n, ':', agent_stats["elo"]["value"][-1], flush=True)
elo_delta = 0.
for i in range(args.num_envs):
env_stack.env_method("load_new_opp", 0, opp_fp(args.model_dir, all_opps[i]), 0, indices=[i])
env_idx_to_opp_idx[i] = i
del i
states = env_stack.reset()
envs_done = []
next_i = args.num_envs
prog_bar = tqdm(range(len(all_opps)), file=sys.stdout)
while not all([tmp == -1 for tmp in env_idx_to_opp_idx]):
#print("Worker", args.seed, "current opps:", env_idx_to_opp_idx, flush=True)
reset_states = env_stack.env_method("reset", indices = envs_done)
for state,env_idx in zip(reset_states, envs_done):
states[env_idx] = state
envs_done = []
while len(envs_done) < 1:
actions, _ = agent.predict(states)
states, _, dones, infos = env_stack.step(actions)
if any(dones):
for j,done in enumerate(dones):
if done:
# Record elo change if needed
if env_idx_to_opp_idx[j] != -1:
win_rate = .5
if "winner" in infos[j]:
if infos[j]["winner"] == PLAYER_1:
win_rate = 1.
elif infos[j]["winner"] == PLAYER_2:
win_rate = 0.
elo_delta += elo_change(agent_stats["elo"]["value"][-1], all_elos[env_idx_to_opp_idx[j]], args.K, win_rate)[0]
prog_bar.update()
# Load next opponent if needed
if next_i < len(all_opps):
env_stack.env_method("load_new_opp", 0, opp_fp(args.model_dir, all_opps[next_i]), 0, indices=[j])
env_idx_to_opp_idx[j] = next_i
next_i += 1
else:
env_idx_to_opp_idx[j] = -1
envs_done.append(j)
prog_bar.close()
agent_stats["elo"]["value"][-1] += int(elo_delta)
print("Final agent Elo:", agent_stats["elo"]["value"][-1], flush=True)
save_stats(args.agent_dir, args.agent_id, agent_stats)
finally:
env_stack.close()
def record_results(model_dir, agent_id, results):
agent_stats = train.load_stats(model_dir, agent_id)
agent_stats["last_eval_steps"] = agent_stats["num_steps"]
agent_stats["performance"][str(agent_stats["num_steps"])] = results
train.save_stats(model_dir, agent_id, agent_stats)
def ai_matchmaking(args):
set_random_seed(args.seed)
if args.model_dir != '/':
args.model_dir += '/'
pop = load_pop(args.model_dir)
all_stats = {}
for p in pop:
all_stats[p] = load_stats(args.model_dir, p)
all_opps = sorted_keys(all_stats)
all_opps.reverse()
all_elos = []
for opp in all_opps:
all_elos.append(int(avg_elo(all_stats[opp], avg_len=args.avg_len)))
if args.agent_dir[-1] != '/':
args.agent_dir += '/'
if not os.path.exists(args.agent_dir):
os.mkdir(args.agent_dir)
load_agent = True
if args.agent_id:
if ".txt" in args.agent_id and os.path.exists(args.agent_dir +
args.agent_id):
with open(args.agent_dir + args.agent_id, 'r') as name_file:
agent_name = name_file.readlines()[0]
agent_stats = load_stats(args.agent_dir, agent_name)
elif not ".txt" in args.agent_id:
agent_name = args.agent_id
agent_stats = load_stats(args.agent_dir, agent_name)
else:
agent_name, agent_stats = init_agent(args)
load_agent = False
else:
agent_name, agent_stats = init_agent(args)
load_agent = False
try:
env_stack = make_ai_matchmaker_stack(
all_stats,
all_opps,
all_elos,
args.game_path,
args.model_dir,
base_port=args.base_port,
image_based=args.image_based,
level_path=args.level_path,
env_p=args.env_p,
time_reward=args.time_reward,
num_envs=args.num_envs,
matchmaking_mode=args.mm,
win_loss_ratio=args.win_loss_ratio)
v = 1 if args.verbose else 0
if load_agent:
agent = PPO.load(args.agent_dir + agent_name + '/' + agent_name +
'_' + str(agent_stats["num_steps"]),
env=env_stack)
else:
agent = save_new_model(agent_name,
env_stack,
args.num_envs,
args.agent_dir,
image_based=args.image_based,
image_pretrain=args.image_pretrain,
verbose=v,
w=args.w)
agent.learn(total_timesteps=args.num_steps)
agent_stats["num_steps"] += args.num_steps
agent_base = args.agent_dir + agent_name + '/'
agent_save_path = agent_base + agent_name + '_' + str(
agent_stats["num_steps"])
os.system("zip " + agent_base + "archive.zip " + agent_base +
"*_*.zip")
os.system("rm " + agent_base + "*_*.zip")
agent.save(agent_save_path)
agent_avg_elo = env_stack.env_method("get_agent_elo")
agent_avg_elo = sum(agent_avg_elo) / len(agent_avg_elo)
agent_stats["elo"]["steps"].append(agent_stats["num_steps"])
agent_stats["elo"]["value"].append(agent_avg_elo)
save_stats(args.agent_dir, agent_name, agent_stats)
if not load_agent:
with open(args.agent_dir + "name_" + str(args.seed) + ".txt",
'w') as name_file:
name_file.write(agent_name)
finally:
env_stack.close()
def record_results(model_dir, agent_id, results):
agent_stats = train.load_stats(model_dir, agent_id)
agent_stats["win_rates"][str(agent_stats["curr_iter"])] = results
agent_stats["curr_iter"] += 1
train.save_stats(model_dir, agent_id, agent_stats)
parser.add_argument("--total_workers", type=int, default=1, help="Total number of workers (for parallel training)")
parser.add_argument("--from_right", action="store_true", help="Indicates that data will be collected where player 1 starts on the right")
args = parser.parse_args()
print(args)
if not (args.model_dir[-1] == '/' or args.model_dir[-1] == '\\'):
args.model_dir = args.model_dir + "/"
if not (args.local_pop_dir[-1] == '/' or args.local_pop_dir[-1] == '\\'):
args.local_pop_dir = args.local_pop_dir + "/"
print("Worker", args.worker_idx, "got here", 1, flush=True)
pop = load_pop(args.local_pop_dir)
pop_stats = []
for opp in pop:
pop_stats.append(load_stats(args.local_pop_dir, opp))
my_pop = subset_pop(pop, args.worker_idx, args.total_workers)
print("Worker", args.worker_idx, "got here", 2, flush=True)
for port,p in enumerate(my_pop):
p_idx = pop.index(p)
p_model = PPO.load(curr_model_path(args.local_pop_dir, p, pop_stats[pop.index(p)]))
traj_set = np.full((len(pop), args.N, args.max_len+1, 12*pop_stats[p_idx]["env_p"], 20*pop_stats[p_idx]["env_p"], 3), 255, dtype=np.uint8)
info_set = np.full((len(pop), args.N), -1, dtype=np.int16)
print("Worker", args.worker_idx, "got here", 3, flush=True)
try:
env = TankEnv(args.game_path,
opp_fp_and_elo=[],
def human_matchmaking(args):
WINS = 0
LOSSES = 1
GAMES = 2
pop = load_pop(args.model_dir)
all_stats = {}
for p in pop:
all_stats[p] = load_stats(args.model_dir, p)
all_opps = sorted_keys(all_stats)
all_opps.reverse()
all_elos = []
for opp in all_opps:
all_elos.append(int(avg_elo(all_stats[opp], avg_len=args.avg_len)))
human_stats = get_human_stats(args.human_db)
current_opp_idx = len(all_elos) // 2
current_opp = all_opps[current_opp_idx]
current_opp_elo = all_elos[current_opp_idx]
human_elo = human_stats["elo"][-1] if len(
human_stats["elo"]) > 0 else current_opp_elo
try:
env = TankEnv(args.game_path,
opp_fp_and_elo=[(opp_fp(args.model_dir,
current_opp), current_opp_elo)],
game_port=args.base_port,
my_port=args.my_port,
image_based=args.image_based,
level_path=args.level_path,
p=args.env_p)
print("Starting matchmaking")
while human_elo <= all_elos[-1]:
print("Current opp:", current_opp)
print("Opp elo:", current_opp_elo)
print("Human elo:", human_elo)
score = play_match(env, args.num_games)
human_win_rate = (
(score[WINS] - score[LOSSES]) / sum(score) + 1) / 2
K = 16
human_elo_change, _ = elo_change(human_elo, current_opp_elo, K,
human_win_rate)
human_elo += int(human_elo_change)
human_stats["elo"].append(human_elo)
if not current_opp in human_stats["win_rate"]:
human_stats["win_rate"][current_opp] = [0, 0, 0]
human_stats["win_rate"][current_opp][WINS] += score[WINS]
human_stats["win_rate"][current_opp][LOSSES] += score[LOSSES]
human_stats["win_rate"][current_opp][GAMES] += sum(score)
D = 5.
current_opp_idx = elo_based_choice(all_elos, human_elo, D)
current_opp = all_opps[current_opp_idx]
current_opp_elo = all_elos[current_opp_idx]
env.load_new_opp(0, opp_fp(args.model_dir, current_opp),
current_opp_elo)
print("CONGRATS, YOU ARE BETTER THAN ALL THE AGENTS!")
finally:
env.close()