def create_envs(
num_env,
seed,
num_player,
hand_size,
bomb,
explore_eps,
max_len,
sad,
shuffle_obs,
shuffle_color,
):
games = []
for game_idx in range(num_env):
params = {
"players": str(num_player),
"hand_size": str(hand_size),
"seed": str(seed + game_idx),
"bomb": str(bomb),
}
game = hanalearn.HanabiEnv(
params,
explore_eps,
max_len,
sad,
shuffle_obs,
shuffle_color,
False,
)
games.append(game)
return games
def create_train_env(
method,
seed,
num_thread,
num_game_per_thread,
actor_cons,
max_len,
num_player,
bomb,
greedy_extra,
):
assert method in ["vdn", "iql"]
context = rela.Context()
games = []
actors = []
threads = []
print("training with bomb: %d" % bomb)
for thread_idx in range(num_thread):
env = rela.VectorEnv()
for game_idx in range(num_game_per_thread):
unique_seed = seed + game_idx + thread_idx * num_game_per_thread
game = hanalearn.HanabiEnv(
{
"players": str(num_player),
"seed": str(unique_seed),
"bomb": str(bomb),
},
max_len,
greedy_extra,
False,
)
games.append(game)
env.append(game)
assert max_len > 0
if method == "vdn":
# assert len(actor_cons) == 1
actor = actor_cons(thread_idx)
actors.append(actor)
thread = hanalearn.HanabiVDNThreadLoop(actor, env, False)
else:
assert len(actor_cons) == num_player
env_actors = []
for i in range(num_player):
env_actors.append(actor_cons[i](thread_idx))
actors.extend(env_actors)
thread = hanalearn.HanabiIQLThreadLoop(env_actors, env, False)
threads.append(thread)
context.push_env_thread(thread)
print("Finished creating environments with %d games and %d actors" %
(len(games), len(actors)))
return context, games, actors, threads
def create_eval_env(
seed,
num_thread,
model_lockers,
eval_eps,
num_player,
bomb,
greedy_extra,
log_prefix=None,
):
context = rela.Context()
games = []
for i in range(num_thread):
game = hanalearn.HanabiEnv(
{
"players": str(num_player),
"seed": str(seed + i),
"bomb": str(bomb),
},
-1,
greedy_extra,
False,
)
games.append(game)
env = rela.VectorEnv()
env.append(game)
env_actors = []
for j in range(num_player):
env_actors.append(rela.R2D2Actor(model_lockers[j], 1, eval_eps))
if log_prefix is None:
thread = hanalearn.HanabiIQLThreadLoop(env_actors, env, True)
else:
log_file = os.path.join(log_prefix, "game%d.txt" % i)
thread = hanalearn.HanabiIQLThreadLoop(env_actors, env, True,
log_file)
context.push_env_thread(thread)
return context, games