def test_generate_saved_game_proto(self):
""" Tests the generate_saved_game_proto method """
from diplomacy_research.utils.tensorflow import tf
hparams = self.parse_flags(load_args() + load_value_args() + load_draw_args())
# Generating model
graph = tf.Graph()
with graph.as_default():
dataset = QueueDataset(batch_size=32, dataset_builder=BaseDatasetBuilder())
model = PolicyModel(dataset, hparams)
model = ValueModel(model, dataset, hparams)
model = DrawModel(model, dataset, hparams)
model.finalize_build()
adapter = PolicyAdapter(dataset, graph, tf.Session(graph=graph))
advantage_fn = MonteCarlo(gamma=0.99)
reward_fn = DefaultRewardFunction()
# Creating players
player = ModelBasedPlayer(adapter)
rule_player = RuleBasedPlayer(easy_ruleset)
players = [player, player, player, player, player, player, rule_player]
def env_constructor(players):
""" Env constructor """
env = gym.make('DiplomacyEnv-v0')
env = LimitNumberYears(env, 5)
env = RandomizePlayers(env, players)
env = AutoDraw(env)
return env
# Generating game
saved_game_proto = yield generate_trajectory(players, reward_fn, advantage_fn, env_constructor)
# Validating game
assert saved_game_proto.id
assert len(saved_game_proto.phases) >= 10
# Validating policy details
for phase in saved_game_proto.phases:
for power_name in phase.policy:
nb_locs = len(phase.policy[power_name].locs)
assert (len(phase.policy[power_name].tokens) == nb_locs * TOKENS_PER_ORDER # Token-based
or len(phase.policy[power_name].tokens) == nb_locs) # Order-based
assert len(phase.policy[power_name].log_probs) == len(phase.policy[power_name].tokens)
assert phase.policy[power_name].draw_action in (True, False)
assert 0. <= phase.policy[power_name].draw_prob <= 1.
# Validating assignments
assert len(saved_game_proto.assigned_powers) == NB_POWERS
# Validating rewards and returns
assert saved_game_proto.reward_fn == DefaultRewardFunction().name
for power_name in saved_game_proto.assigned_powers:
assert len(saved_game_proto.rewards[power_name].value) == len(saved_game_proto.phases) - 1
assert len(saved_game_proto.returns[power_name].value) == len(saved_game_proto.phases) - 1
def launch_adapter():
""" Launches the tests """
testable_class = PolicyAdapterTestSetup(policy_model_ctor=PolicyModel,
value_model_ctor=ValueModel,
draw_model_ctor=None,
dataset_builder=BaseDatasetBuilder(),
policy_adapter_ctor=PolicyAdapter,
load_policy_args=load_args,
load_value_args=load_value_args,
load_draw_args=None,
strict=False)
testable_class.run_tests()
def build_adapter(self):
""" Builds adapter """
from diplomacy_research.utils.tensorflow import tf
hparams = self.parse_flags(load_args())
# Generating model
dataset = QueueDataset(batch_size=32,
dataset_builder=BaseDatasetBuilder())
model = PolicyModel(dataset, hparams)
model.finalize_build()
model.add_meta_information(
{'state_value': model.outputs['logits'][:, 0, 0]})
self.adapter = PolicyAdapter(dataset, self.graph,
tf.Session(graph=self.graph))
self.create_advantage()
# Setting cache path
filename = '%s_savedgame.pbz' % self.model_type
self.saved_game_cache_path = os.path.join(HOME_DIR, '.cache',
'diplomacy', filename)