def _new_model():
return rcfr.DeepRcfrModel(
_GAME,
num_hidden_layers=1,
num_hidden_units=13,
num_hidden_factors=1,
use_skip_connections=True)
def rcfr_train(unused_arg):
tf.enable_eager_execution()
game = pyspiel.load_game(FLAGS.game, {"players": pyspiel.GameParameter(2)})
models = [
rcfr.DeepRcfrModel(
game,
num_hidden_layers=1,
num_hidden_units=64 if FLAGS.game == "leduc_poker" else 13,
num_hidden_factors=1,
use_skip_connections=True) for _ in range(game.num_players())
]
patient = rcfr.RcfrSolver(game, models, False, True)
exploit_history = list()
exploit_idx = list()
def _train(model, data):
data = data.shuffle(1000)
data = data.batch(12)
#data = data.repeat(1)
optimizer = tf.keras.optimizers.Adam(lr=0.005, amsgrad=True)
for x, y in data:
optimizer.minimize(
lambda: tf.losses.huber_loss(y, model(x)), # pylint: disable=cell-var-from-loop
model.trainable_variables)
agent_name = "rcfr"
checkpoint = datetime.now()
for iteration in range(FLAGS.episodes):
if (iteration % 100) == 0:
delta = datetime.now() - checkpoint
conv = pyspiel.exploitability(game, patient.average_policy())
exploit_idx.append(iteration)
exploit_history.append(conv)
print(
"[RCFR] Iteration {} exploitability {} - {} seconds since last checkpoint"
.format(iteration, conv, delta.seconds))
checkpoint = datetime.now()
patient.evaluate_and_update_policy(_train)
pickle.dump([exploit_idx, exploit_history],
open(
FLAGS.game + "_" + agent_name + "_" + str(FLAGS.episodes) +
".dat", "wb"))
now = datetime.now()
policy = patient.average_policy()
for pid in [1, 2]:
policy_to_csv(
game, policy, f"policies/policy_" +
now.strftime("%m-%d-%Y_%H-%M") + "_" + agent_name + "_" +
str(pid + 1) + "_+" + str(FLAGS.episodes) + "episodes.csv")
def main(_):
game = pyspiel.load_game(FLAGS.game,
{"players": pyspiel.GameParameter(FLAGS.players)})
models = []
for _ in range(game.num_players()):
models.append(
rcfr.DeepRcfrModel(
game,
num_hidden_layers=FLAGS.num_hidden_layers,
num_hidden_units=FLAGS.num_hidden_units,
num_hidden_factors=FLAGS.num_hidden_factors,
use_skip_connections=FLAGS.use_skip_connections))
if FLAGS.buffer_size > 0:
solver = rcfr.ReservoirRcfrSolver(
game,
models,
FLAGS.buffer_size,
truncate_negative=FLAGS.truncate_negative)
else:
solver = rcfr.RcfrSolver(game,
models,
truncate_negative=FLAGS.truncate_negative,
bootstrap=FLAGS.bootstrap)
def _train_fn(model, data):
"""Train `model` on `data`."""
data = data.shuffle(FLAGS.batch_size * 10)
data = data.batch(FLAGS.batch_size)
data = data.repeat(FLAGS.num_epochs)
optimizer = tf.keras.optimizers.Adam(lr=FLAGS.step_size, amsgrad=True)
@tf.function
def _train():
for x, y in data:
optimizer.minimize(
lambda: tf.compat.v1.losses.huber_loss(
y, model(x), delta=0.01), # pylint: disable=cell-var-from-loop
model.trainable_variables)
_train()
# End of _train_fn
for i in range(FLAGS.iterations):
solver.evaluate_and_update_policy(_train_fn)
if i % FLAGS.print_freq == 0:
conv = pyspiel.exploitability(game, solver.average_policy())
print("Iteration {} exploitability {}".format(i, conv))