def cfr_train(unused_arg):
exploit_history = list()
exploit_idx = list()
tf.enable_eager_execution()
game = pyspiel.load_game(FLAGS.game, {"players": pyspiel.GameParameter(2)})
agent_name = "cfr"
cfr_solver = cfr.CFRSolver(game)
checkpoint = datetime.now()
for ep in range(FLAGS.episodes):
cfr_solver.evaluate_and_update_policy()
if ep % 100 == 0:
delta = datetime.now() - checkpoint
conv = exploitability.exploitability(game,
cfr_solver.average_policy())
exploit_idx.append(ep)
exploit_history.append(conv)
print(
"Iteration {} exploitability {} - {} seconds since last checkpoint"
.format(ep, conv, delta.seconds))
checkpoint = datetime.now()
pickle.dump([exploit_idx, exploit_history],
open(
FLAGS.game + "_" + agent_name + "_" + str(FLAGS.episodes) +
".dat", "wb"))
now = datetime.now()
policy = cfr_solver.average_policy()
agent_name = "cfr"
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(ep) + "episodes.csv")
def xfsp_train(_):
exploit_history = list()
exploit_idx = list()
game = pyspiel.load_game(FLAGS.game, {"players": pyspiel.GameParameter(2)})
fsp_solver = fictitious_play.XFPSolver(game)
checkpoint = datetime.now()
for ep in range(FLAGS.episodes):
if (ep % 1000) == 0:
delta = datetime.now() - checkpoint
pol = policy.PolicyFromCallable(
game, fsp_solver.average_policy_callable())
conv = exploitability.exploitability(game, pol)
exploit_history.append(conv)
exploit_idx.append(ep)
print(
"[XFSP] Iteration {} exploitability {} - {} seconds since last checkpoint"
.format(ep, conv, delta.seconds))
checkpoint = datetime.now()
fsp_solver.iteration()
agent_name = "xfsp"
pickle.dump([exploit_idx, exploit_history],
open(
FLAGS.game + "_" + agent_name + "_" + str(FLAGS.episodes) +
".dat", "wb"))
pol = policy.PolicyFromCallable(game, fsp_solver.average_policy_callable())
for pid in [1, 2]:
policy_to_csv(
game, pol, f"policies/policy_" + now.strftime("%m-%d-%Y_%H-%M") +
"_" + agent_name + "_" + str(pid + 1) + "_+" +
str(FLAGS.episodes) + "episodes.csv")
def test_tabular_policy_from_csv(tmpdir):
game = pyspiel.load_game("kuhn_poker")
output = os.path.join(tmpdir, 'policy.csv')
tabular_policy = policy.TabularPolicy(game)
# Save policy as CSV
output = os.path.join(tmpdir, 'policy.csv')
policy_to_csv(game, tabular_policy, output)
tabular_policy_from_csv(game, output)
def csv_policy(tmpdir):
# Setup game and policy
game = pyspiel.load_game("kuhn_poker")
tabular_policy = policy.TabularPolicy(game)
# Save policy as CSV
output = os.path.join(tmpdir, 'policy.csv')
policy_to_csv(game, tabular_policy, output)
return output
def run_agents(sess, env, agents, expl_policies_avg):
agent_name = "nfsp"
write_policy_at = [1e4, 1e5, 1e6, 3e6, 5e6]
sess.run(tf.global_variables_initializer())
exploit_idx = list()
exploit_history = list()
for ep in range(FLAGS.episodes):
if (ep + 1) % 10000 == 0:
expl = exploitability.exploitability(env.game, expl_policies_avg)
exploit_idx.append(ep)
exploit_history.append(expl)
with open("exploitabilities.txt", "a") as f:
f.write(str(expl) + "\n")
losses = [agent.loss for agent in agents]
msg = "-" * 80 + "\n"
msg += "{}: {}\n{}\n".format(ep + 1, expl, losses)
logging.info("%s", msg)
if ep in write_policy_at:
for pid, agent in enumerate(agents):
policy_to_csv(
env.game, expl_policies_avg,
f"policies/policy_" + agent_name + "_" +
datetime.now().strftime("%m-%d-%Y_%H-%M") + "_" +
str(pid + 1) + "_" + str(ep) + "episodes.csv")
time_step = env.reset()
while not time_step.last():
player_id = time_stcfr_trainep.observations["current_player"]
agent_output = agents[player_id].step(time_step)
action_list = [agent_output.action]
time_step = env.step(action_list)
# Episode is over, step all agents with final info state.
for agent in agents:
agent.step(time_step)
pickle.dump([exploit_idx, exploit_history],
open(
FLAGS.game + "_" + agent_name + "_" + str(FLAGS.episodes) +
".dat", "wb"))
now = datetime.now()
for pid, agent in enumerate(agents):
policy_to_csv(
env.game, expl_policies_avg,
f"policies/policy_" + now.strftime("%m-%d-%Y_%H-%M") + "_" +
agent_name + "_" + str(pid + 1) + "_+" + str(ep) + "episodes.csv")
plt.plot([i for i in range(len(exploit_history))], exploit_history)
plt.ylim(0.01, 1)
plt.yticks([1, 0.1, 0.01])
plt.yscale("log")
plt.xscale("log")
plt.show()
def test_play_tournament(tmpdir):
game = pyspiel.load_game("kuhn_poker")
for team in ["python", "ruby", "java"]:
for player in ["p1", "p2"]:
tabular_policy = policy.TabularPolicy(game)
# Save policy as CSV
output = os.path.join(tmpdir, f'{team}_{player}.csv')
policy_to_csv(game, tabular_policy, output)
ranking, results = play_tournament(game, str(tmpdir))
assert len(list(ranking.keys())) == 3
assert len(results) == 3 * 2 * 2
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(unused_argv):
game = pyspiel.load_game("kuhn_poker")
cfr_solver = cfr.CFRSolver(game)
episodes = []
exploits = []
nashes = []
# Train the agent for a specific amount of episodes
for ep in range(FLAGS.num_train_episodes):
print("Running episode {} of {}".format(ep, FLAGS.num_train_episodes))
cfr_solver.evaluate_and_update_policy()
avg_pol = cfr_solver.average_policy()
# Calculate the exploitability and nash convergence
expl = exploitability.exploitability(game, avg_pol)
nash = exploitability.nash_conv(game, avg_pol)
exploits.append(expl)
nashes.append(nash)
episodes.append(ep)
# Get the average policy
average_policy = cfr_solver.average_policy()
average_policy_values = expected_game_score.policy_value(
game.new_initial_state(), [average_policy] * 2)
cur_pol = cfr_solver.current_policy()
# Plot the exploitability
plt.plot(episodes, exploits, "-r", label="Exploitability")
plt.xscale("log")
plt.yscale("log")
plt.xlim(FLAGS.eval_every, FLAGS.num_train_episodes)
plt.legend(loc="upper right")
plt.show()
plt.savefig("cfr_expl.png")
plt.figure()
# Plot the nash convergence
plt.plot(episodes, nashes, "-r", label="NashConv")
plt.xscale("log")
plt.yscale("log")
plt.xlim(FLAGS.eval_every, FLAGS.num_train_episodes)
plt.legend(loc="upper right")
plt.show()
plt.savefig("cfr_nash.png")
print(average_policy)
print(average_policy_values)
policy_to_csv(game, average_policy, "./kuhn_policy.csv")
def neurd_train(unudes_arg):
tf.enable_eager_execution()
game = pyspiel.load_game(FLAGS.game, {"players": pyspiel.GameParameter(2)})
models = []
for _ in range(game.num_players()):
models.append(
neurd.DeepNeurdModel(game,
num_hidden_layers=1,
num_hidden_units=13,
num_hidden_factors=8,
use_skip_connections=True,
autoencode=False))
solver = neurd.CounterfactualNeurdSolver(game, models)
def _train(model, data):
neurd.train(model,
data,
batch_size=100,
step_size=1,
threshold=2,
autoencoder_loss=(None))
exploit_history = list()
for ep in range(FLAGS.episodes):
solver.evaluate_and_update_policy(_train)
if ep % 100 == 0:
conv = pyspiel.exploitability(game, solver.average_policy())
exploit_history.append(conv)
print("Iteration {} exploitability {}".format(ep, conv))
now = datetime.now()
policy = solver.average_policy()
agent_name = "neurd"
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(ep) + "episodes.csv")
plt.plot([i for i in range(len(exploit_history))], exploit_history)
plt.ylim(0.01, 1)
plt.yticks([1, 0.1, 0.01])
plt.yscale("log")
plt.xscale("log")
plt.show()
def main(_):
game = "kuhn_poker"
num_players = 2
env_configs = {"players": num_players}
env = rl_environment.Environment(game, **env_configs)
info_state_size = env.observation_spec()["info_state"][0]
num_actions = env.action_spec()["num_actions"]
with tf.Session() as sess:
# pylint: disable=g-complex-comprehension
agents = [
policy_gradient.PolicyGradient(sess,
idx,
info_state_size,
num_actions,
loss_str=FLAGS.loss_str,
hidden_layers_sizes=(128, ))
for idx in range(num_players)
]
expl_policies_avg = PolicyGradientPolicies(env, agents)
sess.run(tf.global_variables_initializer())
for ep in range(FLAGS.num_episodes):
if (ep + 1) % FLAGS.eval_every == 0:
losses = [agent.loss for agent in agents]
expl = exploitability.exploitability(env.game,
expl_policies_avg)
msg = "-" * 80 + "\n"
msg += "{}: {}\n{}\n".format(ep + 1, expl, losses)
logging.info("%s", msg)
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
agent_output = agents[player_id].step(time_step)
action_list = [agent_output.action]
time_step = env.step(action_list)
# Episode is over, step all agents with final info state.
for agent in agents:
agent.step(time_step)
for pid, agent in enumerate(agents):
policy_to_csv(env.game, expl_policies_avg,
f"{FLAGS.modeldir}/test_p{pid+1}.csv")
def test_tabular_policy_to_csv(tmpdir):
# Setup game and policy
game = pyspiel.load_game("kuhn_poker")
tabular_policy = policy.TabularPolicy(game)
# Save policy as CSV
output = os.path.join(tmpdir, 'policy.csv')
policy_to_csv(game, tabular_policy, output)
assert list(tmpdir.listdir()) == [output]
# Check created CSV
csv = pd.read_csv(output, index_col=0)
# Get all states in the game at which players have to make decisions.
states = get_all_states.get_all_states(game,
depth_limit=-1,
include_terminals=False,
include_chance_states=False)
assert set(csv.index.values) <= set(states.keys())
assert len(csv.columns) == game.num_distinct_actions()
def test_callable_policy_to_csv(tmpdir):
def _uniform_policy(state):
actions = state.legal_actions()
p = 1.0 / len(actions)
return [(a, p) for a in actions]
# Setup game and policy
game = pyspiel.load_game("kuhn_poker")
callable_policy = policy.PolicyFromCallable(game, _uniform_policy)
# Save policy as CSV
output = os.path.join(tmpdir, 'policy.csv')
policy_to_csv(game, callable_policy, output)
assert list(tmpdir.listdir()) == [output]
# Check created CSV
csv = pd.read_csv(output, index_col=0)
# Get all states in the game at which players have to make decisions.
states = get_all_states.get_all_states(game,
depth_limit=-1,
include_terminals=False,
include_chance_states=False)
assert set(csv.index.values) <= set(states.keys())
def runNFSP(hidden_layers_sizes, replay_buffer_capacity,
reservoir_buffer_capacity, epsilon_start, epsilon_end,
anticipatory_param):
# Define data storage arrays
episodes = []
exploits = []
# Initialize the game
game = FLAGS.game
num_players = FLAGS.num_players
env_configs = {"players": num_players}
env = rl_environment.Environment(game, **env_configs)
info_state_size = env.observation_spec()["info_state"][0]
num_actions = env.action_spec()["num_actions"]
kwargs = {
"replay_buffer_capacity": replay_buffer_capacity,
"epsilon_decay_duration": FLAGS.num_train_episodes,
"epsilon_start": epsilon_start,
"epsilon_end": epsilon_end,
}
# Start the TensorFlow session
with tf.Session() as sess:
# Initialize NFSP Agent
agents = [
nfsp.NFSP(sess, idx, info_state_size, num_actions,
hidden_layers_sizes, reservoir_buffer_capacity,
anticipatory_param, **kwargs)
for idx in range(num_players)
]
expl_policies_avg = NFSPPolicies(env, agents, nfsp.MODE.average_policy)
sess.run(tf.global_variables_initializer())
for ep in range(FLAGS.num_train_episodes):
# Evaluate Agents
if ((ep + 1) % FLAGS.eval_every == 0) & ((ep + 1) >= 100):
losses = [agent.loss for agent in agents]
logging.info("Losses: %s", losses)
expl = exploitability.exploitability(env.game,
expl_policies_avg)
logging.info("[%s] Exploitability AVG %s", ep + 1, expl)
logging.info("_____________________________________________")
episodes.append(ep + 1)
exploits.append(expl)
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
agent_output = agents[player_id].step(time_step)
action_list = [agent_output.action]
time_step = env.step(action_list)
# Episode is over, step all agents with final info state.
for agent in agents:
agent.step(time_step)
for pid, agent in enumerate(agents):
policy_to_csv(env.game, expl_policies_avg,
f"{FLAGS.modeldir}/test_p{pid+1}.csv")
play(agents, env)
return episodes, exploits