def test_best_response_cfr_one_card_poker():
game = OneCardPoker.create_game(n_cards=4)
strategy, exploitabilities, strategies = cfr(game, num_iters=10, use_chance_sampling=False)
exploitability = compute_exploitability(game, strategy)
print("Exploitability: {}".format(exploitability))
assert exploitability > 0.0
def external_sampling_cfr(game: extensive_game.ExtensiveGame,
num_iters: int = 1000):
"""
Args:
game: ExtensiveGame.
num_iters: int. The number of iterations of CFR to perform.
Returns:
average_strategy
exploitabilities
strategies
"""
# regrets is a dictionary where the keys are the information sets and values
# are dictionaries from actions available in that information set to the
# counterfactual regret for not playing that action in that information set.
# Since information sets encode the player, we only require one dictionary.
regrets = dict()
# Strategy_t holds the strategy at time t; similarly strategy_t_1 holds the
# strategy at time t + 1.
strategy_t = extensive_game.Strategy.initialise()
strategy_t_1 = extensive_game.Strategy.initialise()
cfr_state = cfr_util.CFRState()
average_strategy = cfr_util.AverageStrategy(game)
strategies = []
exploitabilities = []
for t in range(num_iters):
for player in [1, 2]:
external_sampling_cfr_recursive(game, game.root, player, regrets,
strategy_t, strategy_t_1,
cfr_state)
# Update the strategies
strategy_t = strategy_t_1.copy()
strategies.append(strategy_t)
# Update average strategy
cfr_util.update_average_strategy(game, average_strategy, strategy_t)
# Compute the average strategy
if t % 200 == 0:
# Compute exploitability
exploitability = best_response.compute_exploitability(
game, average_strategy.compute_strategy())
exploitabilities.append((t, exploitability))
print("t: {}, nodes touched: {}, exploitability: {:.3f} mbb/h".
format(t, cfr_state.nodes_touched, exploitability * 1000))
# immediate_regret, _, _ = cfr_metrics.compute_immediate_regret(game, strategies)
# print("Immediate regret: {}".format(immediate_regret))
return average_strategy.compute_strategy(), exploitabilities, strategies
def cfr(game, num_iters=10000, use_chance_sampling=True):
# regrets is a dictionary where the keys are the information sets and values
# are dictionaries from actions available in that information set to the
# counterfactual regret for not playing that action in that information set.
# Since information sets encode the player, we only require one dictionary.
regrets = dict()
# Similarly, action_counts is a dictionary with keys the information sets
# and values dictionaries from actions to action counts.
action_counts = dict()
# Strategy_t holds the strategy at time t; similarly strategy_t_1 holds the
# strategy at time t + 1.
strategy_t = dict()
strategy_t_1 = dict()
average_strategy = None
exploitabilities = []
# Each information set is uniquely identified with an action tuple.
values = {1: [], 2: []}
for t in range(num_iters):
for i in [1, 2]:
cfr_recursive(game,
game.root,
i,
t,
1.0,
1.0,
1.0,
regrets,
action_counts,
strategy_t,
strategy_t_1,
use_chance_sampling=use_chance_sampling)
average_strategy = compute_average_strategy(action_counts)
# Update strategy_t to equal strategy_t_1. We update strategy_t_1 inside
# cfr_recursive. We take a copy because we update it inside
# cfr_recursive, and want to hold on to strategy_t_1 separately to
# compare.
strategy_t = strategy_t_1.copy()
# Compute the exploitability of the strategy.
if t % 1000 == 0:
completed_strategy = game.complete_strategy_uniformly(
average_strategy)
exploitability = best_response.compute_exploitability(
game, completed_strategy)
exploitabilities.append((t, exploitability))
print("t: {}, exploitability: {}".format(t, exploitability))
return average_strategy, exploitabilities
def test_best_response_cfr():
"""Test we can run 10 iterations of CFR on Leduc and then compute a best
response.
"""
cards = [Card(value, suit) for value in range(3) for suit in range(2)]
game = Leduc(cards)
strategy, exploitabilities, strategies = cfr(game, num_iters=10, use_chance_sampling=False)
exploitability = compute_exploitability(game, strategy)
print("Exploitability: {}".format(exploitability))
assert exploitability > 0.0
def compute_agent_exploitability(agent: Agent, sess: tf.Session, game: NFSPGame):
"""Computes the exploitability of the agent's current strategy.
Args:
agent: Agent.
sess: tensorflow session.
game: NFSPGame.
Returns:
float. Exploitability of the agent's strategy.
"""
states = game._state_vectors
strategy = agent.get_strategy(sess, states)
return compute_exploitability(game._game, strategy)
default=3,
type=int,
help='In OneCardPoker or Leduc, pass the number of cards to use.')
parser.add_argument('--num_suits',
default=2,
type=int,
help='In Leduc, pass the number of suits to use.')
args = parser.parse_args()
if args.game == 'Leduc':
print("Solving Leduc Hold'em")
cards = card.get_deck(num_values=args.num_values,
num_suits=args.num_suits)
n_game = leduc.create_neural_leduc(cards)
elif args.game == 'RockPaperScissors':
print("Solving rock paper scissors")
n_game = rock_paper_scissors.create_neural_rock_paper_scissors()
strategy, exploitabilities = deep_cfr.deep_cfr(
n_game,
num_iters=args.num_iters,
num_traversals=args.num_traversals,
advantage_maxlen=args.advantage_maxlen,
strategy_maxlen=args.strategy_maxlen,
batch_size=args.batch_size,
num_sgd_updates=args.num_sgd_updates)
exploitability = compute_exploitability(n_game.extensive_game, strategy)
print("Exploitability of strategy: {}".format(exploitability))
def deep_cfr(n_game: neural_game.NeuralGame,
num_iters: int=100, num_traversals: int=10000,
advantage_maxlen: int=1000000, strategy_maxlen: int=1000000,
batch_size: int=1024, num_sgd_updates: int=100):
"""
Args:
n_game: NeuralGame.
num_iters: int. The number of iterations to run deep CFR for.
num_traversals: int. The number of traversals per CFR iteration.
advantage_maxlen: int. The maximum length of the advantage memories.
strategy_maxlen: int. The maximum length of the strategy memory.
batch_size: int. The batch size to use in training.
num_sgd_updates: int. The number of sgd updates per training.
Returns:
strategy, exploitability.
"""
game, action_indexer, info_set_vectoriser = n_game
advantage_memory1 = buffer.Reservoir(maxlen=advantage_maxlen)
advantage_memory2 = buffer.Reservoir(maxlen=advantage_maxlen)
strategy_memory = buffer.Reservoir(maxlen=strategy_maxlen)
# Create summary tensors
valid_summariser = util.TBSummariser(['exploitability'])
time_str = time.strftime("%Y-%m-%d-%H:%M:%S", time.gmtime())
save_path = os.path.join('experiments', time_str)
if not os.path.exists(save_path):
print("Path doesn't exist, so creating: {}".format(save_path))
os.makedirs(save_path)
log_file = os.path.join(save_path, 'nfsp.log')
print("Log file {}".format(log_file))
print("To run tensorboard: tensorboard --logdir {}".format(os.path.join(os.getcwd(), save_path)))
with tf.Session() as sess:
network1 = DeepRegretNetwork(info_set_vectoriser.state_shape, action_indexer, 1)
network1.set_sess(sess)
network2 = DeepRegretNetwork(info_set_vectoriser.state_shape, action_indexer, 2)
network2.set_sess(sess)
network1.initialise()
network2.initialise()
tf_train_writer = tf.summary.FileWriter(os.path.join(save_path, 'train'), graph=sess.graph)
# Iterate over players and do cfr traversals.
for t in range(1, num_iters + 1):
print("Iteration t = {}".format(t))
for player in [1, 2]:
print("Player: {}".format(player))
print("Traversing")
for i in tqdm(range(num_traversals)):
cfr_traverse(game, action_indexer, info_set_vectoriser,
game.root, player, network1, network2,
advantage_memory1, advantage_memory2,
strategy_memory, t)
# Train the traversing player's network on the cfr traversals.
network = network1 if player == 1 else network2
network.initialise()
advantage_memory = advantage_memory1 if player == 1 else advantage_memory2
mean_loss = train_network(
network, advantage_memory, action_indexer, info_set_vectoriser, t,
tf_train_writer, batch_size, num_sgd_updates)
print("Mean loss: {}".format(mean_loss))
tf_train_writer.flush()
# print("################")
#
# print("----------------")
# print("Advantage memory 1:")
# print(advantage_memory1.buffer)
# print("----------------")
# print("Advantage memory 2:")
# print(advantage_memory2.buffer)
# print("----------------")
#
# print("################")
#
# print("----------------")
# print("Predicted advantages:")
# for info_set_id in set(game.info_set_ids.values()):
# print("{}: {}".format(
# info_set_id,
# network.predict_advantages(info_set_vectoriser.get_vector(info_set_id), action_indexer))
# )
# print("----------------")
#
print("Advantage memory 1 length: {}".format(len(advantage_memory1)))
print("Advantage memory 2 length: {}".format(len(advantage_memory2)))
print("Strategy memory length: {}".format(len(strategy_memory)))
mean_strategy = compute_mean_strategy(strategy_memory)
# print("Strategy summary")
# print(mean_strategy)
if game.is_strategy_complete(mean_strategy):
exploitability = best_response.compute_exploitability(game, mean_strategy)
else:
print("Strategy not complete, filling uniformly.")
exploitability = best_response.compute_exploitability(
game,
mean_strategy,
)
print("Exploitability: {} mbb/h".format(exploitability * 1000))
valid_summary = valid_summariser.summarise(sess, {'exploitability': exploitability})
tf_train_writer.add_summary(valid_summary, global_step=t)
# TODO(chrisn). Train the network on the strategy memory.
return mean_strategy, exploitability
help='The dropout rate to use.')
args = parser.parse_args()
dropout_rate = None
if args.dropout_rate:
dropout_rate = float(args.dropout_rate)
cards = get_deck(num_values=args.num_values, num_suits=args.num_suits)
game = Leduc(cards)
strategy, exploitabilities, strategies = cfr(
game,
num_iters=args.cfr_iters,
use_chance_sampling=args.use_chance_sampling)
exploitability = compute_exploitability(game, strategy)
print("Exploitability of final strategy: {}".format(exploitability))
leduc_nfsp = LeducNFSP(cards)
state_vectors = leduc_nfsp._state_vectors
state_dim = leduc_nfsp.state_dim
action_dim = leduc_nfsp.action_dim
# Now build a network.
layer_dims = [64, 64, 64]
network = build_network(state_dim,
action_dim,
layer_dims,
dropout_rate=dropout_rate)
states = list(strategy.keys())
cards = get_deck(num_values=args.num_values, num_suits=args.num_suits)
game = Leduc(cards)
elif args.game == 'OneCardPoker':
print("Solving One Card Poker")
game = OneCardPoker.create_game(args.num_values)
strategy, exploitabilities = cfr(
game,
num_iters=args.num_iters,
use_chance_sampling=args.use_chance_sampling)
# Save the strategy and plot the performance.
strategy_name = '{}_cfr.strategy'.format(args.game)
print("Saving strategy at {}".format(strategy_name))
save_strategy(strategy, strategy_name)
exploitability = compute_exploitability(game, strategy)
print("Exploitability of saved strategy: {}".format(exploitability))
# plot_name = '{}.html'.format(args.game)
# plt.output_file(plot_name)
# p = plt.figure(title='Exploitability for CFR trained on {}'.format(
# args.game), x_axis_label='t', y_axis_label='Exploitability')
# times = [pair[0] for pair in exploitabilities]
# exploits = [pair[1] for pair in exploitabilities]
# p.line(times, exploits)
#
# print("Saved plot of exploitability at: {}".format(plot_name))
def cfr(game, num_iters=10000, use_chance_sampling=True, linear_weight=False):
"""
Args:
game:
num_iters:
use_chance_sampling:
Returns:
average_strategy, exploitabilities
"""
# regrets is a dictionary where the keys are the information sets and values
# are dictionaries from actions available in that information set to the
# counterfactual regret for not playing that action in that information set.
# Since information sets encode the player, we only require one dictionary.
regrets = dict()
# Similarly, action_counts is a dictionary with keys the information sets
# and values dictionaries from actions to action counts.
action_counts = dict()
cfr_state = cfr_util.CFRState()
# Strategy_t holds the strategy at time t; similarly strategy_t_1 holds the
# strategy at time t + 1.
strategy_t = Strategy.initialise()
strategy_t_1 = Strategy.initialise()
average_strategy = None
exploitabilities = []
strategies = []
average_strategy2 = cfr_util.AverageStrategy(game)
# Each information set is uniquely identified with an action tuple.
start_time = time.time()
for t in range(num_iters):
weight = t if linear_weight else 1.0
for i in [1, 2]:
cfr_recursive(game, game.root, i, t, 1.0, 1.0, 1.0, regrets,
action_counts, strategy_t, strategy_t_1,
cfr_state,
use_chance_sampling=use_chance_sampling,
weight=weight)
average_strategy = compute_average_strategy(action_counts)
cfr_util.update_average_strategy(game, average_strategy2, strategy_t, weight=weight)
# Update strategy_t to equal strategy_t_1. We update strategy_t_1 inside
# cfr_recursive. We take a copy because we update it inside
# cfr_recursive, and want to hold on to strategy_t_1 separately to
# compare.
strategy_t = strategy_t_1.copy()
strategies.append(strategy_t.copy())
# Compute the exploitability of the strategy.
if t % 10 == 0:
print("t: {}. Time since last evaluation: {:.4f} s".format(t, time.time() - start_time))
start_time = time.time()
exploitability = best_response.compute_exploitability(
game, average_strategy)
exploitabilities.append((t, exploitability))
print("t: {}, nodes touched: {}, exploitability: {} mbb/h".format(t, cfr_state.nodes_touched,
exploitability * 1000))
exploitability = best_response.compute_exploitability(game, average_strategy2.compute_strategy())
print("Exploitability (av strategy method 2): {} mbb/h".format(exploitability * 1000))
immediate_regret, _, _ = cfr_metrics.compute_immediate_regret(game, strategies)
print("Immediate regret: {}".format(immediate_regret))
return average_strategy, exploitabilities, strategies
