print(">> Start a new game")
trajectories, payoffs = env.run(is_training=False)
# If the human does not take the final action, we need to
# print other players action
final_state = trajectories[0][-1][-2]
action_record = final_state['action_record']
state = final_state['raw_obs']
_action_list = []
for i in range(1, len(action_record) + 1):
if action_record[-i][0] == state['current_player']:
break
_action_list.insert(0, action_record[-i])
for pair in _action_list:
print('>> Player', pair[0], 'chooses', pair[1])
# Let's take a look at what the agent card is
print('=============== Cards all Players ===============')
for hands in env.get_perfect_information()['hand_cards']:
print_card(hands)
print('=============== Result ===============')
if payoffs[0] > 0:
print('You win {} chips!'.format(payoffs[0]))
elif payoffs[0] == 0:
print('It is a tie.')
else:
print('You lose {} chips!'.format(-payoffs[0]))
print('')
input("Press any key to continue...")
print(">> Start a new game")
trajectories, payoffs = env.run(is_training=False)
# If the human does not take the final action, we need to
# print other players action
final_state = trajectories[0][-1]
action_record = final_state['action_record']
state = final_state['raw_obs']
_action_list = []
for i in range(1, len(action_record) + 1):
if action_record[-i][0] == state['current_player']:
break
_action_list.insert(0, action_record[-i])
for pair in _action_list:
print('>> Player', pair[0], 'chooses', pair[1])
# Let's take a look at what the agent card is
print('=============== CFR Agent ===============')
print_card(env.get_perfect_information()['hand_cards'][1])
print('=============== Result ===============')
if payoffs[0] > 0:
print('You win {} chips!'.format(payoffs[0]))
elif payoffs[0] == 0:
print('It is a tie.')
else:
print('You lose {} chips!'.format(-payoffs[0]))
print('')
input("Press any key to continue...")
def run():
torch.multiprocessing.freeze_support()
env = rlcard.make('no-limit-holdem',
config={
'record_action': True,
'game_player_num': 2,
'env_num': 8,
'use_raw': True
})
# eval_env = rlcard.make('no-limit-holdem', config={'seed': 12, 'game_player_num': 2})
# eval_env2 = rlcard.make('no-limit-holdem', config={'seed': 43, 'game_player_num': 2})
#eval_env3 = rlcard.make('no-limit-holdem', config={'seed': 43, 'game_player_num': 2})
# Set the iterations numbers and how frequently we evaluate the performance
evaluate_every = 1024
evaluate_num = 32
episode_num = 20480
# The intial memory size
memory_init_size = 256
# Train the agent every X steps
train_every = 256
agents = []
agents.append(
NFSPAgent(scope='nfsp' + str(0),
action_num=env.action_num,
state_shape=env.state_shape,
hidden_layers_sizes=[512, 512],
anticipatory_param=0.1,
rl_learning_rate=0.015,
sl_learning_rate=0.0075,
q_epsilon_start=.3,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_size=20480,
q_replay_memory_init_size=memory_init_size,
train_every=train_every + 44,
q_train_every=train_every,
q_mlp_layers=[512, 512],
evaluate_with='best_response'))
agents.append(
NFSPAgent(scope='nfsp' + str(1),
action_num=env.action_num,
state_shape=env.state_shape,
hidden_layers_sizes=[512, 512],
anticipatory_param=0.1,
rl_learning_rate=0.015,
sl_learning_rate=0.0075,
q_epsilon_start=.3,
q_replay_memory_size=20480,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_init_size=memory_init_size,
train_every=train_every + 44,
q_train_every=train_every,
q_mlp_layers=[512, 512],
evaluate_with='best_response'))
# 7, 5 - all in junkies
check_point_path = os.path.join('models/ivvan/cp/8/model-nfsp1.pth')
checkpoint = torch.load(check_point_path)
check_point_path = os.path.join('models/ivvan/cp/8/model-nfsp0.pth')
checkpoint2 = torch.load(check_point_path)
# for agent in agents:
# agent.load(checkpoint)
agents[1].load(checkpoint)
agents[0].load(checkpoint2)
human = nolimit_holdem_human_agent.HumanAgent(env.action_num)
env.set_agents([agents[0], agents[1]])
while (True):
print(">> Start a new game")
trajectories, payoffs = env.run(is_training=False)
if (len(trajectories[0]) == 0):
# the bot folded immediately
continue
# If the human does not take the final action, we need to
# print other players action
final_state = trajectories[0][-1][-2]
# print(final_state, 'waa')
action_record = final_state['action_record']
state = final_state['raw_obs']
_action_list = []
for i in range(1, len(action_record) + 1):
if action_record[-i][0] == state['current_player']:
break
_action_list.insert(0, action_record[-i])
for pair in _action_list:
print('>> Player', pair[0], 'chooses', pair[1])
# Let's take a look at what the agent card is
print('=============== CFR Agent ===============')
print_card(env.get_perfect_information()['hand_cards'][1])
print('=============== Result ===============')
if payoffs[0] > 0:
print('You win {} chips!'.format(payoffs[0]))
elif payoffs[0] == 0:
print('It is a tie.')
else:
print('You lose {} chips!'.format(-payoffs[0]))
print('')
input("Press any key to continue...")
def play():
import tensorflow as tf
# We have a pretrained model here. Change the path for your model.
if tf.test.gpu_device_name():
print('GPU found')
else:
print("No GPU found")
#os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
# Make environment
env = rlcard.make('no-limit-holdem',
config={
'record_action': True,
'game_player_num': 2
})
# Set a global seed
set_global_seed(0)
evaluate_every = 2048
evaluate_num = 32
episode_num = 262144
# The intial memory size
memory_init_size = 256
# Train the agent every X steps
train_every = 256
graph = tf.Graph()
sess = tf.Session(graph=graph)
with graph.as_default():
agents = []
agents.append(
NFSPAgent(sess,
scope='nfsp' + str(0),
action_num=env.action_num,
state_shape=env.state_shape,
hidden_layers_sizes=[512, 512],
anticipatory_param=0.1,
rl_learning_rate=0.01,
sl_learning_rate=0.005,
q_epsilon_start=.6,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_size=80000,
q_replay_memory_init_size=memory_init_size,
train_every=train_every + 44,
q_train_every=train_every,
q_mlp_layers=[512, 512],
evaluate_with='best_response'))
agents.append(
NFSPAgent(sess,
scope='nfsp' + str(1),
action_num=env.action_num,
state_shape=env.state_shape,
hidden_layers_sizes=[512, 512],
anticipatory_param=0.1,
rl_learning_rate=0.01,
sl_learning_rate=0.005,
q_epsilon_start=.6,
q_replay_memory_size=80000,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_init_size=memory_init_size,
train_every=train_every + 44,
q_train_every=train_every,
q_mlp_layers=[512, 512],
evaluate_with='best_response'))
check_point_path = os.path.join(
'models/nolimit_holdem_nfsp/no_all_in/cp/9/')
print(
'-------------------------------------------------------------------------------------'
)
print(check_point_path)
with sess.as_default():
with graph.as_default():
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(check_point_path))
human = nolimit_holdem_human_agent.HumanAgent(env.action_num)
env.set_agents([human, agents[1]])
while (True):
print(">> Start a new game")
trajectories, payoffs = env.run(is_training=False)
if (len(trajectories[0]) == 0):
# the bot folded immediately
continue
# If the human does not take the final action, we need to
# print other players action
final_state = trajectories[0][-1][-2]
action_record = final_state['action_record']
state = final_state['raw_obs']
_action_list = []
for i in range(1, len(action_record) + 1):
if action_record[-i][0] == state['current_player']:
break
_action_list.insert(0, action_record[-i])
for pair in _action_list:
print('>> Player', pair[0], 'chooses', pair[1])
# Let's take a look at what the agent card is
print('=============== CFR Agent ===============')
print_card(env.get_perfect_information()['hand_cards'][1])
print('=============== Result ===============')
if payoffs[0] > 0:
print('You win {} chips!'.format(payoffs[0]))
elif payoffs[0] == 0:
print('It is a tie.')
else:
print('You lose {} chips!'.format(-payoffs[0]))
print('')
input("Press any key to continue...")
