def __init__(self):
''' Load pretrained model
'''
import tensorflow as tf
from rlcard.agents import DQNAgent
#tf.compat.v1.global_variables_initializer()
#tf.compat.v1.local_variables_initializer()
env = rlcard.make('uno')
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph)
with self.graph.as_default():
self.dqn_agents = []
for i in range(env.player_num):
agent = DQNAgent(self.sess,
scope='dqn' + str(i),
action_num=env.action_num,
state_shape=env.state_shape,
mlp_layers=[512, 512])
self.dqn_agents.append(agent)
check_point_path = os.path.join(ROOT_PATH, 'uno_dqn')
with self.sess.as_default():
with self.graph.as_default():
saver = tf.train.Saver()
# saver = tf.train.Saver()
saver.restore(self.sess,
tf.train.latest_checkpoint(check_point_path))
def __init__(self):
''' Load pretrained model
'''
import tensorflow as tf
self.graph = tf.Graph()
# Mitigation for gpu memory issue
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(graph=self.graph, config=config)
env = rlcard.make('tractor')
with self.graph.as_default():
self.dqn_agents = []
for i in range(1):
agent = DQNAgent(self.sess,
scope='dqn',
action_num=env.action_num,
state_shape=env.state_shape,
mlp_layers=[2048,2048],
replay_memory_size=100000,
update_target_estimator_every=100,
discount_factor=0.5,
epsilon_start=1,
epsilon_end=0.1,
epsilon_decay_steps=100000,
batch_size=256,
learning_rate=0.00002,
use_rule_policy=False
)
self.dqn_agents.append(agent)
check_point_path = os.path.join(TRACTOR_PATH, 'tractor_dqn_345k')
with self.sess.as_default():
with self.graph.as_default():
saver = tf.train.Saver()
saver.restore(self.sess, tf.train.latest_checkpoint(check_point_path))
def __init__(self):
''' Load pretrained model
'''
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph)
env = rlcard.make('limit-holdem')
with self.graph.as_default():
self.dqn_agents = []
agent = DQNAgent(self.sess,
scope='dqn',
action_num=env.action_num,
replay_memory_init_size=1000,
train_every=1,
state_shape=env.state_shape,
mlp_layers=[512,512])
self.dqn_agents.append(agent)
check_point_path = os.path.join(ROOT_PATH, 'limit_holdem_dqn')
with self.sess.as_default():
with self.graph.as_default():
print(2)
saver = tf.train.Saver()
saver.restore(self.sess,tf.train.latest_checkpoint(check_point_path))
def main():
# Make environment
env = rlcard.make('blackjack', config={'env_num': 4, 'seed': 0})
eval_env = rlcard.make('blackjack', config={'env_num': 4, 'seed': 0})
# Set the iterations numbers and how frequently we evaluate performance
evaluate_every = 100
evaluate_num = 10000
iteration_num = 100000
# The intial memory size
memory_init_size = 100
# Train the agent every X steps
train_every = 1
# The paths for saving the logs and learning curves
log_dir = './experiments/blackjack_dqn_result/'
# Set a global seed
set_global_seed(0)
with tf.compat.v1.Session() as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set up the agents
agent = DQNAgent(sess,
scope='dqn',
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[10, 10])
env.set_agents([agent])
eval_env.set_agents([agent])
# Initialize global variables
sess.run(tf.compat.v1.global_variables_initializer())
# Initialize a Logger to plot the learning curve
logger = Logger(log_dir)
for iteration in range(iteration_num):
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for ts in trajectories[0]:
agent.feed(ts)
# Evaluate the performance. Play with random agents.
if iteration % evaluate_every == 0:
logger.log_performance(env.timestep,
tournament(eval_env, evaluate_num)[0])
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('DQN')
# Save model
save_dir = 'models/blackjack_dqn'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.compat.v1.train.Saver()
saver.save(sess, os.path.join(save_dir, 'model'))
# The paths for saving the logs and learning curves
log_dir = './experiments/uno_single_dqn_result/'
# Set a global seed
set_global_seed(0)
with tf.compat.v1.Session() as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set up the agents
agent = DQNAgent(sess,
scope='dqn',
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[128, 128])
# Initialize global variables
sess.run(tf.compat.v1.global_variables_initializer())
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
state = env.reset()
for timestep in range(timesteps):
action = agent.step(state)
next_state, reward, done = env.step(action)
ts = (state, action, reward, next_state, done)
import tensorflow as tf
import os
# Make environment and enable human mode
# Set 'record_action' to True because we need it to print results
env = rlcard.make('limit-holdem', config={'record_action': True})
human_agent = HumanAgent(env.action_num)
with tf.Session() as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
agent = DQNAgent(sess,
scope='dqn',
action_num=env.action_num,
replay_memory_init_size=1000,
train_every=1,
state_shape=env.state_shape,
mlp_layers=[512, 512])
saver = tf.train.Saver()
save_dir = 'models/limit_holdem_dqn'
saver.restore(sess, os.path.join(save_dir, 'model'))
env.set_agents([human_agent, agent])
print(">> Limit Hold'em random agent")
while (True):
print(">> Start a new game")
# Train the agent every X steps
train_every = 1
# The paths for saving the logs and learning curves
log_dir = './experiments/gin_rummy_dqn_result/'
# Set a global seed
set_global_seed(0)
agent = DQNAgent(
scope='dqn',
action_num=env.action_num,
#replay_memory_size=20000,
replay_memory_size=1000,
#replay_memory_init_size=memory_init_size,
replay_memory_init_size=500,
train_every=train_every,
#state_shape=env.state_shape,
state_shape=[768],
mlp_layers=[512, 512],
device=torch.device('cpu'))
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents([agent, random_agent])
eval_env.set_agents([agent, random_agent])
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
print('epi: ', episode)
# latest_ckpt = tf.train.latest_checkpoint(check_point_path)
sess = tf.Session(graph=graph, config=config)
env = rlcard.make('tractor')
with graph.as_default():
dqn_agents = []
for i in range(1):
agent = DQNAgent(sess,
scope='dqn',
action_num=env.action_num,
state_shape=env.state_shape,
mlp_layers=[2048,2048],
replay_memory_size=100000,
update_target_estimator_every=100,
discount_factor=0.5,
epsilon_start=1,
epsilon_end=0.1,
epsilon_decay_steps=100000,
batch_size=256,
learning_rate=0.00002,
use_rule_policy=False
)
dqn_agents.append(agent)
# check_point_path = os.path.join(TRACTOR_PATH, 'dqn_10k_blindcard')
with sess.as_default():
with graph.as_default():
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(check_point_path))
log_dir = './experiments/nolimit_holdem_dqn_result/'
# Set a global seed
# set_global_seed(0)
with tf.Session() as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
with tf.variable_scope('agent1'):
# Set up the agents
agent1 = DQNAgent(sess,
scope='dqn',
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[1280, 1280])
with tf.variable_scope('agent2'):
agent2 = DQNAgent(sess,
scope='dqn',
action_num=eval_env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=eval_env.state_shape,
mlp_layers=[512, 512])
random_agent = RandomAgent(action_num=eval_env.action_num)
human_agent = NolimitholdemHumanAgent(eval_env.action_num)
log_dir = './experiments/uno_dqn_result/'
# Set a global seed
set_global_seed(0)
with tf.Session() as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set up the agents
agent = DQNAgent(sess,
scope='dqn',
action_num=env.action_num,
replay_memory_size=20000,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[60, 60, 60, 60, 60],
batch_size=512)
saver = tf.train.Saver()
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents([agent, random_agent])
eval_env.set_agents([agent, random_agent])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# saver.restore(sess, "models/uno_dqn5/model")
def main():
# Make environment
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env = rlcard.make('no-limit-holdem', config={'seed': 0, 'env_num': 4})
eval_env = rlcard.make('no-limit-holdem', config={'seed': 0, 'env_num': 4})
# Set the iterations numbers and how frequently we evaluate performance
evaluate_every = 5000
selfplay_every = 25000
evaluate_num = 10000
iteration_num = 8000000
# The intial memory size
memory_init_size = 100
# Train the agent every X steps
train_every = 1
agent = DQNAgent(num_actions=env.num_actions,
state_shape=env.state_shape[0],
mlp_layers=[64, 64, 64, 64],
device=device)
agents = [agent, load_model("model.pth")]
env.set_agents(agents)
with Logger('./') as logger:
for episode in range(iteration_num):
# Generate data from the environment
trajectories, payoffs = env.run(is_training=True)
# Reorganaize the data to be state, action, reward, next_state, done
trajectories = reorganize(trajectories, payoffs)
# Feed transitions into agent memory, and train the agent
# Here, we assume that DQN always plays the first position
# and the other players play randomly (if any)
for ts in trajectories[0]:
agent.feed(ts)
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
logger.log_performance(env.timestep,
tournament(env, evaluate_num)[0])
if episode % selfplay_every == 0:
save_path = os.path.join('./', str(episode) + "model.pth")
torch.save(agent, save_path)
print('Model saved in', save_path)
agents = [agent, load_model(str(episode) + "model.pth")]
env.set_agents(agents)
# Get the paths
csv_path, fig_path = logger.csv_path, logger.fig_path
# Plot the learning curve
#plot_curve(csv_path, fig_path, args.algorithm)
# Save model
save_path = os.path.join('./', 'model.pth')
torch.save(agent, save_path)
print('Model saved in', save_path)
# The paths for saving the logs and learning curves
log_dir = './experiments/nlh_cfr_result/'
# Set a global seed
set_seed(0)
def main():
# Make environment
env = rlcard.make('no-limit-holdem',
config={
'seed': 0,
'env_num': 16,
'game_player_num': 4
})
eval_env = rlcard.make('no-limit-holdem',
config={
'seed': 0,
'env_num': 16
})
# Set the iterations numbers and how frequently we evaluate the performance
evaluate_every = 100
evaluate_num = 1000
episode_num = 200000
# The intial memory size
memory_init_size = 1000
# Train the agent every X steps
train_every = 1
_reward_max = -0.8
# The paths for saving the logs and learning curves
log_dir = './experiments/nolimit_holdem_dqn_result/'
# Set a global seed
set_global_seed(0)
with tf.Session() as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set up the agents
agent = DQNAgent(sess,
scope='dqn',
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[512, 512])
agent2 = NFSPAgent(sess,
scope='nfsp',
action_num=env.action_num,
state_shape=env.state_shape,
hidden_layers_sizes=[512, 512],
anticipatory_param=0.1,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_init_size=memory_init_size,
train_every=64,
q_train_every=64,
q_mlp_layers=[512, 512])
# Initialize global variables
sess.run(tf.global_variables_initializer())
save_dir = 'models/nolimit_holdem_dqn'
saver = tf.train.Saver()
#saver.restore(sess, os.path.join(save_dir, 'model'))
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents([agent, agent, agent2, random_agent])
eval_env.set_agents([agent, agent2])
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
agent2.sample_episode_policy()
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for ts in trajectories[0]:
agent.feed(ts)
for ts in trajectories[2]:
agent2.feed(ts)
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
_reward = tournament(eval_env, evaluate_num)[0]
logger.log_performance(episode, _reward)
if _reward > _reward_max:
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver.save(sess, os.path.join(save_dir, 'model'))
_reward_max = _reward
# Close files in the logger
logger.close_files()
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver.save(sess, os.path.join(save_dir, 'model_final'))
def main():
# Make environment
env = rlcard.make('leduc-holdem', config={'seed': 0, 'env_num': 4})
eval_env = rlcard.make('leduc-holdem', config={'seed': 0, 'env_num': 4})
# Set the iterations numbers and how frequently we evaluate the performance
evaluate_every = 100
evaluate_num = 10000
episode_num = 800000
# The intial memory size
memory_init_size = 1000
# Train the agent every X steps
train_every = 1
_reward_max = -0.5
# The paths for saving the logs and learning curves
log_dir = './experiments/leduc_holdem_dqn_result/'
# Set a global seed
set_global_seed(0)
with tf.Session() as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set up the agents
agent = DQNAgent(sess,
scope='dqn',
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[128, 128])
# random_agent = RandomAgent(action_num=eval_env.action_num)
cfr_agent = models.load('leduc-holdem-cfr').agents[0]
env.set_agents([agent, agent])
eval_env.set_agents([agent, cfr_agent])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
saver = tf.train.Saver()
save_dir = 'models/leduc_holdem_dqn'
saver.restore(sess, os.path.join(save_dir, 'model'))
for episode in range(episode_num):
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for ts in trajectories[0]:
agent.feed(ts)
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
_reward = tournament(eval_env, evaluate_num)[0]
logger.log_performance(episode, _reward)
if _reward > _reward_max:
# Save model
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver.save(sess, os.path.join(save_dir, 'model'))
_reward_max = _reward
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('DQN')
# Make environment
env = rlcard.make('uno', config={'seed': 0})
# Set a global seed
set_global_seed(0)
# Load pretrained model
graph = tf.Graph()
sess = tf.Session(graph=graph)
with graph.as_default():
dqn_agents = []
for i in range(env.player_num):
agent = DQNAgent(sess,
scope='dqn' + str(i),
action_num=env.action_num,
state_shape=env.state_shape,
mlp_layers=[512, 512])
dqn_agents.append(agent)
# We have a pretrained model here. Change the path for your model.
check_point_path = os.path.join(rlcard.__path__[0],
'models/pretrained/uno_dqn')
with sess.as_default():
with graph.as_default():
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(check_point_path))
# Evaluate the performance. Play with random agents.
evaluate_num = 1000
set_global_seed(0)
with tf.Session() as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set up the agents
agent = DQNAgent(
sess,
scope='dqn',
replay_memory_size=replay_memory_size,
replay_memory_init_size=memory_init_size,
update_target_estimator_every=update_target_estimator_every,
discount_factor=discount_factor,
epsilon_start=epsilon_start,
epsilon_end=epsilon_end,
epsilon_decay_steps=epsilon_decay_steps,
batch_size=batch_size,
action_num=env.action_num,
state_shape=env.state_shape,
train_every=train_every,
mlp_layers=mlp_layers,
learning_rate=learning_rate)
random_agent = RandomAgent(action_num=eval_env.action_num)
agent_list = [agent, random_agent, random_agent] # default
#deactivated at the moment because we might not need it if we use landlord score anyway for switching positions/roles
if (landlord_score):
agent_list = [agent, random_agent, random_agent]
memory_init_size = 1000
# Train the agent every X steps
train_every = 1
# The paths for saving the logs and learning curves
log_dir = './experiments/dqn_random_result/'
# Set a global seed
set_global_seed(0)
# Set up the agents
agent = DQNAgent(scope='dqn',
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[128, 128],
device=torch.device('cpu'))
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents([agent, random_agent])
eval_env.set_agents([agent, random_agent])
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
def train(args):
# Check whether gpu is available
device = get_device()
# Seed numpy, torch, random
set_seed(args.seed)
# Make the environment with seed
env = rlcard.make(args.env, config={
'seed': args.seed,
})
# Initialize the agent and use random agents as opponents
if args.algorithm == 'dqn':
from rlcard.agents import DQNAgent
agent = DQNAgent(
num_actions=env.num_actions,
state_shape=env.state_shape[0],
mlp_layers=[64, 64],
device=device,
)
elif args.algorithm == 'nfsp':
from rlcard.agents import NFSPAgent
agent = NFSPAgent(
num_actions=env.num_actions,
state_shape=env.state_shape[0],
hidden_layers_sizes=[64, 64],
q_mlp_layers=[64, 64],
device=device,
)
agents = [agent]
for _ in range(1, env.num_players):
agents.append(RandomAgent(num_actions=env.num_actions))
env.set_agents(agents)
# Start training
with Logger(args.log_dir) as logger:
for episode in range(args.num_episodes):
if args.algorithm == 'nfsp':
agents[0].sample_episode_policy()
# Generate data from the environment
trajectories, payoffs = env.run(is_training=True)
# Reorganaize the data to be state, action, reward, next_state, done
trajectories = reorganize(trajectories, payoffs)
# Feed transitions into agent memory, and train the agent
# Here, we assume that DQN always plays the first position
# and the other players play randomly (if any)
for ts in trajectories[0]:
agent.feed(ts)
# Evaluate the performance. Play with random agents.
if episode % args.evaluate_every == 0:
logger.log_performance(
env.timestep,
tournament(
env,
args.num_eval_games,
)[0])
# Get the paths
csv_path, fig_path = logger.csv_path, logger.fig_path
# Plot the learning curve
plot_curve(csv_path, fig_path, args.algorithm)
# Save model
save_path = os.path.join(args.log_dir, 'model.pth')
torch.save(agent, save_path)
print('Model saved in', save_path)
# Mitigation for gpu memory issue
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# config.gpu_options.per_process_gpu_memory_fraction = 0.9
# with tf.Session(config=config) as sess:
with tf.Session() as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set up the agents
agent = DQNAgent(sess,
scope='dqn',
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[512, 512])
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents([agent, random_agent, random_agent])
eval_env.set_agents([agent, random_agent, random_agent])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
with tf.Session(config=config) as sess:
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set up the agents
for i in range(1):
agent = DQNAgent(sess,
scope='dqn' if i==0 else 'dqn' + str(i),
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[2048,2048],
replay_memory_size=100000,
update_target_estimator_every=500,
discount_factor=0.99,
epsilon_start=0.1,
epsilon_end=0.1,
epsilon_decay_steps=100000,
batch_size=256,
learning_rate=0.00002,
use_rule_policy=False
)
agents.append(agent)
random_agent = RandomAgent(action_num=eval_env.action_num)
rule_agent = TractorRuleAgent(action_num=eval_env.action_num)
# 1 dqn agent vs 3 rule agent
# env.set_agents([agent, rule_agent, rule_agent, rule_agent])
