def train(args):
# Make environments, CFR only supports Leduc Holdem
env = rlcard.make('leduc-holdem', config={'seed': 0, 'allow_step_back':True})
eval_env = rlcard.make('leduc-holdem', config={'seed': 0})
# Seed numpy, torch, random
set_seed(args.seed)
# Initilize CFR Agent
agent = CFRAgent(env, os.path.join(args.log_dir, 'cfr_model'))
agent.load() # If we have saved model, we first load the model
# Evaluate CFR against random
eval_env.set_agents([agent, RandomAgent(num_actions=env.num_actions)])
# Start training
with Logger(args.log_dir) as logger:
for episode in range(args.num_episodes):
agent.train()
print('\rIteration {}'.format(episode), end='')
# Evaluate the performance. Play with Random agents.
if episode % args.evaluate_every == 0:
agent.save() # Save model
logger.log_performance(env.timestep, tournament(eval_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, 'cfr')
def __init__(self):
super().__init__()
self.wins = 0
self.losses = 0
'''
Instantiate agent.
'''
# Setup RL NFSP agent
# Set the iterations numbers and how frequently we evaluate/save plot
evaluate_every = 10000
evaluate_num = 10000
episode_num = 100000
# The intial memory size
memory_init_size = 1000
# Train the agent every X steps
train_every = 64
# The paths for saving the logs and learning curves
log_dir = './training/nfsp/'
# Set a global seed
set_global_seed(0)
# Set agent - TODO - determine PPE parameters
self.agent = NFSPAgent(scope='nfsp',
action_num=3,
state_shape=54,
hidden_layers_sizes=[512, 512],
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_init_size=memory_init_size,
train_every=train_every,
q_train_every=train_every,
q_mlp_layers=[512, 512],
device=torch.device('cpu'))
# Init a Logger to plot the learning curve
self.logger = Logger(log_dir)
def train_leduc():
# Make environment and enable human mode
env = rlcard.make('leduc-holdem',
config={
'seed': 0,
'allow_step_back': True
})
eval_env = rlcard.make('leduc-holdem', config={'seed': 0})
# Set the iterations numbers and how frequently we evaluate the performance and save model
evaluate_every = 100
save_plot_every = 1000
evaluate_num = 10000
episode_num = 10000
# The paths for saving the logs and learning curves
log_dir = './experiments/leduc_holdem_oscfr_result/'
# Set a global seed
set_global_seed(0)
# Initilize CFR Agent
model_path = 'models/leduc_holdem_oscfr'
agent = OutcomeSampling_CFR(env, model_path=model_path)
agent.load() # If we have saved model, we first load the model
# Evaluate CFR against pre-trained NFSP
eval_env.set_agents([agent, models.load('leduc-holdem-nfsp').agents[0]])
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
agent.train()
print('\rIteration {}'.format(episode), end='')
# Evaluate the performance. Play with NFSP agents.
if episode % evaluate_every == 0:
agent.save() # Save model
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('OSCFR')
anticipatory_param=0.1,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_init_size=memory_init_size,
train_every=train_every,
q_train_every=train_every,
q_mlp_layers=[512, 512])
mcts_agent = MCTS_Agent(env.action_num, duration, explore, model_action,
model_hand_rank)
env.set_agents([mcts_agent, nfsp_agent])
eval_env.set_agents([mcts_agent, nfsp_agent])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curve
logger_mcts = Logger(log_dir_mcts)
logger_nfsp = Logger(log_dir_nfsp)
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]:
nfsp_agent.feed(ts)
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
logger_mcts.log_performance(env.timestep,
tournament(eval_env, evaluate_num)[0])
train_every=train_every + 44,
q_train_every=train_every,
q_mlp_layers=[512, 512],
evaluate_with='average_policy'))
random_agent = RandomAgent(action_num=eval_env2.action_num)
env.set_agents(agents)
eval_env.set_agents([agents[0], random_agent])
eval_env2.set_agents([random_agent, agents[1]])
# eval_env3.set_agents([agents[1], random_agent])
# Initialize global variables
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
print(episode, end='\r')
#print('oh')
# First sample a policy for the episode
for agent in agents:
agent.sample_episode_policy()
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for i in range(env.player_num):
# update ray rl model
def main():
parser = createParser()
namespace = parser.parse_args(sys.argv[1:])
#random seed
random_seed = namespace.random_seed
#names
env_name = namespace.env_name
env_num = 1
test_name = namespace.test_name
dir_name = str(env_name)+'_a2c_'+str(test_name)+str(random_seed)
# Set the iterations numbers and how frequently we evaluate/save plot
evaluate_every = namespace.evaluate_every
evaluate_num = namespace.evaluate_num
episode_num = namespace.episode_num
# Train the agent every X steps
train_every = namespace.train_every
save_every = namespace.save_every
# Make environment
env_rand = rlcard.make(env_name, config={'seed': random_seed})
eval_env = rlcard.make(env_name, config={'seed': random_seed})
# The paths for saving the logs and learning curves
log_dir = './experiments/rl/'+dir_name+'_result'
# Save model
save_dir = 'models/rl/'+dir_name+'_result'
# Set a global seed
set_global_seed(random_seed)
# Initialize a global step
global_step = tf.Variable(0, name='global_step', trainable=False)
# Set up the agents
agent_rand = RandomAgent(action_num=eval_env.action_num)
agent_test = A2CLSTMQPGAgent(
action_num=eval_env.action_num,
state_shape=eval_env.state_shape,
discount_factor=0.95,
critic_lstm_layers=[1,512],
critic_mlp_layers=[3,512],
critic_activation_func='tanh',
critic_kernel_initializer='glorot_uniform',
critic_learning_rate=0.001,
critic_bacth_size=128,
actor_lstm_layers=[1,512],
actor_mlp_layers=[3,512],
actor_activation_func='tanh',
actor_kernel_initializer='glorot_uniform',
actor_learning_rate=0.0001,
actor_bacth_size=512,
entropy_coef=0.5,
entropy_decoy=math.pow(0.1/0.5, 1.0/(episode_num//train_every)),
max_grad_norm = 1,)
if namespace.load_model is not None:
agent_test.load_model(namespace.load_model)
env_rand.set_agents([agent_test, agent_rand])
eval_env.set_agents([agent_test, agent_rand])
# Init a Logger to plot the learning curve
logger = Logger(log_dir+'/'+test_name)
envs = [env_rand,
]
env_num = len(envs)
for episode in range(episode_num // env_num):
# Generate data from the
for env in envs:
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for ts in trajectories[0]:
agent_test.feed(ts)
if episode % (train_every // env_num) == 0:
agent_test.train()
if episode % (save_every // env_num) == 0 :
# Save model
if not os.path.exists(save_dir+'/'+test_name+str(episode*env_num)):
os.makedirs(save_dir+'/'+test_name+str(episode*env_num))
agent_test.save_model(save_dir+'/'+test_name+str(episode*env_num))
# Evaluate the performance. Play with random agents.
if episode % (evaluate_every // env_num) == 0:
print('episode: ', episode*env_num)
logger.log_performance(episode*env_num, tournament(eval_env, evaluate_num)[0])
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot(dir_name)
# Save model
if not os.path.exists(save_dir+'/'+test_name+str(episode_num)):
os.makedirs(save_dir+'/'+test_name+str(episode_num))
agent_test.save_model(save_dir+'/'+test_name+str(episode_num))
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'))
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_func = env_name_to_env_func[args.env]
env = env_func.env()
env.seed(args.seed)
env.reset()
# Initialize the agent and use random agents as opponents
learning_agent_name = env.agents[0]
if args.algorithm == 'dqn':
from rlcard.agents.pettingzoo_agents import DQNAgentPettingZoo
agent = DQNAgentPettingZoo(
num_actions=env.action_space(learning_agent_name).n,
state_shape=env.observation_space(
learning_agent_name)["observation"].shape,
mlp_layers=[64, 64],
device=device)
elif args.algorithm == 'nfsp':
from rlcard.agents.pettingzoo_agents import NFSPAgentPettingZoo
agent = NFSPAgentPettingZoo(
num_actions=env.action_space(learning_agent_name).n,
state_shape=env.observation_space(
learning_agent_name)["observation"].shape,
hidden_layers_sizes=[64, 64],
q_mlp_layers=[64, 64],
device=device)
agents = {learning_agent_name: agent}
for i in range(1, env.num_agents):
agents[env.agents[i]] = RandomAgentPettingZoo(
num_actions=env.action_space(env.agents[i]).n)
# Start training
num_timesteps = 0
with Logger(args.log_dir) as logger:
for episode in range(args.num_episodes):
if args.algorithm == 'nfsp':
agent.sample_episode_policy()
# Generate data from the environment
trajectories = run_game_pettingzoo(env, agents, is_training=True)
trajectories = reorganize_pettingzoo(trajectories)
num_timesteps += sum([len(t) for t in trajectories.values()])
for ts in trajectories[learning_agent_name]:
agent.feed(ts)
# Evaluate the performance. Play with random agents.
if episode % args.evaluate_every == 0:
average_rewards = tournament_pettingzoo(
env, agents, args.num_eval_games)
logger.log_performance(num_timesteps,
average_rewards[learning_agent_name])
# 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)
agents = []
for i in range(env.player_num):
agent = DeepCFR(sess, scope='deepcfr' + str(i), env=env)
agents.append(agent)
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents(agents)
eval_env.set_agents([agents[0], random_agent])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# restore checkpoint
saver = tf.train.Saver()
save_dir = 'models/nolimit_holdem_deepcfr'
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
for agent in agents:
agent.train()
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
_reward = tournament(eval_env, evaluate_num)[0]
logger.log_performance(episode, _reward)
# Save model
if _reward > _reward_max:
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver.save(sess, os.path.join(save_dir, 'model'))
def nfsp():
import tensorflow as tf
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': False,
'game_player_num': 2
})
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
})
# Set the iterations numbers and how frequently we evaluate the performance
# The intial memory size
memory_init_size = 1000
# The paths for saving the logs and learning curves
log_dir = './experiments/nolimit_holdem_nfsp_result/1v1MCNFSPv3'
# Set a global seed
set_global_seed(0)
graph = tf.Graph()
sess = tf.Session(graph=graph)
evaluate_every = 1000
evaluate_num = 250
episode_num = 5000
# The intial memory size
memory_init_size = 1500
# Train the agent every X steps
train_every = 256
agents = []
with graph.as_default():
# Model1v1V3cp10good
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=.1,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_init_size=memory_init_size,
train_every=train_every,
q_train_every=train_every,
q_mlp_layers=[512, 512]))
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=0.075,
rl_learning_rate=0.075,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_init_size=memory_init_size,
train_every=train_every // 2,
q_train_every=train_every // 2,
q_mlp_layers=[512, 512]))
# check_point_path = os.path.join('models\\nolimit_holdem_nfsp\\1v1MCNFSPv3\\cp\\10')
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))
global_step = tf.Variable(0, name='global_step', trainable=False)
random_agent = RandomAgent(action_num=eval_env2.action_num)
#easy_agent = nfsp_agents[0]
print(agents)
# print(nfsp_agents)
env.set_agents(agents)
eval_env.set_agents(agents)
eval_env2.set_agents([agents[0], 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):
# First sample a policy for the episode
for agent in agents:
agent.sample_episode_policy()
table = []
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for i in range(env.player_num):
for ts in trajectories[i]:
agents[i].feed(ts, table)
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
logger.log(
'\n\n\n---------------------------------------------------------------\nTournament '
+ str(episode / evaluate_every))
res = tournament(eval_env, evaluate_num)
res2 = tournament(eval_env2, evaluate_num // 4)
logger.log_performance(env.timestep, res[0])
logger.log_performance(env.timestep, res2[0])
logger.log('' + str(episode_num) + " - " + str(episode) +
'\n')
logger.log(
'\n\n----------------------------------------------------------------'
)
if episode % (evaluate_every) == 0 and not episode == 0:
save_dir = 'models/nolimit_holdem_nfsp/1v1MCNFSPv3/cp/10/good' + str(
episode // evaluate_every)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.train.Saver()
saver.save(sess, os.path.join(save_dir, 'model'))
logger.log(
'\n\n\n---------------------------------------------------------------\nTournament '
+ str(episode / evaluate_every))
res = tournament(eval_env, evaluate_num)
logger.log_performance(env.timestep, res[0])
logger.log('' + str(episode_num) + " - " + str(episode))
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('NFSP')
# Save model
save_dir = 'models/nolimit_holdem_nfsp/1v1MCNFSPv3/cp/10/good'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.train.Saver()
saver.save(sess, os.path.join(save_dir, 'model'))
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')
learning_rate=1e-5,
strategy_memory_capacity=2 * int(1e6))
agents.append(agent)
for _ in range(env.player_num - 1):
agent = RandomAgent(action_num=eval_env.action_num)
random_agents.append(agent)
env.set_agents(agents)
eval_env.set_agents([agents[0], *random_agents])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
# Create dir for results
save_dir = 'models/thousand_schnapsen_deep_cfr3'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.train.Saver()
best_win_rate = 0
for episode in range(episode_num):
agents[0].train()
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
payoffs, wins = tournament(eval_env, evaluate_num)
logger.log_performance(env.timestep, payoffs[0])
duration = args.d
explore = args.e
model_action = args.ma
model_hand_rank = args.mh
# Make environment
env = rlcard.make('limit-holdem', config={'seed': 0})
eval_env = rlcard.make('limit-holdem', config={'seed': 10})
#episode_num = 5
num_tournaments = 25
# episode_num = 100
# evaluate_every = 10
evaluate_num = 1000
log_dir = name
logger = Logger(log_dir)
# Set a global seed
set_global_seed(0)
# Set up agents
agent1 = limitholdem_rule_models.LimitholdemRuleAgentV1()
agent2 = MCTS_Agent(action_num=env.action_num,
duration=duration,
exploration=explore,
model_action=model_action,
model_hand_rank=model_hand_rank)
env.set_agents([agent2, agent1])
eval_env.set_agents([agent2, agent1])
for i in range(num_tournaments):
def train_uno():
# Make environment
env = rlcard.make("uno", config={"seed": 0})
eval_env = rlcard.make("uno", config={"seed": 0})
# Set the iterations numbers and how frequently we evaluate the performance
evaluate_every = 100
evaluate_num = 1000
episode_num = 3000
# The intial memory size
memory_init_size = 1000
# Train the agent every X steps
train_every = 100
# The paths for saving the logs and learning curves
log_dir = "./experiments/uno_results_dqn/"
# Set a global seed
set_global_seed(0)
params = {
"scope": "DQN-Agent",
"num_actions": env.action_num,
"replay_memory_size": memory_init_size,
"num_states": env.state_shape,
"discount_factor": 0.99,
"epsilon_start": 1.0,
"epsilon_end": 0.1,
"epsilon_decay_steps": 20000,
"batch_size": 32,
"train_every": 1,
"mlp_layers": [512, 512],
"lr": 0.0005,
}
agent_conf = DQN_conf(**params)
agent = DQN_agent(agent_conf)
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents([agent, random_agent])
eval_env.set_agents([agent, random_agent])
logger = Logger(log_dir)
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:
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 UNO")
# Save model
save_dir = "models/uno_dqn_pytorch"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
state_dict = agent.get_state_dict()
print(state_dict.keys())
torch.save(state_dict, os.path.join(save_dir, "model.pth"))
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)
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])
mcts_agent = MCTS_Agent(env.action_num, duration, explore, model_action,
model_hand_rank)
env.set_agents([mcts_agent, dqn_agent])
eval_env.set_agents([mcts_agent, dqn_agent])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curve
logger_mcts = Logger(log_dir_mcts)
logger_dqn = Logger(log_dir_dqn)
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]:
dqn_agent.feed(ts)
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
logger_mcts.log_performance(env.timestep,
tournament(eval_env, evaluate_num)[0])
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)
# 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)
agent.feed(ts)
if timestep % evaluate_every == 0:
rewards = []
state = eval_env.reset()
for _ in range(evaluate_num):
action, _ = agent.eval_step(state)
_, reward, done = env.step(action)
# Set up the agents
agent = RandomAgent(action_num=env.action_num)
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents([agent, random_agent, random_agent])
eval_env.set_agents([random_agent, random_agent, random_agent])
env.set_landlord_score(landlord_score)
eval_env.set_landlord_score(landlord_score)
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curve
log_dir = './experiments/doudizhu_random_result/'
logger = Logger(log_dir)
logger.log_parameters(parameter_dict)
for episode in range(episode_num):
## dont need these for random agent
# 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:
payoffs, peasant_wins, landlord_wins = tournament(
q_replay_memory_size=int(1e5),
q_replay_memory_init_size=memory_init_size,
train_every=train_every,
q_train_every=train_every,
q_batch_size=256,
q_mlp_layers=[512, 1024, 2048, 1024, 512])
agents.append(agent)
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents(agents)
eval_env.set_agents([agents[0], random_agent])
# Initialize global variables
sess.run(tf.compat.v1.global_variables_initializer())
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
# First sample a policy for the episode
for agent in agents:
agent.sample_episode_policy()
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for i in range(env.player_num):
for ts in trajectories[i]:
agents[i].feed(ts)
actor_layers=[64, 64],
critic_layers=[64, 64],
)
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())
# Include this line to verify graph not being updated in each iteration. This helps identify memory leaks.
# Leave uncommented since tf.train.Saver() below is a graph operation.
# sess.graph.finalize()
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
start_time = time.time()
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:
if episode > 0:
current_time = time.time()
episodes_per_sec = episode / (current_time - start_time)
remaining_mins = (episode_num -
def nfsp():
import tensorflow as tf
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={
'game_player_num': 2,
'seed': 477
})
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
# The intial memory size
memory_init_size = 1000
# The paths for saving the logs and learning curves
log_dir = './experiments/nolimit_holdem_nfsp_result/no_all_in'
# Set a global seed
set_global_seed(477)
graph = tf.Graph()
tf.ConfigProto()
sess = tf.Session(graph=graph)
evaluate_every = 2048
evaluate_num = 32
episode_num = 24576
# The intial memory size
memory_init_size = 256
# Train the agent every X steps
train_every = 256
agents = []
with graph.as_default():
"""
def __init__(self,
sess,
scope,
action_num=4,
state_shape=None,
hidden_layers_sizes=None,
reservoir_buffer_capacity=int(1e6),
anticipatory_param=0.1,
batch_size=256,
train_every=1,
rl_learning_rate=0.1,
sl_learning_rate=0.005,
min_buffer_size_to_learn=1000,
q_replay_memory_size=30000,
q_replay_memory_init_size=1000,
q_update_target_estimator_every=1000,
q_discount_factor=0.99,
q_epsilon_start=0.06,
q_epsilon_end=0,
q_epsilon_decay_steps=int(1e6),
q_batch_size=256,
q_train_every=1,
q_mlp_layers=None,
evaluate_with='average_policy'):
"""
# Model1v1V3cp10good
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=.7,
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]))
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=.7,
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]))
# check_point_path = os.path.join('models\\nolimit_holdem_nfsp\\iivan')
print(
'-------------------------------------------------------------------------------------'
)
# print(check_point_path)
#todays project :)
# https://stackoverflow.com/questions/33758669/running-multiple-tensorflow-sessions-concurrently
with sess.as_default():
with graph.as_default():
# saver = tf.train.Saver()
# saver.restore(sess, tf.train.latest_checkpoint(check_point_path))
global_step = tf.Variable(0, name='global_step', trainable=False)
random_agent = RandomAgent(action_num=eval_env2.action_num)
env.set_agents(agents)
eval_env.set_agents([agents[0], random_agent])
eval_env2.set_agents([random_agent, agents[1]])
# eval_env3.set_agents([agents[1], 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):
print(episode, end='\r')
#print('oh')
# First sample a policy for the episode
for agent in agents:
agent.sample_episode_policy()
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for i in range(env.player_num):
for ts in trajectories[i]:
agents[i].feed(ts)
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
logger.log(
'\n\n\n---------------------------------------------------------------\nTournament '
+ str(episode / evaluate_every))
# tournament(eval_env2, 6)
# exploitability.exploitability(eval_env, agents[0], 500)
res = tournament(env, evaluate_num)
logger.log_performance(env.timestep, res[0])
res2 = tournament(eval_env, evaluate_num // 3)
logger.log_performance(env.timestep, res2[0])
res3 = tournament(eval_env2, evaluate_num // 3)
logger.log_performance(env.timestep, res3[0])
logger.log('' + str(episode_num) + " - " + str(episode) +
'\n')
logger.log(
'\n\n----------------------------------------------------------------'
)
if episode % (evaluate_every) == 0 and not episode == 0:
save_dir = 'models/nolimit_holdem_nfsp/no_all_in/cp/' + str(
episode // evaluate_every)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.train.Saver()
saver.save(sess, os.path.join(save_dir, 'model'))
logger.log(
'\n\n\n---------------------------------------------------------------\nTournament '
+ str(episode / evaluate_every))
res = tournament(eval_env, evaluate_num)
logger.log_performance(env.timestep, res[0])
logger.log('' + str(episode_num) + " - " + str(episode))
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('NFSP')
# Save model
save_dir = 'models/nolimit_holdem_nfsp/no_all_in'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.train.Saver()
saver.save(sess, os.path.join(save_dir, 'model'))
train_every=train_every,
q_train_every=train_every,
q_batch_size=256,
q_mlp_layers=[512, 1024, 2048, 1024, 512])
agents.append(agent)
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents(agents)
eval_env.set_agents([agents[0], random_agent])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curvefrom rlcard.agents.random_agent import RandomAgent
logger = Logger(log_dir)
for episode in range(episode_num):
print("Episode: " + str(episode))
# First sample a policy for the episode
for agent in agents:
agent.sample_episode_policy()
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for i in range(env.player_num):
for ts in trajectories[i]:
agents[i].feed(ts)
from rlcard.utils import set_global_seed, tournament
from SeedRanomAgent import SRandomAgent
from rlcard.utils import Logger
from eval_util import *
# Set the iterations numbers and how frequently we evaluate/save plot
evaluate_num = 100
emu_num = 50
log_dir = './experiments/doudizhu_mcts_vs_drqn_result/'
best_model_path = './models/doudizhu_train_drqn_as_L_vs_random_and_eval_vs_random_best.npy'
# Set a global seed
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
logger.log("MCTS-UCT VS DRQN")
env = rlcard.make('doudizhu', config={'seed': 0, 'allow_step_back': True})
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
sess = tf.Session(config=config)
drqn_agent = DRQNAgent(sess,
scope='doudizhu_drqn',
action_num=env.action_num,
memory_init_size=3000,
memory_size=6000,
elif(role==2):
agent_list = [random_agent, random_agent, agent]
parameter_dict['always_peasant_2'] = True
'''
# set agents in environment
env.set_agents(agent_list)
eval_env.set_agents(agent_list)
env.set_landlord_score(landlord_score)
eval_env.set_landlord_score(landlord_score)
eval_env.set_eval_agent(role)
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curve
log_dir = './results/doudizhu_dqn_result/'
logger = Logger(log_dir)
logger.log_parameters(parameter_dict)
role_counter = role
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:
train_every=train_every,
q_train_every=train_every,
q_batch_size=256,
q_mlp_layers=[512, 1024, 2048, 1024, 512])
agents.append(agent)
random_agent = RandomAgent(action_num=eval_env.action_num)
env.set_agents(agents)
eval_env.set_agents([agents[0], random_agent, random_agent])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curvefrom rlcard.agents.random_agent import RandomAgent
logger = Logger(log_dir)
for episode in range(episode_num):
# First sample a policy for the episode
for agent in agents:
agent.sample_episode_policy()
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
# Feed transitions into agent memory, and train the agent
for i in range(env.player_num):
for ts in trajectories[i]:
agents[i].feed(ts)
episode_num = 100000 # The intial memory size memory_init_size = 100 memory_size = 6000 # Train the agent every X steps train_every = 1 # The paths for saving the logs and learning curves log_dir = './experiments/doudizhu_train_dqn_as_L_vs_random_and_eval_vs_random' loss_log = os.path.join(log_dir,'loss') L_WR_log = os.path.join(log_dir,'L_WR') P_WR_log = os.path.join(log_dir,'P_WR') logger = Logger(log_dir) loss_logger = Logger(loss_log) L_WR_logger = Logger(L_WR_log) P_WR_logger = Logger(P_WR_log) best_model_path = './models/doudizhu_train_dqn_as_L_vs_random_and_eval_vs_random_best' max_P_WR = 0.0 max_L_WR = 0.0 # Set a global seed set_global_seed(0) config = tf.ConfigProto() config.gpu_options.allow_growth = True os.environ["CUDA_VISIBLE_DEVICES"] = "2"
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):
# 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:
logger.log_performance(env.timestep,
tournament(eval_env, evaluate_num)[0])
# env.set_agents([agents[0], rule_agent, agents[0], rule_agent])
# eval_env.set_agents([agents[0], rule_agent, agents[0], rule_agent])
# 4 dqn agent with single brain
env.set_agents([agents[0], agents[0], agents[0], agents[0]])
eval_env.set_agents([agents[0], rule_agent, agents[0], rule_agent])
# 4 dqn agent with two brains
# env.set_agents([agents[0], agents[1], agents[0], agents[1]])
# eval_env.set_agents([agents[0], rule_agent, agents[0], rule_agent])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curve
logger = Logger(save_dir)
# Init moving average calculator
m_avg = MovingAvg(100)
payoff_avg = MovingAvg(100)
# load the pre-trained model
check_point_path = os.path.join(TRACTOR_PATH, 'tractor_dqn_430k')
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(check_point_path))
graph = tf.get_default_graph()
print('INFO: Loaded model from {}'.format(check_point_path))
t = trange(episode_num, desc='rl-loss:', leave=True)
for episode in t:
# Generate data from the environment
