def test_log(self):
log_dir = "./newtest/test_log.txt"
if os.path.exists(log_dir):
shutil.rmtree(log_dir)
logger = Logger(log_dir)
logger.log("test text")
logger.log_performance(1, 1)
logger.log_performance(2, 2)
logger.log_performance(3, 3)
logger.close_files()
logger.plot('aaa')
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)
if done:
rewards.append(reward)
logger.log_performance(env.timestep, np.mean(rewards))
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('DQN')
# Save model
save_dir = 'models/uno_single_dqn'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.train.Saver()
saver.save(sess, os.path.join(save_dir, 'model'))
# The paths for saving the logs and learning curves
log_dir = './experiments/leduc_holdem_cfr_result/'
# Set a global seed
set_global_seed(0)
# Initilize CFR Agent
agent = CFRAgent(env)
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('CFR')
scope="dqn", collection=tf.GraphKeys.TRAINABLE_VARIABLES)
variables = [var.eval() for var in variables]
for task in tasks:
INPUT_QUEUE.put((task, False, variables, agent.total_t))
for _ in range(evaluate_num):
payoffs = OUTPUT_QUEUE.get()
reward += payoffs[0]
logger.log('\n########## Evaluation ##########')
logger.log('Average reward is {}'.format(
float(reward) / evaluate_num))
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('DQN_multi_process')
# Save model
save_dir = 'models/leduc_dqn_multi'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.train.Saver()
saver.save(sess, os.path.join(save_dir, 'model'))
# Close multi-processes
for _ in range(PROCESS_NUM):
INPUT_QUEUE.put(None)
INPUT_QUEUE.join()
for p in PROCESSES:
def train_mahjong():
# Make environment
env = rlcard.make('mahjong', config={'seed': 0})
eval_env = rlcard.make('mahjong', config={'seed': 0})
# Set the iterations numbers and how frequently we evaluate the performance
evaluate_every = 1000
evaluate_num = 1000
episode_num = 10000
# 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 = './experiments/mahjong_nfsp_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
agents = []
for i in range(env.player_num):
agent = NFSPAgent(sess,
scope='nfsp' + str(i),
action_num=env.action_num,
state_shape=env.state_shape,
hidden_layers_sizes=[512, 512],
anticipatory_param=0.5,
batch_size=256,
rl_learning_rate=0.00005,
sl_learning_rate=0.00001,
min_buffer_size_to_learn=memory_init_size,
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, 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, 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 tqdm(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)
# 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('NFSP')
# Save model
save_dir = 'models/mahjong_nfsp'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.train.Saver()
saver.save(sess, os.path.join(save_dir, 'model'))
# 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_performance(env.timestep,
tournament(eval_env, evaluate_num)[0])
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('NFSP')
# Save model
save_dir = 'models/limit_holdem_nfsp'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
saver = tf.train.Saver()
saver.save(sess, os.path.join(save_dir, 'model'))
log_dir = './experiments/leduc_holdem_br_result/'
# Set a global seed
set_global_seed(0)
# Initilize CFR Agent
opponent = CFRAgent(env)
#opponent = RandomAgent(action_num=env.action_num)
#opponent.load() # If we have saved model, we first load the model
#agent = RandomAgent(action_num=env.action_num)
agent = BRAgent(eval_env, opponent)
#agent = CFRAgent(env)
eval_env.set_agents([agent, opponent])
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
for episode in range(episode_num):
opponent.train()
#agent.train()
print('\rIteration {}'.format(episode), end='')
# Evaluate the performance. Play with NFSP 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()
logger.plot('BR')
# 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])
# Close files in the logger
logger.close_files()
# Plot the learning curve
logger.plot('DQN_Keras')
# Save model
save_dir = 'models/blackjack_dqn_keras'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, 'model.keras')
agent.save_model(model_path)
print("model saved")
state_shape=env.state_shape,
discount_factor=0.99,
learning_rate=1e-6,
device=None)
env.set_agents([agent])
eval_env.set_agents([agent])
logger = Logger(log_dir)
for episode in range(episode_num):
trajectories, _ = env.run(is_training=True)
for ts in trajectories[0]:
agent.feed(ts)
loss = agent.train()
# logger.log(f"Loss: {loss}")
if episode % evaluate_every == 0:
logger.log_performance(env.timestep,
tournament(eval_env, evaluate_num)[0])
logger.close_files()
logger.plot("REINFORCE")
# Save model
save_dir = 'models/blackjack_reinforce'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
torch.save(agent.get_state_dict(), os.path.join(save_dir, 'model.pth'))