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)
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
logger.log_performance(
episode,
tournament(eval_env, evaluate_num)[0]
) # tournament over evaluate_num times, returns avg reward of agent [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'))
