def main(unused_argv):
begin = time.time()
env = Go()
agents = [agent.Random_Rollout_MCTS_Agent(n_playout=100), agent.RandomAgent(1)]
ret = []
for ep in range(NUM_EPISODES):
time_step = env.reset()
print('start ep: %d'%ep)
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step, env)
else:
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
time_step = env.step(action_list)
# Episode is over, step all agents with final info state.
agents[0].step(time_step, env)
agents[1].step(time_step)
ret.append(time_step.rewards[0])
print('end')
print(np.mean(ret))
# print(ret)
print('Time elapsed:', time.time()-begin)
def main(unused_argv):
begin = time.time()
env = Go()
info_state_size = env.state_size
num_actions = env.action_size
agentR = agent.RandomAgent(0)
hidden_layers_sizes = [int(l) for l in FLAGS.hidden_layers_sizes]
kwargs = {
"replay_buffer_capacity": FLAGS.replay_buffer_capacity,
"epsilon_decay_duration": int(0.6 * FLAGS.num_train_episodes),
"epsilon_start": 0.8,
"epsilon_end": 0.001,
"learning_rate": 1e-3,
"learn_every": FLAGS.learn_every,
"batch_size": 128,
"max_global_gradient_norm": 10,
}
ret = [0]
max_len = 2000
with tf.Session() as sess:
dqn = DQN(sess, 0, info_state_size, num_actions, hidden_layers_sizes,
**kwargs)
dqn.restore("saved_model/10000")
for ep in range(10):
print("start mcts train ep" + str(ep))
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0: #用MCTS
root = Node(None, env, time_step, None, 0, 0)
mcts = MCTS(root,
dqn,
random_value_net,
random_rollout_net,
env,
time_limit=5)
action_list = mcts.start()
else:
agent_output = agentR.step(time_step).action
action_list = agent_output #获得动作
#print(action_list,player_id)
time_step = env.step(action_list)
print(time_step.rewards)
print('Time elapsed:', time.time() - begin)
def main(unused_argv):
begin = time.time()
env = Go()
agents = [RandomAgent(idx) for idx in range(2)]
for ep in range(FLAGS.num_eval):
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step)
else:
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
time_step = env.step(action_list)
print(time_step.observations["info_state"][0])
# Episode is over, step all agents with final info state.
# for agent in agents:
agents[0].step(time_step)
agents[1].step(time_step)
print(time_step.rewards, env.get_current_board())
print('Time elapsed:', time.time() - begin)
def main(unused_argv):
begin = time.time()
env = Go()
info_state_size = env.state_size
num_actions = env.action_size
hidden_layers_sizes = [int(l) for l in FLAGS.hidden_layers_sizes]
kwargs = {
"pi_learning_rate": 1e-2,
"critic_learning_rate": 1e-1,
"batch_size": 128,
"entropy_cost": 0.5,
"max_global_gradient_norm": 20,
}
import agent.agent as agent
ret = [0]
max_len = 2000
with tf.Session() as sess:
# agents = [DQN(sess, _idx, info_state_size,
# num_actions, hidden_layers_sizes, **kwargs) for _idx in range(2)]
agents = [PolicyGradient(sess, 0, info_state_size,
num_actions, hidden_layers_sizes, **kwargs), agent.RandomAgent(1)]
sess.run(tf.global_variables_initializer())
for ep in range(FLAGS.num_train_episodes):
if (ep + 1) % FLAGS.eval_every == 0:
losses = agents[0].loss
logging.info("Episodes: {}: Losses: {}, Rewards: {}".format(ep+1, losses, np.mean(ret)))
with open('log_pg_{}'.format(os.environ.get('BOARD_SIZE')), 'a+') as log_file:
log_file.writelines("{}, {}\n".format(ep+1, np.mean(ret)))
time_step = env.reset() # a go.Position object
while not time_step.last():
player_id = time_step.observations["current_player"]
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
time_step = env.step(action_list)
for agent in agents:
agent.step(time_step)
if len(ret) < max_len:
ret.append(time_step.rewards[0])
else:
ret[ep % max_len] = time_step.rewards[0]
ret = []
for ep in range(FLAGS.num_eval):
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step, is_evaluation=True)
else:
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
time_step = env.step(action_list)
# Episode is over, step all agents with final info state.
# for agent in agents:
agents[0].step(time_step, is_evaluation=True)
agents[1].step(time_step)
ret.append(time_step.rewards[0])
print(np.mean(ret))
print('Time elapsed:', time.time()-begin)
def main(unused_argv):
begin = time.time()
env = Go()
info_state_size = env.state_size
num_actions = env.action_size
hidden_layers_sizes = [int(l) for l in FLAGS.hidden_layers_sizes]
kwargs = {
"replay_buffer_capacity": FLAGS.replay_buffer_capacity,
"epsilon_decay_duration": int(0.6 * FLAGS.num_train_episodes),
"epsilon_start": 0.8,
"epsilon_end": 0.001,
"learning_rate": 1e-3,
"learn_every": FLAGS.learn_every,
"batch_size": 128,
"max_global_gradient_norm": 10,
}
import agent.agent as agent
ret = [0]
max_len = 2000
with tf.Session() as sess:
# agents = [DQN(sess, _idx, info_state_size,
# num_actions, hidden_layers_sizes, **kwargs) for _idx in range(2)] # for self play
agents = [
agent.RandomAgent(1),
DQN(sess, 1, info_state_size, num_actions, hidden_layers_sizes,
**kwargs)
]
sess.run(tf.global_variables_initializer())
# train the agent
for ep in range(FLAGS.num_train_episodes):
if (ep + 1) % FLAGS.save_every == 0:
if not os.path.exists("saved_model/random_vs_dqn"):
os.mkdir('saved_model/random_vs_dqn')
agents[1].save(checkpoint_root='saved_model/random_vs_dqn',
checkpoint_name='random_vs_dqn_{}'.format(ep +
1))
print('saved %d' % (ep + 1))
time_step = env.reset() # a go.Position object
while not time_step.last():
player_id = time_step.observations["current_player"]
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
# print(action_list)
time_step = env.step(action_list)
for agent in agents:
agent.step(time_step)
if len(ret) < max_len:
ret.append(time_step.rewards[0])
else:
ret[ep % max_len] = time_step.rewards[0]
# evaluated the trained agent
agents[1].restore("saved_model/random_vs_dqn/random_vs_dqn_10000")
ret = []
for ep in range(FLAGS.num_eval):
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step)
else:
agent_output = agents[player_id].step(
time_step,
is_evaluation=True,
add_transition_record=False)
action_list = agent_output.action
time_step = env.step(action_list)
# Episode is over, step all agents with final info state.
# for agent in agents:
agents[0].step(time_step)
agents[1].step(time_step,
is_evaluation=True,
add_transition_record=False)
ret.append(time_step.rewards[0])
print(np.mean(ret))
# print(ret)
print('Time elapsed:', time.time() - begin)
import random
# import os
#
# os.environ['BOARD_SIZE'] = '7'
from environment.GoEnv import Go
import time
if __name__ == '__main__':
begin = time.time()
env = Go()
for i in range(10):
state = env.reset() # a go.Position object
while True:
# cur_player = env.to_play
_a = env.get_all_legal_moves()
probs = _a / sum(_a)
probs = probs.cumsum()
selection = random.random()
fcoord = probs.searchsorted(selection)
n_s, done, rew, info = env.step(fcoord)
print(fcoord, env.get_current_board())
if done:
break
print('Time elapsed:', time.time() - begin)
import random
from environment.GoEnv import Go
import time
from agent.agent import RandomAgent
if __name__ == '__main__':
begin = time.time()
env = Go(flatten_board_state=False)
agents = [RandomAgent(idx) for idx in range(2)]
for ep in range(10):
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step)
else:
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
time_step = env.step(action_list)
print(time_step.observations["info_state"][0])
# Episode is over, step all agents with final info state.
for agent in agents:
agent.step(time_step)
print('Time elapsed:', time.time() - begin)
def main(unused_argv):
begin = time.time()
env = Go()
ret = [0]
policy_function = [
'saved_model/dqn_vs_random/10000',
'saved_model/random_vs_dqn/random_vs_dqn_10000'
]
value_function = 'saved_model/dqn_vs_random/10000'
agents = [
agent.Net_MCTS_Agent(value_function, policy_function, n_playout=50),
agent.RandomAgent(1)
]
for ep in range(NUM_TRAIN):
if (ep + 1) % NUM_SAVE_EVERY == 0:
if not os.path.exists("saved_model/net_mcts_vs_random"):
os.mkdir('saved_model/net_mcts_vs_random')
agents[0].mcts._policy_fn[0].save(
checkpoint_root='saved_model/net_mcts_vs_random',
checkpoint_name='_policy_fn_0_{}'.format(ep + 1))
agents[0].mcts._policy_fn[1].save(
checkpoint_root='saved_model/net_mcts_vs_random',
checkpoint_name='_policy_fn_1_{}'.format(ep + 1))
agents[0].mcts._value_fn.save(
checkpoint_root='saved_model/net_mcts_vs_random',
checkpoint_name='_value_fn_{}'.format(ep + 1))
time_step = env.reset() # a new env
print('start ep: %d' % ep)
while not time_step.last(): # play until the game is over
cur_player = time_step.observations["current_player"]
state = time_step.observations["info_state"][cur_player]
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step, env)
else:
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
time_step = env.step(action_list)
print('end')
agents[0].step(time_step, env)
agents[1].step(time_step)
if len(ret) < max_len:
ret.append(time_step.rewards[0])
else:
ret[ep % max_len] = time_step.rewards[0]
# evaluated the trained mcts agent
ret = []
for ep in range(NUM_EVAL):
print('eval ep: %d' % ep)
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step, env)
else:
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
time_step = env.step(action_list)
# Episode is over, step all agents with final info state.
agents[0].step(time_step, env)
agents[1].step(time_step)
ret.append(time_step.rewards[0])
print(np.mean(ret))
print(ret)
print('Time elapsed:', time.time() - begin)
def main(unused_argv):
begin = time.time()
env = Go()
info_state_size = env.state_size
num_actions = env.action_size
hidden_layers_sizes = [int(l) for l in FLAGS.hidden_layers_sizes]
kwargs = {
"replay_buffer_capacity": FLAGS.replay_buffer_capacity,
"epsilon_decay_duration": int(0.6*FLAGS.num_train_episodes),
"epsilon_start": 0.8,
"epsilon_end": 0.001,
"learning_rate": 1e-3,
"learn_every": FLAGS.learn_every,
"batch_size": 128,
"max_global_gradient_norm": 10,
}
import agent.agent as agent
ret = [0]
max_len = 2000
with tf.Session() as sess:
agents = [DQN(sess, 0, info_state_size,
num_actions, hidden_layers_sizes, **kwargs), agent.Random_Rollout_MCTS_Agent(max_simulations=50)]
sess.run(tf.global_variables_initializer())
# train the agent
for ep in range(FLAGS.num_train_episodes):
if (ep + 1) % FLAGS.eval_every == 0:
losses = agents[0].loss
logging.info("Episodes: {}: Losses: {}, Rewards: {}".format(ep + 1, losses, np.mean(ret)))
with open('log/log_{}_{}'.format(os.environ.get('BOARD_SIZE'), begin), 'a+') as log_file:
log_file.writelines("{}, {}\n".format(ep+1, np.mean(ret)))
if (ep + 1) % FLAGS.save_every == 0:
if not os.path.exists("saved_model"):
os.mkdir('saved_model')
agents[0].save(checkpoint_root='saved_model', checkpoint_name='{}'.format(ep+1))
time_step = env.reset() # a go.Position object
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step)
else:
agent_output = agents[player_id].step(time_step, env)
action_list = agent_output.action
time_step = env.step(action_list)
# for agent in agents:
agents[0].step(time_step)
agents[1].step(time_step, env)
if len(ret) < max_len:
ret.append(time_step.rewards[0])
else:
ret[ep % max_len] = time_step.rewards[0]
# evaluated the trained agent
agents[0].restore("saved_model/10000")
ret = []
for ep in range(FLAGS.num_eval):
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step, is_evaluation=True, add_transition_record=False)
else:
agent_output = agents[player_id].step(time_step, env)
action_list = agent_output.action
time_step = env.step(action_list)
# Episode is over, step all agents with final info state.
# for agent in agents:
agents[0].step(time_step, is_evaluation=True, add_transition_record=False)
agents[1].step(time_step, env)
ret.append(time_step.rewards[0])
print(np.mean(ret))
print('Time elapsed:', time.time()-begin)
def main(unused_argv):
begin = time.time()
env = Go()
info_state_size = env.state_size
num_actions = env.action_size
num_cnn_layer = len(FLAGS.output_channels)
kernel_shapes = [3 for _ in range(num_cnn_layer)]
strides = [1 for _ in range(num_cnn_layer)]
paddings = ["SAME" for _ in range(num_cnn_layer - 1)]
paddings.append("VALID")
cnn_parameters = [FLAGS.output_channels, kernel_shapes, strides, paddings]
#hidden_layers_sizes = [int(l) for l in FLAGS.hidden_layers_sizes]
hidden_layers_sizes = [int(l) for l in FLAGS.hidden_layers_sizes]
kwargs = {
"pi_learning_rate": 3e-4,
"critic_learning_rate": 1e-3,
"batch_size": 128,
"entropy_cost": 0.5,
"max_global_gradient_norm": 20,
}
import agent.agent as agent
ret = [0]
max_len = 2000
with tf.Session() as sess:
# agents = [DQN(sess, _idx, info_state_size,
# num_actions, hidden_layers_sizes, **kwargs) for _idx in range(2)]
agents = [
PolicyGradient(sess, 0, info_state_size**0.5, num_actions,
cnn_parameters, hidden_layers_sizes, **kwargs),
agent.RandomAgent(1)
]
sess.run(tf.global_variables_initializer())
for ep in range(FLAGS.num_train_episodes):
if (ep + 1) % FLAGS.eval_every == 0:
losses = agents[0].loss
logging.info("Episodes: {}: Losses: {}, Rewards: {}".format(
ep + 1, losses, np.mean(ret)))
with open('log_pg_{}'.format(os.environ.get('BOARD_SIZE')),
'a+') as log_file:
log_file.writelines("{}, {}\n".format(
ep + 1, np.mean(ret)))
if (ep + 1) % FLAGS.save_every == 0:
agents[0].save(checkpoint_root='saved_model',
checkpoint_name='{}'.format(ep + 1))
time_step = env.reset() # a go.Position object
while not time_step.last():
player_id = time_step.observations["current_player"]
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
time_step = env.step(action_list)
for agent in agents:
agent.step(time_step)
if len(ret) < max_len:
ret.append(time_step.rewards[0])
else:
ret[ep % max_len] = time_step.rewards[0]
ret = []
agents[0].restore("saved_model/10000")
for ep in range(FLAGS.num_eval):
time_step = env.reset()
while not time_step.last():
player_id = time_step.observations["current_player"]
if player_id == 0:
agent_output = agents[player_id].step(time_step,
is_evaluation=True)
print(agents[0].policy_fn(time_step, player_id))
else:
agent_output = agents[player_id].step(time_step)
action_list = agent_output.action
time_step = env.step(action_list)
# Episode is over, step all agents with final info state.
# for agent in agents:
agents[0].step(time_step, is_evaluation=True)
agents[1].step(time_step)
ret.append(time_step.rewards[0])
print(np.mean(ret))
print('Time elapsed:', time.time() - begin)