def train():
FLAGS(sys.argv)
with sc2_env.SC2Env(
map_name='MoveToBeacon',
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=64,
feature_minimap=64,
rgb_screen=None,
rgb_minimap=None,
action_space=None,
use_feature_units=False),
step_mul=step_mul,
game_steps_per_episode=None,
disable_fog=False,
visualize=True) as env:
sess = tf.Session()
actor = Actor(sess, n_features=2, n_actions=4, lr=0.001)
critic = Critic(sess, n_features=2, lr=0.001)
sess.run(tf.global_variables_initializer())
for episodes in range(EPISODES):
done = False
obs = env.reset()
while not 331 in obs[0].observation["available_actions"]:
actions = actAgent2Pysc2(100,obs)
obs = env.step(actions=[actions])
state = np.array(obs2state(obs))
#print('episode start')
global_step = 0
reward = 0
while not done:
global_step += 1
action = actor.choose_action(state)
actions = actAgent2Pysc2(action,obs)
obs = env.step(actions=[actions])
for i in range(3):
actions = no_operation(obs)
obs = env.step(actions=[actions])
distance = obs2distance(obs)
if global_step == 1:
pre_distance = distance
next_state = np.array(obs2state(obs))
reward = -(distance-pre_distance)*400
if distance < 0.03 or global_step == 200: # 게임 종료시
if distance < 0.03:
reward = 10
if global_step == 200:
reward = -10
done = True
td_error = critic.learn(state, reward, next_state)
actor.learn(state, action, td_error)
if distance < 0.03 or global_step == 200: # 게임 종료시
break
state = next_state
pre_distance = distance
def worker(remote, visualize):
env = sc2_env.SC2Env(
map_name='CollectMineralShards',
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=32,
feature_minimap=32,
rgb_screen=None,
rgb_minimap=None,
action_space=None,
use_feature_units=False),
step_mul=4,
game_steps_per_episode=None,
disable_fog=False,
visualize=True)
done = False
pre_num_mineral = 20
while True:
cmd, action, obs, global_step = remote.recv()
end_step = 400
if cmd == 'step':
if not action == 'done':
available_action, state = obs
a = actAgent2Pysc2(action, state)
obs = env.step(actions=[a])
state = obs2state(obs)
obs = (obs[0].observation.available_actions, state)
#reward shaping
mineral_map = state.reshape(32, 32, 2)
num_mineral = np.sum(mineral_map[:, :, 1])
reward = -0.1
if num_mineral != pre_num_mineral: # when getting mineral
reward = -num_mineral + pre_num_mineral
if num_mineral <= 2 or global_step == end_step - 1: # when getting all mineral or failed to getting all mineral
done = True
pre_num_mineral = num_mineral
remote.send((obs, state, action, reward, done))
else:
remote.send((0, 0, 0, 0, True))
if cmd == 'reset':
pre_num_mineral = 20
done = False
obs = env.reset() #env 초기화
while not 331 in obs[0].observation.available_actions: #마린을 선택하기
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
obs = (obs[0].observation.available_actions, state)
remote.send((obs, state, 0, 0, False))
if cmd == 'close':
remote.close()
break
def train():
FLAGS(sys.argv)
with sc2_env.SC2Env(
map_name='MoveToBeacon',
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=64,
feature_minimap=64,
rgb_screen=None,
rgb_minimap=None,
action_space=None,
use_feature_units=False),
step_mul=step_mul,
game_steps_per_episode=None,
disable_fog=False,
visualize=True) as env:
with tf.Session() as sess:
Policy = Policy_net('policy', 2, 4)
Old_Policy = Policy_net('old_policy', 2, 4)
PPO = PPOTrain(Policy, Old_Policy)
D = Discriminator()
saver = tf.train.Saver()
saver.restore(sess, './model/gail.cpkt')
c = 0
for episodes in range(100000):
done = False
obs = env.reset()
while not 331 in obs[0].observation.available_actions:
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
observations = []
actions_list = []
rewards = []
v_preds = []
reward = 0
global_step = 0
while not done:
global_step += 1
state = np.stack([state]).astype(dtype=np.float32)
act, v_pred = Policy.act(obs=state, stochastic=True)
act, v_pred = np.asscalar(act), np.asscalar(v_pred)
observations.append(state)
actions_list.append(act)
rewards.append(reward)
v_preds.append(v_pred)
actions = actAgent2Pysc2(act, obs)
obs = env.step(actions=[actions])
next_state = obs2state(obs)
distance = obs2distance(obs)
if distance < 0.03 or global_step == 100:
done = True
if done:
v_preds_next = v_preds[1:] + [0]
break
state = next_state
def worker(remote, visualize):
env = sc2_env.SC2Env(
map_name='MoveToBeacon',
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=32,
feature_minimap=32,
rgb_screen=None,
rgb_minimap=None,
action_space=None,
use_feature_units=False),
step_mul=4,
game_steps_per_episode=None,
disable_fog=False,
visualize=False)
done = False
while True:
cmd, action, obs, global_step = remote.recv()
end_step = 400
if cmd == 'step':
if not action == 'done':
available_action, state = obs
a = actAgent2Pysc2(action, state)
obs = env.step(actions=[a])
distance = obs2distance(obs)
state = obs2state(obs)
obs = (obs[0].observation.available_actions, state)
#reward shaping
reward = -0.1
if distance < 0.03 or global_step == end_step - 1:
if distance < 0.03:
reward = 1
if global_step == end_step - 1:
reward = -1
done = True
remote.send((obs, state, action, reward, done))
else:
remote.send((0, 0, 0, 0, True))
if cmd == 'reset':
pre_num_mineral = 20
done = False
obs = env.reset() #env 초기화
while not 331 in obs[0].observation.available_actions: #마린을 선택하기
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
obs = (obs[0].observation.available_actions, state)
remote.send((obs, state, 0, 0, False))
if cmd == 'close':
remote.close()
break
def worker(remote, visualize):
env = sc2_env.SC2Env(
map_name='MoveToBeacon',
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=64,
feature_minimap=64,
rgb_screen=None,
rgb_minimap=None,
action_space=None,
use_feature_units=False),
step_mul=4,
game_steps_per_episode=None,
disable_fog=False,
visualize=True)
done = False
while True:
cmd, action, obs, global_step = remote.recv()
end_step = 100
if cmd == 'step':
if not action == 'done':
#while not 331 in obs[0].observation['available_actions']: #마린을 선택하기
# actions = actAgent2Pysc2(100, obs)
# obs = env.step(actions=[actions])
a = actAgent2Pysc2(action, obs)
obs = env.step(actions=[a])
for i in range(1):
actions = no_operation(obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
distance = obs2distance(obs)
reward = -0.1
obs = obs[0].observation.feature_screen.base[4]
if distance < 0.03 or global_step == end_step - 1:
if distance < 0.03:
reward = 1
done = True
remote.send((obs, state, action, reward, done))
else:
remote.send((0, 0, 0, 0, True))
if cmd == 'reset':
done = False
obs = env.reset() #env 초기화
while not 331 in obs[0].observation['available_actions']: #마린을 선택하기
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
obs = obs[0].observation.feature_screen.base[4]
remote.send((obs, state, 0, 0, False))
if cmd == 'close':
remote.close()
break
def test():
FLAGS(sys.argv)
with sc2_env.SC2Env(map_name="MoveToBeacon", step_mul=step_mul) as env:
sess = tf.Session()
mainDQN = DQN(sess, 2, 4, name='main')
targetDQN = DQN(sess, 2, 4, name='target')
#sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, './Move2Beacon(DQN)/model.cpkt')
copy_ops = get_copy_var_ops(dest_scope_name="target",
src_scope_name="main")
sess.run(copy_ops)
for episodes in range(EPISODES):
done = False
obs = env.reset()
while not 331 in obs[0].observation["available_actions"]:
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
print('episode start')
global_step = 0
random_rate = 0
e = 1. / ((episodes / 10) + 1)
reward = 0
while not done:
time.sleep(0.13)
global_step += 1
action = np.argmax(mainDQN.predict(state))
actions = actAgent2Pysc2(action, obs)
obs = env.step(actions=[actions])
for i in range(1):
actions = no_operation(obs)
obs = env.step(actions=[actions])
distance = obs2distance(obs)
if global_step == 1:
pre_distance = distance
next_state = obs2state(obs)
reward = -(distance - pre_distance) * 400
if distance < 0.015 or global_step == 200: # 게임 종료시
done = True
if distance < 0.015 or global_step == 200: # 게임 종료시
print(reward, episodes, random_rate / global_step)
break
state = next_state
pre_distance = distance
def worker(remote, visualize):
env = sc2_env.SC2Env(map_name='MoveToBeacon',
step_mul=4,
visualize=visualize,
screen_size_px=(64, 64),
minimap_size_px=(64, 64))
done = False
while True:
cmd, action, obs, global_step = remote.recv()
end_step = 100
if cmd == 'step':
if not action == 'done':
while not 331 in obs[0].observation[
'available_actions']: #마린을 선택하기
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
a = actAgent2Pysc2(action, obs)
obs = env.step(actions=[a])
for i in range(1):
actions = no_operation(obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
distance = obs2distance(obs)
reward = -0.01
if distance < 0.03 or global_step == end_step - 1:
if distance < 0.03:
reward = 1
if global_step == end_step - 1:
reward = -1
done = True
remote.send((obs, state, action, reward, done))
else:
remote.send((0, 0, 0, 0, True))
if cmd == 'reset':
done = False
obs = env.reset() #env 초기화
while not 331 in obs[0].observation['available_actions']: #마린을 선택하기
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
remote.send((obs, state, 0, 0, False))
if cmd == 'close':
remote.close()
break
def train():
FLAGS(sys.argv)
with sc2_env.SC2Env(map_name="MoveToBeacon", step_mul=step_mul) as env:
sess = tf.Session()
actor = Actor(sess, n_features=2, n_actions=4, lr=0.001)
critic = Critic(sess, n_features=2, lr=0.001)
sess.run(tf.global_variables_initializer())
for episodes in range(EPISODES):
done = False
obs = env.reset()
while not 331 in obs[0].observation["available_actions"]:
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = np.array(obs2state(obs))
print('episode start')
global_step = 0
reward = 0
while not done:
global_step += 1
time.sleep(0.2)
action = actor.choose_action(state)
actions = actAgent2Pysc2(action, obs)
obs = env.step(actions=[actions])
for i in range(3):
actions = no_operation(obs)
obs = env.step(actions=[actions])
distance = obs2distance(obs)
if global_step == 1:
pre_distance = distance
next_state = np.array(obs2state(obs))
reward = -(distance - pre_distance) * 400
if distance < 0.03 or global_step == 200: # 게임 종료시
if distance < 0.03:
reward = 10
if global_step == 200:
reward = -10
done = True
td_error = critic.learn(state, reward, next_state)
actor.learn(state, action, td_error)
if distance < 0.03 or global_step == 200: # 게임 종료시
break
state = next_state
pre_distance = distance
def train():
FLAGS(sys.argv)
with sc2_env.SC2Env(map_name="MoveToBeacon",
step_mul=step_mul,
screen_size_px=(16, 16),
minimap_size_px=(16, 16)) as env:
Policy = Policy_net('policy', 16 * 16 * 2, 4)
Old_Policy = Policy_net('old_policy', 16 * 16 * 2, 4)
PPO = PPOTrain(Policy, Old_Policy, gamma=0.95)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for episodes in range(EPISODES):
done = False
obs = env.reset()
while not 331 in obs[0].observation["available_actions"]:
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = np.array(obs2state(obs))
print('episode start')
global_step = 0
reward = 0
observations = []
actions_list = []
v_preds = []
rewards = []
while not done:
global_step += 1
time.sleep(0.05)
state = np.stack([state]).astype(dtype=np.float32)
act, v_pred = Policy.act(obs=state, stochastic=True)
act, v_pred = np.asscalar(act), np.asscalar(v_pred)
actions = actAgent2Pysc2(act, obs)
obs = env.step(actions=[actions])
for i in range(1):
actions = no_operation(obs)
obs = env.step(actions=[actions])
distance = obs2distance(obs)
if global_step == 1:
pre_distance = distance
next_state = np.array(obs2state(obs))
reward = -10 * (distance - pre_distance)
#if reward < 0 :
# reward = -0.01
#if reward <= 0:
# reward = 0
#elif reward > 0:
# reward = 0
reward = -0.01
if distance < 0.03 or global_step == 100: # 게임 종료시
if distance < 0.03:
reward = 1
if global_step == 200:
reward = -1
done = True
observations.append(state)
actions_list.append(act)
v_preds.append(v_pred)
rewards.append(reward)
if distance < 0.03 or global_step == 100: # 게임 종료시
v_preds_next = v_preds[1:] + [0]
gaes = PPO.get_gaes(rewards=rewards,
v_preds=v_preds,
v_preds_next=v_preds_next)
observations = np.reshape(observations,
newshape=[-1, 16 * 16 * 2])
actions = np.array(actions_list).astype(dtype=np.int32)
rewards = np.array(rewards).astype(dtype=np.float32)
v_preds_next = np.array(v_preds_next).astype(
dtype=np.float32)
gaes = np.array(gaes).astype(dtype=np.float32)
gaes = (gaes - gaes.mean())
PPO.assign_policy_parameters()
inp = [
observations, actions, rewards, v_preds_next, gaes
]
for epoch in range(5):
sample_indices = np.random.randint(
low=0, high=observations.shape[0],
size=64) # indices are in [low, high)
sampled_inp = [
np.take(a=a, indices=sample_indices, axis=0)
for a in inp
] # sample training data
PPO.train(obs=sampled_inp[0],
actions=sampled_inp[1],
rewards=sampled_inp[2],
v_preds_next=sampled_inp[3],
gaes=sampled_inp[4])
print(episodes, global_step)
break
state = next_state
pre_distance = distance
def train():
FLAGS(sys.argv)
with sc2_env.SC2Env(
map_name='MoveToBeacon',
agent_interface_format=sc2_env.parse_agent_interface_format(
feature_screen=64,
feature_minimap=64,
rgb_screen=None,
rgb_minimap=None,
action_space=None,
use_feature_units=False),
step_mul=step_mul,
game_steps_per_episode=None,
disable_fog=False,
visualize=False) as env:
r = tf.placeholder(tf.float32) ########
rr = tf.summary.scalar('reward', r)
merged = tf.summary.merge_all() ########
expert_observations = np.genfromtxt('trajectory/observations.csv')
expert_actions = np.genfromtxt('trajectory/actions.csv',
dtype=np.int32)
with tf.Session() as sess:
Policy = Policy_net('policy', 2, 4)
Old_Policy = Policy_net('old_policy', 2, 4)
PPO = PPOTrain(Policy, Old_Policy)
D = Discriminator()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
writer = tf.summary.FileWriter('./board/gail',
sess.graph) ########
c = 0
for episodes in range(100000):
done = False
obs = env.reset()
while not 331 in obs[0].observation.available_actions:
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
observations = []
actions_list = []
rewards = []
v_preds = []
reward = 0
global_step = 0
while not done:
global_step += 1
state = np.stack([state]).astype(dtype=np.float32)
act, v_pred = Policy.act(obs=state, stochastic=True)
act, v_pred = np.asscalar(act), np.asscalar(v_pred)
observations.append(state)
actions_list.append(act)
rewards.append(reward)
v_preds.append(v_pred)
actions = actAgent2Pysc2(act, obs)
obs = env.step(actions=[actions])
next_state = obs2state(obs)
distance = obs2distance(obs)
if distance < 0.03 or global_step == 100:
done = True
if done:
v_preds_next = v_preds[1:] + [0]
break
state = next_state
observations = np.reshape(observations, newshape=[-1, 2])
actions_list = np.array(actions_list).astype(dtype=np.int32)
for i in range(2):
sample_indices = (np.random.randint(
expert_observations.shape[0],
size=observations.shape[0]))
inp = [expert_observations, expert_actions]
sampled_inp = [
np.take(a=a, indices=sample_indices, axis=0)
for a in inp
] # sample training data
D.train(expert_s=sampled_inp[0],
expert_a=sampled_inp[1],
agent_s=observations,
agent_a=actions_list)
d_rewards = D.get_rewards(agent_s=observations,
agent_a=actions_list)
d_rewards = np.reshape(d_rewards,
newshape=[-1]).astype(dtype=np.float32)
gaes = PPO.get_gaes(rewards=d_rewards,
v_preds=v_preds,
v_preds_next=v_preds_next)
gaes = np.array(gaes).astype(dtype=np.float32)
v_preds_next = np.array(v_preds_next).astype(dtype=np.float32)
inp = [
observations, actions_list, gaes, d_rewards, v_preds_next
]
PPO.assign_policy_parameters()
for epoch in range(15):
sample_indices = np.random.randint(
low=0, high=observations.shape[0],
size=32) # indices are in [low, high)
sampled_inp = [
np.take(a=a, indices=sample_indices, axis=0)
for a in inp
] # sample training data
PPO.train(obs=sampled_inp[0],
actions=sampled_inp[1],
gaes=sampled_inp[2],
rewards=sampled_inp[3],
v_preds_next=sampled_inp[4])
summary = sess.run(merged, feed_dict={r: global_step})
writer.add_summary(summary, episodes)
if global_step < 50:
c += 1
else:
c = 0
if c > 10:
saver.save(sess, './model/gail.cpkt')
print('save model')
break
print(episodes, global_step, c)
def train():
FLAGS(sys.argv)
with sc2_env.SC2Env(map_name="MoveToBeacon", step_mul=step_mul) as env:
replay_buffer = deque(maxlen=1000)
sess = tf.Session()
mainDQN = DQN(sess, 2, 4, name='main')
targetDQN = DQN(sess, 2, 4, name='target')
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
#saver.restore(sess, './Move2Beacon/model.cpkt')
copy_ops = get_copy_var_ops(dest_scope_name="target",
src_scope_name="main")
sess.run(copy_ops)
for episodes in range(EPISODES):
done = False
obs = env.reset()
while not 331 in obs[0].observation["available_actions"]:
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
print('episode start')
global_step = 0
random_rate = 0
e = 1. / ((episodes / 10) + 1)
reward = 0
while not done:
global_step += 1
time.sleep(0.05)
if np.random.rand() < e:
random_rate += 1
action = random.randrange(4)
else:
action = np.argmax(mainDQN.predict(state))
#action = np.argmax(mainDQN.predict(state))
actions = actAgent2Pysc2(action, obs)
obs = env.step(actions=[actions])
for i in range(3):
actions = no_operation(obs)
obs = env.step(actions=[actions])
distance = obs2distance(obs)
if global_step == 1:
pre_distance = distance
next_state = obs2state(obs)
reward = -(distance - pre_distance) * 400
#print(reward)
if distance < 0.03 or global_step == 200: # 게임 종료시
if distance < 0.03:
reward = 10
if global_step == 200:
reward = -10
done = True
#print(next_state, reward)
replay_buffer.append((state, action, reward, next_state, done))
if distance < 0.03 or global_step == 200: # 게임 종료시
if len(replay_buffer) > BATCH_SIZE:
minibatch = random.sample(replay_buffer, BATCH_SIZE)
loss, _ = replay_train(mainDQN, targetDQN, minibatch)
sess.run(copy_ops)
print('model trained')
saver.save(sess, './Move2Beacon/model.cpkt')
print(reward, episodes, random_rate / global_step)
break
state = next_state
pre_distance = distance
def train():
FLAGS(sys.argv)
with sc2_env.SC2Env(map_name="CollectMineralShards", step_mul=step_mul,
screen_size_px=(32, 32), minimap_size_px=(32, 32)) as env:
Policy = Policy_net('policy', 32*32*2, 4)
Old_Policy = Policy_net('old_policy', 32*32*2, 4)
PPO = PPOTrain(Policy, Old_Policy, gamma=0.95)
saver = tf.train.Saver()
with tf.Session() as sess:
print('a')
saver.restore(sess, './model/model.ckpt')
print('a')
#sess.run(tf.global_variables_initializer())
for episodes in range(EPISODES):
done = False
obs = env.reset()
while not 331 in obs[0].observation["available_actions"]:
actions = actAgent2Pysc2(100, obs)
obs = env.step(actions=[actions])
actions = gather(obs)
obs = env.step(actions=[actions])
end_step = 200
global_step = 0
score = 0
reward = 0
for i in range(100):
time.sleep(0.01)
actions = no_operation(obs)
obs = env.step(actions=[actions])
state = obs2state(obs)
observations = []
actions_list = []
v_preds = []
rewards = []
print('episode start')
while not done:
global_step += 1
time.sleep(0.05)
state = np.stack([state]).astype(dtype=np.float32)
act, v_pred = Policy.act(obs=state, stochastic=True)
act, v_pred = np.asscalar(act), np.asscalar(v_pred)
actions = actAgent2Pysc2(act, obs)
#while not 331 in obs[0].observation["available_actions"]:
# actions = actAgent2Pysc2(100, obs)
# obs = env.step(actions=[actions])
obs = env.step(actions=[actions])
if global_step == end_step or obs2done(obs) >= 1900 : # 게임 time을 다 사용하거나 미네랄을 다 먹었을 경우 게임이 끝난다.
done = True
next_state = obs2state(obs)
reward = obs[0].reward
if reward == 0:
reward = -0.1
if done:
if obs2done(obs) >= 1900: # 게임이 종료되었는데 미네랄을 다 먹었으면
reward = 3
else: # 게임이 종료되었는데 미네랄을 다 못먹으면
reward = -3
score += reward
observations.append(state)
actions_list.append(act)
v_preds.append(v_pred)
rewards.append(reward)
if done: # 게임 종료시
v_preds_next = v_preds[1:] + [0]
gaes = PPO.get_gaes(rewards=rewards, v_preds=v_preds, v_preds_next=v_preds_next)
observations = np.reshape(observations, newshape=[-1, 32*32*2])
actions = np.array(actions_list).astype(dtype=np.int32)
rewards = np.array(rewards).astype(np.float32)
v_preds_next = np.array(v_preds_next).astype(dtype=np.float32)
gaes = np.array(gaes).astype(dtype=np.float32)
gaes = (gaes - gaes.mean())
PPO.assign_policy_parameters()
inp = [observations, actions, rewards, v_preds_next, gaes]
for epoch in range(5):
sample_indices = np.random.randint(low=0, high=observations.shape[0], size=64)
sampled_inp = [np.take(a=a, indices=sample_indices, axis=0) for a in inp] # sample training data
PPO.train(obs=sampled_inp[0],
actions=sampled_inp[1],
rewards=sampled_inp[2],
v_preds_next=sampled_inp[3],
gaes=sampled_inp[4])
print(episodes, score)
save_path = saver.save(sess, './model/model.ckpt')
if episodes == 0:
f = open('test2.csv', 'w', encoding='utf-8', newline='')
else:
f = open('test2.csv', 'a', encoding='utf-8', newline='')
wr = csv.writer(f)
wr.writerow([episodes, score])
f.close()
break
state = next_state
