def train():
num_process = 4
sub = SubprocVecEnv(num_process, False)
state_space = 2
action_space = 4
Policy = Policy_net('policy', state_space, action_space)
Old_Policy = Policy_net('old_policy', state_space, action_space)
PPO = PPOTrain(Policy, Old_Policy, gamma=0.95)
with tf.Session() as sess:
saver = tf.train.Saver()
tf.set_random_seed(1234)
#sess.run(tf.global_variables_initializer())
saver.restore(sess, './synch_ubuntu/model')
i = 0
#writer = tf.summary.FileWriter('./board/dqn_per', sess.graph)
#for i in range(10):
while True:
i += 1
info = sub.reset()
terminal, each_terminal = False, [False] * num_process
global_step = 0
memory = []
obs_s, state, action, reward, done = trans_data(info, num_process)
while not terminal:
time.sleep(0.05)
global_step += 1
action, v_pred = get_action(Policy, each_terminal, num_process,
state)
info = sub.step(action, obs_s, [global_step] * num_process)
obs_s, next_state, a, reward, done = trans_data(
info, num_process)
each_terminal, terminal = check_done(info, num_process)
memory.append([state, action, reward, v_pred])
if terminal:
state_, action_, reward_, v_preds_next_, gaes_ = memory_stack(
memory, num_process, state_space, PPO)
PPO.assign_policy_parameters()
inp = [state_, action_, reward_, v_preds_next_, gaes_]
for epoch in range(3):
sample_indices = np.random.randint(
low=0, high=state_.shape[0], size=64)
sampled_inp = [
np.take(a=t, indices=sample_indices, axis=0)
for t in inp
]
PPO.train(obs=sampled_inp[0],
actions=sampled_inp[1],
rewards=sampled_inp[2],
v_preds_next=sampled_inp[3],
gaes=sampled_inp[4])
#summary = sess.run(merged, feed_dict={r:sum(reward_)/(num_process)})
#writer.add_summary(summary, i)
#saver.save(sess, './synch_ubuntu/model')
if i < 5100:
print(sum(reward_) / (num_process), i)
state = next_state
sub.close()
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
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)
PPO.assign_policy_parameters()
inp = [
observations, actions_list, spatial, rewards, v_preds_next,
gaes
]
for epoch in range(10):
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],
spatial=sampled_inp[2],
actions=sampled_inp[1],
rewards=sampled_inp[3],
v_preds_next=sampled_inp[4],
gaes=sampled_inp[5])
saver.save(sess, "collectMineralShards/tmp/model.ckpt")
print(episodes, sum(rewards))
open_file_and_save('collectMineralShards/reward.csv',
[sum(rewards)])
state = next_state
env.close()
def main():
angle = 0.0
angle_thres_deg = 15
cart = 0.0
t.tic()
reward_max = 5
reward_min = -5
reward_disc = 5
pwm_index = 1
pwm_list = [("L", 180), ("L", 170), ("L", 160), ("L", 0), ("R", 160),
("R", 170), ("R", 180)]
pwm_list = [("L", 180), ("L", 0), ("R", 180)]
pwm_list_size = 3
# Serial port for Arduino
if (SERIAL_AVAILABLE):
ser = serial.Serial('COM20', 115200) # Initialize serial port
print("connected to: " + ser.portstr) # Confirm connection
env = gym.make('CartPole-v0')
env.seed(0)
ob_space = env.observation_space
Policy = Policy_net('policy', env)
Old_Policy = Policy_net('old_policy', env)
PPO = PPOTrain(Policy, Old_Policy, gamma=GAMMA)
saver = tf.train.Saver()
with tf.Session() as sess:
if LOAD:
saver.restore(
sess, "./model/model_iter_{:d}_rewards_{:d}.ckpt".format(
load_iteration, load_rewards))
else:
sess.run(
tf.global_variables_initializer()) # remove me if loading save
writer = tf.summary.FileWriter('./log/train', sess.graph)
obs = env.reset()
reward = 0
success_num = 0
for iteration in range(ITERATION): # episode
observations = []
actions = []
v_preds = []
rewards = []
run_policy_steps = 0
while True: # run policy RUN_POLICY_STEPS which is much less than episode length
run_policy_steps += 1
obs = np.stack([obs]).astype(
dtype=np.float32) # prepare to feed placeholder Policy.obs
act, v_pred = Policy.act(obs=obs, stochastic=True)
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
observations.append(obs)
actions.append(act)
v_preds.append(v_pred)
rewards.append(reward)
# env.render()
if (act == 1):
if pwm_index < pwm_list_size - 1:
pwm_index += 1
else:
if pwm_index > 0:
pwm_index -= 1
dir = pwm_list[pwm_index][0]
pwm = pwm_list[pwm_index][1]
print(dir)
print(pwm)
if (SERIAL_AVAILABLE):
PD.writePWM(ser, 180, dir)
last_angle = angle
angle_deg = PD.getPEncoderPos(
ser
) * 360 / 1200 # convert encoder counts (1200) to degrees
angle = angle_deg * 2 * math.pi / 360 # convert degrees to radians
angle_velocity = (angle - last_angle) / t.tocvalue()
last_cart = cart
cart = PD.getMEncoderPos(ser)
cart_velocity = (cart - last_cart) / t.tocvalue()
#print("Angle {:.1f}, Angle_vel (rad/s) {:.1f}, Position (mm) {:.1f}, Velocity (mm/s) {:.1f}".format(angle, angle_velocity, cart,cart_velocity))
t.tic()
m = (reward_max - reward_min) / (reward_disc - angle_thres_deg)
# reward = min(m*(abs(angle_deg)-reward_disc) + reward_max, reward_max)
#reward = 1
reward = ((.9 / 7) * (min(
(6 - abs(angle_deg)), (1))) + 6) + ((0.1 / 6) * (min(
(5 - abs((cart / 1000))), (1)) + 5))
# next_obs = [angle angle_velocity cart cart_velocoty]
# print(next_obs)
next_obs = [angle, angle_velocity, cart, cart_velocity]
#print("angle = ", angle_deg)
print("x: ", PD.getMEncoderPos(ser))
if abs(angle_deg) > angle_thres_deg:
v_preds_next = v_preds[1:] + [
0
] # next state of terminate state has 0 state value
print("reward: ", sum(rewards))
obs = env.reset()
reward = -1
print("Iteration: ", iteration)
print('Waiting to reset')
PD.writePWM(ser, 0, dir)
if iteration % 10 == 0:
saver.save(
sess,
"./model/model_iter_{:d}_rewards_{:d}.ckpt".format(
iteration, sum(rewards)))
print('Scoot scoot!! Model saved.')
while (angle_deg > 1.5 or angle_deg < -1.5):
time.sleep(0.1)
angle_deg = PD.getPEncoderPos(ser) * 360 / 1200
print('Entered iteration {:1f}'.format(iteration + 1))
break
else:
obs = next_obs
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_length',
simple_value=run_policy_steps)
]), iteration)
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_reward',
simple_value=sum(rewards))
]), iteration)
if sum(rewards) >= 195:
success_num += 1
if success_num >= 100:
saver.save(sess, './model/model.ckpt')
print('Clear!! Model saved.')
break
else:
success_num = 0
gaes = PPO.get_gaes(rewards=rewards,
v_preds=v_preds,
v_preds_next=v_preds_next)
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations,
newshape=[-1] + list(ob_space.shape))
actions = np.array(actions).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)
if iteration > 0:
gaes = (gaes - gaes.mean()) / gaes.std()
PPO.assign_policy_parameters()
inp = [observations, actions, rewards, v_preds_next, gaes]
# train
for epoch in range(4):
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])
summary = PPO.get_summary(obs=inp[0],
actions=inp[1],
rewards=inp[2],
v_preds_next=inp[3],
gaes=inp[4])[0]
writer.add_summary(summary, iteration)
writer.close()
if (SERIAL_AVAILABLE):
ser.close()
def main():
# env = gym.make('CartPole-v0')
num_arm = 5
env = BernoulliBanditEnv(num_arm)
env.seed(0)
ob_space = env.observation_space
# Policy = Policy_net('policy', env)
# Old_Policy = Policy_net('old_policy', env)
Policy = PolicyGRUNet('policy', env)
Old_Policy = PolicyGRUNet('old_policy', env)
PPO = PPOTrain(Policy, Old_Policy, gamma=GAMMA)
saver = tf.train.Saver()
with tf.Session() as sess:
writer = tf.summary.FileWriter('./log/train', sess.graph)
sess.run(tf.global_variables_initializer())
obs = env.reset()
reward = 0
success_num = 0
for iteration in range(ITERATION): # episode
observations = []
actions = []
v_preds = []
rewards = []
run_policy_steps = 0
while True: # run policy RUN_POLICY_STEPS which is much less than episode length
run_policy_steps += 1
obs = np.stack([obs]).astype(
dtype=np.float32) # prepare to feed placeholder Policy.obs
# obs = np.expand_dims(obs, axis=0)
act, v_pred = Policy.act(obs=obs, stochastic=True)
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
observations.append(obs)
actions.append(act)
v_preds.append(v_pred)
rewards.append(reward)
if act not in range(num_arm):
act = np.random.randint(num_arm)
next_obs, reward, done, info = env.step(act)
if done:
v_preds_next = v_preds[1:] + [
0
] # next state of terminate state has 0 state value
obs = env.reset()
reward = -1
break
else:
obs = next_obs
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_length',
simple_value=run_policy_steps)
]), iteration)
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_reward',
simple_value=sum(rewards))
]), iteration)
if sum(rewards) >= 195:
success_num += 1
if success_num >= 100:
saver.save(sess, './model/model.ckpt')
print('Clear!! Model saved.')
break
else:
success_num = 0
gaes = PPO.get_gaes(rewards=rewards,
v_preds=v_preds,
v_preds_next=v_preds_next)
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations,
newshape=[-1] + list(ob_space.shape))
# observations = np.expand_dims(observations, axis=0)
actions = np.array(actions).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()) / gaes.std()
PPO.assign_policy_parameters()
inp = [observations, actions, rewards, v_preds_next, gaes]
# train
for epoch in range(4):
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])
summary = PPO.get_summary(obs=inp[0],
actions=inp[1],
rewards=inp[2],
v_preds_next=inp[3],
gaes=inp[4])[0]
writer.add_summary(summary, iteration)
writer.close()
def main():
allrewards = list()
env = gym.make('CartPole-v0')
env.seed(0)
ob_space = env.observation_space
Policy = Policy_net('policy', env)
Old_Policy = Policy_net('old_policy', env)
PPO = PPOTrain(Policy, Old_Policy, gamma=GAMMA)
saver = tf.train.Saver()
name = 'Model_Noise'
filename = "data/{n}_{ts:%H_%M_%S}.csv".format(n=name, ts=datetime.now())
with open(filename, "w", 1) as result:
result.write("Iteration, Reward \n")
with tf.Session() as sess:
writer = tf.summary.FileWriter('./log/train', sess.graph)
sess.run(tf.global_variables_initializer())
obs = env.reset()
reward = 0
success_num = 0
for iteration in range(ITERATION): # episode
observations = []
actions = []
v_preds = []
rewards = []
run_policy_steps = 0
while True: # run policy RUN_POLICY_STEPS which is much less than episode length
if iteration % 500 == 0:
env.render()
run_policy_steps += 1
obs = np.stack([obs]).astype(
dtype=np.float32
) # prepare to feed placeholder Policy.obs
act, v_pred = Policy.act(obs=obs, stochastic=True)
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
observations.append(obs)
actions.append(act)
v_preds.append(v_pred)
rewards.append(reward)
next_obs, reward, done, info = env.step(act)
if done:
v_preds_next = v_preds[1:] + [
0
] # next state of terminate state has 0 state value
obs = env.reset()
reward = -1
break
else:
obs = next_obs
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_length',
simple_value=run_policy_steps)
]), iteration)
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_reward',
simple_value=sum(rewards))
]), iteration)
result.write("{:d},{:2f}\n".format(iteration, sum(rewards)))
print("Rewards: {:2f}, Iterations: {:d}".format(
sum(rewards), iteration))
if sum(rewards) >= 195:
success_num += 1
if success_num >= 100:
saver.save(sess, './model/model.ckpt')
print('Clear!! Model saved.')
break
else:
success_num = 0
gaes = PPO.get_gaes(rewards=rewards,
v_preds=v_preds,
v_preds_next=v_preds_next)
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations,
newshape=[-1] + list(ob_space.shape))
actions = np.array(actions).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()) / gaes.std()
PPO.assign_policy_parameters()
inp = [observations, actions, rewards, v_preds_next, gaes]
# train
for epoch in range(4):
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])
summary = PPO.get_summary(obs=inp[0],
actions=inp[1],
rewards=inp[2],
v_preds_next=inp[3],
gaes=inp[4])[0]
writer.add_summary(summary, iteration)
writer.close()
if iteration % 500 == 0:
env.close()
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 run_gail(agent, index_gail, env):
DG_flag = 1
#env.seed(0)
ob_space = env.observation_space
Policy = Policy_net('policy_' + str(index_gail), env)
Old_Policy = Policy_net('old_policy' + str(index_gail), env)
gamma = 0.95
PPO = PPOTrain(Policy, Old_Policy, gamma)
D = Discriminator(env, index_gail)
if DG_flag:
# with open(Config.DEMO_DATA_PATH, 'rb') as f:
# demo_transitions = pickle.load(f)
# demo_transitions = deque(itertools.islice(demo_transitions, 0, Config.demo_buffer_size))
# assert len(demo_transitions) == Config.demo_buffer_size
expert_data = agent.replay_memory if agent.replay_memory.full(
) else agent.demo_memory
_, demo_transitions, _ = expert_data.sample(agent.config.BATCH_SIZE)
expert_observations = [data[0] for data in demo_transitions]
expert_actions = [data[1] for data in demo_transitions]
else:
expert_observations = np.genfromtxt('trajectory/observations.csv')
expert_actions = np.genfromtxt('trajectory/actions.csv',
dtype=np.int32)
with tf.Session() as sess:
# writer = tf.summary.FileWriter(args.logdir, sess.graph)
#load_path=saver.restore(sess,"trained_models/model.ckpt")
#sess.run(tf.global_variables_initializer())
#if index_gail>1:
# saver.restore(sess, 'trained_models/model' + str(index_gail-1) + '.ckpt')
obs = env.reset()
state_for_memory = obs #为了处理两套程序中使用的数据格式不同
success_num = 0
iteration = int(2000) #0319
for iteration in range(iteration):
#print("running policy ")
observations = []
#states_for_memory=[]
actions = []
# do NOT use rewards to update policy , # 0319 why ?
rewards = []
v_preds = []
run_policy_steps = 0
score = 0
if DG_flag:
t_q = deque(maxlen=Config.trajectory_n)
done, score, n_step_reward, state_for_memory = False, 0, None, env.reset(
)
while True:
run_policy_steps += 1
obs = np.stack([obs]).astype(
dtype=np.float32) # prepare to feed placeholder Policy.obs
act, v_pred = Policy.act(obs=obs, stochastic=True)
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
next_obs, reward, done, info = env.step(act)
next_state_for_memory = next_obs
score += reward
if DG_flag:
reward_to_sub = 0. if len(t_q) < t_q.maxlen else t_q[0][
2] # record the earliest reward for the sub
t_q.append([
state_for_memory, act, reward, next_state_for_memory,
done, 0.0
])
if len(t_q) == t_q.maxlen:
if n_step_reward is None: # only compute once when t_q first filled
n_step_reward = sum([
t[2] * Config.GAMMA**i
for i, t in enumerate(t_q)
])
else:
n_step_reward = (n_step_reward -
reward_to_sub) / Config.GAMMA
n_step_reward += reward * Config.GAMMA**(
Config.trajectory_n - 1)
t_q[0].extend([
n_step_reward, next_state_for_memory, done,
t_q.maxlen
]) # actual_n is max_len here
#agent.perceive(t_q[0]) # perceive when a transition is completed
env.render() # 0313
observations.append(obs)
actions.append(act)
rewards.append(reward)
v_preds.append(v_pred)
if done:
if DG_flag:
t_q.popleft(
) # first transition's n-step is already set
transitions = set_n_step(t_q, Config.trajectory_n)
next_obs = np.stack([next_obs]).astype(
dtype=np.float32
) # prepare to feed placeholder Policy.obs
_, v_pred = Policy.act(obs=next_obs, stochastic=True)
v_preds_next = v_preds[1:] + [np.asscalar(v_pred)]
obs = env.reset()
print("iteration", iteration, "score", score)
break
else:
obs = next_obs
state_for_memory = next_state_for_memory
#print("state_for memory",state_for_memory)
#writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='episode_length', simple_value=run_policy_steps)]), iteration)
#writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='episode_reward', simple_value=sum(rewards))]), iteration)
#
if sum(rewards) >= 100:
success_num += 1
# todo
# 在 能够得到较好的回报 的时候 存储这次的demo
if DG_flag:
for t in transitions:
agent.perceive(t)
agent.replay_memory.memory_len()
if success_num >= 3:
#saver.save(sess, 'trained_models/model.ckpt')
#saver.save(sess, 'trained_models/model' + str(index_gail) + '.ckpt')
print(success_num)
print('Clear!! Model saved.')
env.close()
break
else:
success_num = 0
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations,
newshape=[-1] + list(ob_space.shape))
actions = np.array(actions).astype(dtype=np.int32)
# train discriminator
for i in range(2):
#print("training D")
D.train(expert_s=expert_observations,
expert_a=expert_actions,
agent_s=observations,
agent_a=actions)
# output of this discriminator is reward
d_rewards = D.get_rewards(agent_s=observations, agent_a=actions)
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)
# gaes = (gaes - gaes.mean()) / gaes.std()
v_preds_next = np.array(v_preds_next).astype(dtype=np.float32)
# train policy
inp = [observations, actions, gaes, d_rewards, v_preds_next]
PPO.assign_policy_parameters()
for epoch in range(6):
#print("updating PPO ")
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])
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
def __init__(self):
rospy.init_node('runPPO', anonymous=True)
Policy = Policy_net('policy', self.n_inputs, self.n_outputs)
Old_Policy = Policy_net('old_policy', self.n_inputs, self.n_outputs)
PPO = PPOTrain(Policy, Old_Policy, gamma=GAMMA, c_2=0.1)
saver = tf.train.Saver()
rospy.Subscriber('/RL/gripper_status', String,
self.callbackGripperStatus)
# rospy.Service('/RL/net', net_eval, self.EvalNet)
rospy.Service('/RL/start_learning', Empty, self.start_learning)
obs_srv = rospy.ServiceProxy('/RL/observation', observation)
drop_srv = rospy.ServiceProxy('/RL/IsObjDropped', IsDropped)
move_srv = rospy.ServiceProxy('/RL/MoveGripper', TargetAngles)
reset_srv = rospy.ServiceProxy('/RL/ResetGripper', Empty)
pub_goal = rospy.Publisher('/RL/Goal',
Float32MultiArray,
queue_size=10)
gg = Float32MultiArray()
gg.data = self.g
with tf.Session() as sess:
# $ tensorboard --logdir=logs
# http://0.0.0.0:6006/
writer = tf.summary.FileWriter(
'/home/pracsys/catkin_ws/src/rutgers_collab/src/rl_pkg/src/PPO/log/train',
sess.graph)
sess.run(tf.global_variables_initializer())
reward = 0
success_num = 0
episode_count = 0
rate = rospy.Rate(100) # 100hz
while not rospy.is_shutdown():
if self.stLearning:
## Start episode ##
episode_count += 1
# Reset gripper
reset_srv()
while not self.gripper_closed:
rate.sleep()
# Get observation
obs = np.array(obs_srv().state)
self.prev_dis2goal = np.linalg.norm(self.g - obs[:2])
observations = []
actions = []
v_preds = []
rewards = []
run_policy_steps = 0
while True: # run policy RUN_POLICY_STEPS which is much less than episode length
run_policy_steps += 1
print(
'[RL] Step %d in episode %d, distance to goal: %f.'
% (run_policy_steps, episode_count,
self.prev_dis2goal))
pub_goal.publish(gg)
obs = np.stack([obs]).astype(
dtype=np.float32
) # prepare to feed placeholder Policy.obs
while 1:
act, v_pred = Policy.act(obs=obs, stochastic=True)
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
if act < 8:
break
# Act
suc = move_srv(self.A[act])
rospy.sleep(0.05)
rate.sleep()
if suc:
# Get observation
next_obs = np.array(obs_srv().state)
fail = drop_srv(
).dropped # Check if dropped - end of episode
else:
# End episode if overload or angle limits reached
rospy.logerr(
'[RL] Failed to move gripper. Episode declared failed.'
)
fail = True
reward, done = self.transition_reward(next_obs, fail)
observations.append(obs)
actions.append(act)
v_preds.append(v_pred)
rewards.append(
reward
) # Weird that this is before the step - this is the reward of the previos action
print(
'[RL] Action %d yielded reward %f and position (%f,%f).'
% (act, reward, obs[0][0], obs[0][1]))
if run_policy_steps > self.max_steps:
done = True
if done:
v_preds_next = v_preds[1:] + [
0
] # next state of terminate state has 0 state value - adds zero in the end of the vector
reward = -1
break
else:
obs = next_obs
rate.sleep()
print('episode_length', run_policy_steps, 'episode_reward',
sum(rewards))
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_length',
simple_value=run_policy_steps)
]), episode_count)
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_reward',
simple_value=sum(rewards))
]), episode_count)
if sum(rewards) >= self.stop_bound:
success_num += 1
if success_num >= 100:
saver.save(
sess,
'/home/pracsys/catkin_ws/src/rutgers_collab/src/rl_pkg/logs/model_ppo.ckpt'
)
print('Clear!! Model saved.')
break
else:
success_num = 0
gaes = PPO.get_gaes(rewards=rewards,
v_preds=v_preds,
v_preds_next=v_preds_next)
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations,
newshape=[-1] + list(
(self.n_inputs, )))
actions = np.array(actions).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()) / gaes.std()
PPO.assign_policy_parameters()
inp = [observations, actions, rewards, v_preds_next, gaes]
# train
for epoch in range(4):
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])
summary = PPO.get_summary(obs=inp[0],
actions=inp[1],
rewards=inp[2],
v_preds_next=inp[3],
gaes=inp[4])[0]
writer.add_summary(summary, episode_count)
if episode_count > self.max_episodes:
break
rate.sleep()
writer.close()
def main():
env = gym.make(ENV) # Instancia o ambiente CartPole
env.seed(0) #
ob_space = env.observation_space # Descrevem o formato de observações válidas do espaço
Policy = Policy_net('policy', env) # Cria a rede de Politica
Old_Policy = Policy_net('old_policy',
env) # Cria a rede de politica antiga
PPO = PPOTrain(Policy, Old_Policy, gamma=GAMMA)
saver = tf.train.Saver() #
with tf.Session() as sess: # Bloco da sessão
writer = tf.summary.FileWriter('./log/train',
sess.graph) # Define diretório de logs
sess.run(tf.global_variables_initializer()) # Inicializa as redes
obs = env.reset() # Reseta o ambiente e obtêm a primeira observação
reward = 0 # Armazena as recompensas
success_num = 0 # Contador de sucessos
for episode in range(EPISODES): # Loop do episodio
observations = [] # Array pra armazenar as observações
actions = [] # Array pra armazenar as ações
v_preds = [] # Array pra armazenar as previsões
rewards = [] # Array pra armazenar as recompensas
run_policy_steps = 0 # Contador de passos em cada episodio
env.render() # Renderiza o ambiente
while True: # Run policy RUN_POLICY_STEPS which is much less than episode length
# Execute a política RUN_POLICY_STEPS, que é muito menor que a duração do episódio
run_policy_steps += 1 # Incrementa contador de passos de cada episodio
obs = np.stack([obs]).astype(
dtype=np.float32) # prepare to feed placeholder Policy.obs
act, v_pred = Policy.act(
obs=obs, stochastic=True
) # Corre a rede neural e obtêm uma ação e o V previsto
act = act.item() # Transforma um array do numpy
v_pred = v_pred.item() # em um objeto scalar do Python
observations.append(
obs) # Adiciona a observação ao buffer de observações
actions.append(act) # Adiciona a ação ao buffer de ações
v_preds.append(v_pred) # Adiciona a v_pred ao buffer de v_pred
rewards.append(
reward) # Adiciona a recompensa ao buffer de recompensa
next_obs, reward, done, info = env.step(
act
) # envia a ação ao ambiente e recebe a próxima observação, a recompensa e se o passo terminou
if done: # Se o done for verdadeiro ...
v_preds_next = v_preds[1:] + [
0
] # [1:] seleciona do segundo elemento da lista em diante e + [0] adiciona um elemento de valor zero no final da lista
# next state of terminate state has 0 state value
# próximo estado do estado final tem 0 valor de estado
obs = env.reset() # Redefine o ambiente
reward = -1 # define a recompensa como -1 (?)
break # Sai do loop while
else: # Senão...
obs = next_obs # Armazena em obs a próxima observação
# Armazena em log para visualização no tensorboard
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_length',
simple_value=run_policy_steps)
]), episode)
writer.add_summary(
tf.Summary(value=[
tf.Summary.Value(tag='episode_reward',
simple_value=sum(rewards))
]), episode)
# Condicional para finalizar o teste
if sum(
rewards
) >= 195: # Se a soma das recompensas for maior ou igual 195
success_num += 1 # Incrementa o contador de sucessos
if success_num >= 100: # Se ocorrerem 100 sucessos
saver.save(sess,
'./model/model.ckpt') # Salva a sessão
print('Clear!! Model saved.') # Escreve na tela
break # Sai do loop
else: # senão,
success_num = 0 # zera o contador de sucessos
print("EP: ", episode, " Rw: ", sum(rewards)
) # Escreve na tela o numero do episodio e a recompensa
gaes = PPO.get_gaes(rewards=rewards,
v_preds=v_preds,
v_preds_next=v_preds_next) # ?
# Converte lista em NPArray para alimentar o tf.placeholder
newshape = [-1] + list(ob_space.shape) # cria um array [-1, 4]
observations = np.reshape(
observations, newshape=newshape
) # antes, cada linha de observations era um array idependente. depois do reshape, observations passou ser um array só com varias linhas.
actions = np.array(actions).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()) / gaes.std(
) # subtrai dos itens de gaes a media de todos os itens de gaes e divide todos pelo desvio padrao de gaes
PPO.assign_policy_parameters()
inp = [
observations, actions, rewards, v_preds_next, gaes
] # Cria um array com 5 colunas: observações, ações, recompensas,
# Treina
for epoch in range(4):
sample_indices = np.random.randint(
low=0, high=observations.shape[0],
size=64) # índices estão em [baixo, alto]
sampled_inp = []
for a in inp:
sampled_inp.append(
np.take(a=a, indices=sample_indices,
axis=0)) # amostra de dados de treinamento
PPO.train(obs=sampled_inp[0],
actions=sampled_inp[1],
rewards=sampled_inp[2],
v_preds_next=sampled_inp[3],
gaes=sampled_inp[4])
summary = PPO.get_summary(obs=inp[0],
actions=inp[1],
rewards=inp[2],
v_preds_next=inp[3],
gaes=inp[4])[0]
writer.add_summary(summary, episode)
writer.close() # Final do episódio
def main(args):
env = gym.make('CartPole-v1')
env.seed(0)
ob_space = env.observation_space
Policy = Policy_net('policy', env)
Old_Policy = Policy_net('old_policy', env)
PPO = PPOTrain(Policy, Old_Policy, gamma=args.gamma)
D = Discriminator(env)
#expert_observations = np.genfromtxt('trajectory/observations.csv')
#expert_actions = np.genfromtxt('trajectory/actions.csv', dtype=np.int32)
with open(Config.DEMO_DATA_PATH, 'rb') as f:
demo_transitions = pickle.load(f)
demo_transitions = deque(itertools.islice(demo_transitions, 0, Config.DEMO_BUFFER_SIZE))
print("demo_transitions len: ", len(demo_transitions))
expert_observations = [data[0] for data in demo_transitions]
expert_actions = [data[1] for data in demo_transitions]
saver = tf.train.Saver()
with tf.Session() as sess:
# writer = tf.summary.FileWriter(args.logdir, sess.graph)
sess.run(tf.global_variables_initializer())
obs = env.reset()
success_num = 0
itera = 0
scores=[]
for iteration in range(args.iteration):
observations = []
actions = []
# do NOT use rewards to update policy
rewards = []
v_preds = []
run_policy_steps = 0
score=0
while True:
run_policy_steps += 1
obs = np.stack([obs]).astype(dtype=np.float32) # prepare to feed placeholder Policy.obs
act, v_pred = Policy.act(obs=obs, stochastic=True)
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
next_obs, reward, done, info = env.step(act)
score = reward+score
env.render()#0313
observations.append(obs)
actions.append(act)
rewards.append(reward)
v_preds.append(v_pred)
if done:
itera += 1
next_obs = np.stack([next_obs]).astype(dtype=np.float32) # prepare to feed placeholder Policy.obs
_, v_pred = Policy.act(obs=next_obs, stochastic=True)
v_preds_next = v_preds[1:] + [np.asscalar(v_pred)]
obs = env.reset()
print("itera :", itera, "score:{}", score)
scores.append(score)
break
else:
obs = next_obs
# writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='episode_length', simple_value=run_policy_steps)]), iteration)
# writer.add_summary(tf.Summary(value=[tf.Summary.Value(tag='episode_reward', simple_value=sum(rewards))]), iteration)
if sum(rewards) >= 195:
success_num += 1
if success_num >= 100:
saver.save(sess, args.savedir + '/model.ckpt')
print('Clear!! Model saved.')
break
else:
success_num = 0
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations, newshape=[-1] + list(ob_space.shape))
actions = np.array(actions).astype(dtype=np.int32)
# train discriminator
for i in range(2):
D.train(expert_s=expert_observations,
expert_a=expert_actions,
agent_s=observations,
agent_a=actions)
# output of this discriminator is reward
d_rewards = D.get_rewards(agent_s=observations, agent_a=actions)
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)
# gaes = (gaes - gaes.mean()) / gaes.std()
v_preds_next = np.array(v_preds_next).astype(dtype=np.float32)
# train policy
inp = [observations, actions, gaes, d_rewards, v_preds_next]
PPO.assign_policy_parameters()
for epoch in range(6):
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 = PPO.get_summary(obs=inp[0],
# actions=inp[1],
# gaes=inp[2],
# rewards=inp[3],
# v_preds_next=inp[4])
#
# writer.add_summary(summary, iteration)
# writer.close()
plt.plot(scores, 'r')
plt.show()
def main():
# Initialize OpenAI_ROS ENV
LoadYamlFileParamsTest(rospackage_name="learning_ros",
rel_path_from_package_to_file="config",
yaml_file_name="aliengo_stand.yaml")
env = StartOpenAI_ROS_Environment('AliengoStand-v0')
time.sleep(3)
# Initialize PPO agent
Policy = Policy_net('policy', ob_space=3, act_space=8)
Old_Policy = Policy_net('old_policy', ob_space=3, act_space=8)
PPO = PPOTrain(Policy, Old_Policy, gamma=GAMMA)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
obs = env.reset()
reward = 0
success_num = 0
scores = []
for iteration in range(ITERATION):
observations = []
actions = []
v_preds = []
rewards = []
while not rospy.is_shutdown(): # until ros is not shutdown
# prepare to feed placeholder Policy.obs
obs = np.stack([obs]).astype(dtype=np.float32)
act, v_pred = Policy.act(obs=obs, stochastic=True)
#print('act: ',act, 'v_pred: ',v_pred )
act = np.asscalar(act)
v_pred = np.asscalar(v_pred)
observations.append(obs)
actions.append(act)
v_preds.append(v_pred)
rewards.append(reward)
#execute according to action
next_obs, reward, done, _ = env.step(act)
time.sleep(0.01)
if done:
# next state of terminate state has 0 state value
v_preds_next = v_preds[1:] + [0]
obs = env.reset()
break
else:
obs = next_obs
#scores store for visualization
scores.append(sum(rewards))
#if consectuvely 10 times has high reward end the training
if sum(rewards) >= 400:
success_num += 1
print("Succes number: " + str(success_num))
if success_num >= 5:
saver.save(sess, 'model_train/model_ppo.ckpt')
print('Clear!! Model saved.')
if success_num >= 10:
saver.save(sess, 'model_train/model_ppo.ckpt')
print('Finished! ')
break
else:
success_num = 0
gaes = PPO.get_gaes(rewards=rewards,
v_preds=v_preds,
v_preds_next=v_preds_next)
# convert list to numpy array for feeding tf.placeholder
observations = np.reshape(observations, [len(observations), 3])
actions = np.array(actions).astype(dtype=np.int32)
rewards = np.array(rewards).astype(dtype=np.float32)
v_preds_next = np.array(v_preds_next).astype(dtype=np.float32)
#calculate generative advantage estimator score
gaes = np.array(gaes).astype(dtype=np.float32)
gaes = (gaes - gaes.mean())
print('gaes', gaes)
#assign current policy params to previous policy params
PPO.assign_policy_parameters()
inp = [observations, actions, rewards, v_preds_next, gaes]
# PPO train
for epoch in range(4):
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])
plt.plot(scores)
plt.show()
env.stop()