def main():
with tf.Session() as sess:
# Task: take-off and hover
init_pose = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
init_velocities = [0.0, 0.0, 0.0]
init_angle_velocities = [0.0, 0.0, 0.0]
run_time = 5
target_pos = [0.0, 0.0, 10.0]
ddpg = agent.DDPG(
Task(init_pose, init_velocities, init_angle_velocities, run_time,
target_pos))
train(sess, ddpg)
def main():
with tf.Session() as sess:
env = gym.make('Pendulum-v0')
random_seed = 1234
np.random.seed(random_seed)
tf.set_random_seed(random_seed)
env.seed(random_seed)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
action_bound = env.action_space.high
action_low = -action_bound
action_high = action_bound
ddpg = agent.DDPG(env, state_dim, action_dim, action_low, action_high)
train(sess, env, ddpg)
def main():
# ===========================
# Task: take-off and hover
# ===========================
tf.reset_default_graph()
with tf.Graph().as_default():
tf.set_random_seed(1234)
with tf.Session() as sess:
init_pose = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
init_velocities = [0.0, 0.0, 0.0]
init_angle_velocities = [0.0, 0.0, 0.0]
run_time = 5
target_pos = [0.0, 0.0, 10.0]
ddpg = agent.DDPG(
Task(init_pose, init_velocities, init_angle_velocities,
run_time, target_pos))
reward_all = train(sess, ddpg)
def main(max_episodes):
with tf.Session() as sess:
# Task: take-off and hover
init_pose = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
init_velocities = [0.0, 0.0, 0.0]
init_angle_velocities = [0.0, 0.0, 0.0]
run_time = 20
target_pos = [0.0, 0.0, 10.0]
np.random.seed(1234)
tf.set_random_seed(2345)
ddpg = agent.DDPG(
Task(init_pose, init_velocities, init_angle_velocities, run_time,
target_pos))
reward_all = train(sess, ddpg, max_episodes)
print('done')
return reward_all
import sys
# Task: take-off and hover
init_pose = [0.0, 0.0, 100.0, 0.0, 0.0, 0.0]
init_velocities = [0.0, 0.0, 0.0]
init_angle_velocities = [0.0, 0.0, 0.0]
run_time = 10
target_pos = [0.0, 0.0, 100.0]
num_episodes = 20 #1000
best_score = -np.inf
np.random.seed(1234)
task = Task(init_pose, init_velocities, init_angle_velocities, run_time,
target_pos)
ddpg = agent.DDPG(task)
reward_all = np.array([], dtype=float)
for i_episode in range(1, num_episodes + 1):
state = ddpg.reset_episode() # start a new episode
count = 0
total_reward = 0.0
while True:
action = ddpg.act(state)
next_state, reward, done = task.step(action)
ddpg.step(action, reward, next_state, done)