def train(num_episodes=20000, ):
"""Train."""
# Create the task environment.
env = gym.make(name)
# Create the DDPG agent in the task environment.
agent = DDPG(env)
with open(name + '.csv', 'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(labels)
i_step = 0
for i_episode in range(1, num_episodes + 1):
# start a new episode
state = agent.reset()
sum_reward = 0.0
N = 0
while True:
# env.render()
# Actor commands the action
action = agent.act(state)
# Environment reacts with next state, reward and done for
# end-of-episode
next_state, reward, done, info = env.step(action)
# Agent (actor-critic) learns
losses = agent.step(action, reward, next_state, done)
# S <- S
state = next_state
sum_reward += reward
N += 1
i_step += 1
# if i_step % 1000 == 0 and losses is not None:
if done and losses is not None:
loss_critic = losses
# End of episode. Show metrics.
to_write = (i_episode, i_step, loss_critic, sum_reward / N)
print('\rEpisode: {:4d}, '
'Step: {:7d}, '
'Loss-crit: {:10.4f}, '
'Av Rwd: {:10.4f}, '
''.format(*to_write))
# Re-use same line to print on.
# sys.stdout.flush()
# Write CSV row
for i, label in enumerate(labels):
results[label].append(to_write[i])
writer.writerow(to_write)
if done:
break
# Plot
i_episode, loss_actor, loss_critic = zip(*telemetry)
def main(argv):
env_name = FLAGS.env_name
env = gym.make(env_name)
agent = DDPG(env, load_path=FLAGS.load_path, training=False)
for episodes in range(FLAGS.num_episodes):
done = False
obs = env.reset()
episode_reward = 0
while not done:
env.render()
action = agent.act(obs, noise=False).flatten()
obs, rew, done, info = env.step(action)
obs = obs.flatten()
episode_reward += rew
print(f'Episode Reward:{episode_reward}')
env.close()
writer = SummaryWriter(logdir=LOG_DIR)
total_numsteps = 0
n_updates = 0 # number of policy updates
for i_episode in itertools.count(1):
episode_reward = 0
episode_steps = 0
done = False
state = env.reset()
while not done:
if total_numsteps < args.start_steps:
action = env.action_space.sample() # Sample random action
else:
action = agent.act(state) # Sample action from policy
next_state, reward, done, _ = env.step(action) # Step
episode_steps += 1
total_numsteps += 1
episode_reward += reward
# Ignore the "done" signal if it comes from hitting the time horizon.
# (https://github.com/openai/spinningup/blob/master/spinup/algos/sac/sac.py)
mask = 1 if episode_steps == env._max_episode_steps else float(
not done)
agent.step(state, action, reward, next_state, mask)
if total_numsteps >= args.start_steps and total_numsteps % args.update_freq == 0:
critic_loss, actor_loss = agent.update()
state = next_state
action_shape,
batch_size=128,
gamma=0.995,
tau=0.001,
actor_lr=0.0001,
critic_lr=0.001,
use_layer_norm=True)
print('DDPG agent configured')
agent.load_model(agent.current_path + '/model/model.ckpt')
agent.load_memory()
max_episode = 10000
tot_rewards = []
print('env reset')
observation, done = env.reset()
action = agent.act(observation)
print(action)
rospy.sleep(0.8)
observation, reward, done = env.step(action)
rospy.sleep(0.8)
noise_sigma = 0.15
save_cutoff = 1
cutoff_count = 0
save_count = 0
curr_highest_eps_reward = -1000.0
for i in xrange(max_episode):
if i % 100 == 0 and noise_sigma > 0.03:
agent.noise = OUNoise(agent.nb_actions, sigma=noise_sigma)
noise_sigma /= 2.0
step_num = 0
while done == False:
writer1 = csv.writer(fout1)
writer1.writerow(labels)
fout2 = open("physical_info.csv", 'w')
labels = [
'time', 'x', 'y', 'z', 'phi', 'theta', 'psi', 'x_velocity', 'y_velocity',
'z_velocity', 'phi_velocity', 'theta_velocity', 'psi_velocity',
'rotor_speed1', 'rotor_speed2', 'rotor_speed3', 'rotor_speed4'
]
writer2 = csv.writer(fout2)
writer2.writerow(labels)
for i_episode in range(1, num_episodes + 1):
state = agent.reset() # start a new episode
while True:
action = agent.act(state)
next_state, reward, done = task.step(action)
agent.step(action, reward, next_state, done)
state = next_state
# Write info to file
to_write = [task.sim.time] + list(
task.sim.pose[:3]
) #+ list(task.sim.v) + list(task.sim.angular_v) + list(action)
fout2.write(
"{:4.2f}, {:7.3f}, {:7.3f}, {:7.3f} {:7.3f} {:7.3f} {:7.3f} {:7.3f}\n"
.format(to_write[0], to_write[1], to_write[2], to_write[3],
action[0], action[1], action[2], action[3]))
if done:
print("\rEpisode = {:4d}, score = {:7.3f} (best = {:7.3f})".format(
