def interact(env: Env, agent: Agent,
start_obs: Arrayable) -> Tuple[array, array, array]:
"""One step interaction between env and agent.
:args env: environment
:args agent: agent
:args start_obs: initial observation
:return: (next observation, reward, terminal?)
"""
action = agent.step(start_obs)
next_obs, reward, done, information = env.step(action)
time_limit = information[
'time_limit'] if 'time_limit' in information else None
agent.observe(next_obs, reward, done, time_limit)
return next_obs, reward, done
target_pos = np.array([0., 0., 150.])
task = Task(init_pose=init_pos, target_pos=target_pos, runtime=10.)
agent = Agent(task)
# save rewards for plotting
rewards = []
rotor_speeds_var = []
for i_episode in range(1, num_episodes + 1):
state = agent.reset_episode() # start a new episode
step = 0
while True:
step += 1
action = agent.act(state)
next_state, reward, done = task.step(action)
agent.step(action, reward, next_state, done)
state = next_state
if done:
rewards.append(agent.score)
rotor_speeds_var.append(np.var(action))
print(
"\r\nEp={:4d}, score={:7.3f} (top={:7.3f}) pos={} {} {} {} {} {} {}"
.format(i_episode, agent.score, agent.top_score,
round(task.sim.pose[:3][0], 2),
round(task.sim.pose[:3][1], 2),
round(task.sim.pose[:3][2], 2),
round(task.sim.pose[3:6][0], 2),
round(task.sim.pose[3:6][1], 2),
round(task.sim.pose[3:6][2], 2),
round(abs(task.sim.v).sum()), 2),
end="") # [debug]
#Results with the conditions of the quadcopter
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'
]
results = {x: [] for x in labels}
num_episodes = 1000
for i_episode in range(1, num_episodes + 1):
state = agent.reset_episode() # start a new episode
while True:
action = agent.act(state)
next_state, reward, done = takeoff.step(action)
agent.step(reward, done)
state = next_state
to_write = [takeoff.sim.time] + list(takeoff.sim.pose) + list(
takeoff.sim.v) + list(takeoff.sim.angular_v) + list(action)
for ii in range(len(labels)):
results[labels[ii]].append(to_write[ii])
if done:
print(
"\rEpisode = {:4d}, score = {:7.3f} (best = {:7.3f}), noise_scale = {}"
.format(i_episode, agent.score, agent.best_score,
agent.noise_scale),
end="") # [debug]
break
sys.stdout.flush()
''' Shows the results of the control
plt.plot(results['time'], results['x'], label='x')
