def eval_nn(genotype, nbstep=2000, dump=False, render=False, name=""):
nn = SimpleNeuralControllerNumpy(5, 2, 2, 10)
nn.set_parameters(genotype)
observation = env.reset()
old_pos = None
total_dist = 0
fit = 0
if (dump):
f = open("traj" + name + ".log", "w")
for t in range(nbstep):
if render:
env.render()
action = nn.predict(observation)
observation, reward, done, info = env.step(action)
pos = info["robot_pos"][:2]
if (dump):
f.write(" ".join(map(str, pos)) + "\n")
if (old_pos is not None):
d = math.sqrt((pos[0] - old_pos[0])**2 + (pos[1] - old_pos[1])**2)
total_dist += d
old_pos = list(pos)
if (done):
break
if (dump):
f.close()
dist_obj = info["dist_obj"]
#print("End of eval, total_dist=%f"%(total_dist))
if done:
fit = 1
return (math.sqrt((pos[0] - env.goalPos[0])**2 +
(pos[1] - env.goalPos[1])**2), pos)
def eval_nn(genotype, nbstep=2000, dump=False, render=False, name=""):
nn=SimpleNeuralControllerNumpy(5,2,2,10)
nn.set_parameters(genotype)
observation = env.reset()
old_pos=None
total_dist=0
if (dump):
f=open("traj"+name+".log","w")
for t in range(nbstep):
if render:
env.render()
action=nn.predict(observation)
observation, reward, done, info = env.step(action)
pos=info["robot_pos"][:2]
if(dump):
f.write(" ".join(map(str,pos))+"\n")
if (old_pos is not None):
d=math.sqrt((pos[0]-old_pos[0])**2+(pos[1]-old_pos[1])**2)
total_dist+=d
old_pos=list(pos)
if(done):
break
if (dump):
f.close()
dist_obj=info["dist_obj"]
#print("End of eval, total_dist=%f"%(total_dist))
return ## A completer: une evaluation devra renvoyer la fitness utilisée, mais aussi le descripteur comportemental résultant de cette évaluation
def simulation(env, genotype, display=False):
global but_atteint
global size_nn
nn = SimpleNeuralControllerNumpy(5, 2, 2, 10)
if genotype != None:
nn.set_parameters(genotype)
observation = env.reset()
if (display):
env.enable_display()
then = time.time()
but = 0
for i in range(800):
env.render()
action = nn.predict(observation)
action = [i * env.maxVel for i in action]
observation, reward, done, info = env.step(action)
#print("Step %d Obs=%s reward=%f dist. to objective=%f robot position=%s End of ep=%s" % (i, str(observation), reward, info["dist_obj"], str(info["robot_pos"]), str(done)))
if (display):
time.sleep(0.01)
if done:
but_atteint = True
but += 1
break
now = time.time()
#print("%d timesteps took %f seconds" % (i, now - then))
xg, yg = env.goalPos
x, y, theta = env.get_robot_pos(
) # x,y,theta ?? pourquoi theta??? to do
return but, math.sqrt((x - xg)**2 + (y - yg)**2), [x, y]
def eval_nn(genotype, render=False, nbstep=500):
total_reward = 0
nn = SimpleNeuralControllerNumpy(4, 1, 2, 5)
nn.set_parameters(genotype)
## à completer
# utilisez render pour activer ou inhiber l'affichage (il est pratique de l'inhiber pendant les calculs et de ne l'activer que pour visualiser les résultats.
# nbstep est le nombre de pas de temps. Plus il est grand, plus votre pendule sera stable, mais par contre, plus vos calculs seront longs. Vous pouvez donc ajuster cette
# valeur pour accélérer ou ralentir vos calculs. Utilisez la valeur par défaut pour indiquer ce qui doit se passer pendant l'apprentissage, vous pourrez indiquer une
# valeur plus importante pour visualiser le comportement du résultat obtenu.
observation = env.reset()
for i in range(nbstep):
if render:
env.render()
action = nn.predict(observation)
if action > 0:
action = 1
else:
action = 0
observation, reward, done, info = env.step(action)
total_reward += reward
if done:
# print("Episode finished after %d timesteps"%(i+1))
break
return total_reward,
def eval_nn(genotype, render=False, verbose=False):
sum_reward = 0
sum_distance = 0
sum_angle = 0
_t = 0
nn=SimpleNeuralControllerNumpy(4,1,2,5)
nn.set_parameters(genotype)
observation = _env.reset()
for t in range(1000):
_t = t
sum_distance += abs(observation[0])
sum_angle += abs(observation[2])
if render:
_env.render()
action=nn.predict(observation)
if action>0:
action=1
else:
action=0
observation, reward, done, info = _env.step(action)
sum_reward +=reward
if done:
if verbose:
print("Episode finished after %d timesteps"%(t+1))
break
if (_t < 500):
sum_distance += abs(observation[0] * (500 - _t))
sum_angle += abs(observation[2] * (500 - _t))
return sum_reward, sum_distance, sum_angle
def evolution_nn(genotype, render=False, verbose=False):
hist_distance = []
hist_angle = []
nn=SimpleNeuralControllerNumpy(4,1,2,5)
nn.set_parameters(genotype)
observation = _env.reset()
for t in range(1000):
hist_distance.append(abs(observation[0]))
hist_angle.append(abs(observation[2]))
if render:
_env.render()
action=nn.predict(observation)
if action>0:
action=1
else:
action=0
observation, reward, done, info = _env.step(action)
if done:
if verbose:
print("Episode finished after %d timesteps"%(t+1))
break
return hist_distance, hist_angle
def eval_nn(env, genotype, render=False):
energie = 500
nn = SimpleNeuralControllerNumpy(4, 1, 2, 10)
nn.set_parameters(genotype)
observation = env.reset()
x = 0
y = 0
for t in range(energie):
if render:
env.render()
time.sleep(0.05)
action = nn.predict(observation)
if action > 0:
action = 1
else:
action = 0
observation, reward, done, info = env.step(action)
x += abs(observation[0])
y += abs(observation[2])
if done:
break
x = x / t
x += (energie - t) * 2.4 #penalisation pour les tours restant
y = y / t
y += (energie - t) * 41.8 #penalisation pour les tours restant
return x, y
def eval_nn(genotype, render=False):
total_reward=0
nn=SimpleNeuralControllerNumpy(4,1,2,5)
nn.set_parameters(genotype)
observation = env.reset()
for t in range(1000):
if render:
env.render()
action=nn.predict(observation)
if action>0:
action=1
else:
action=0
observation, reward, done, info = env.step(action)
total_reward+=reward
if done:
print("Episode finished after %d timesteps"%(t+1))
break
### A completer pour optimiser les parametres du reseau de neurones avec CMA-ES ###
nn = SimpleNeuralControllerNumpy(4, 1, 2, 5)
nn.init_random_params()
res = launch_cmaes_full_genotype(nn.get_parameters(),
sigma,
nbeval=1000,
display=True,
ma_func=eval_nn)
nn.set_parameters(res)
env.reset()
r = env.step(env.action_space.sample()) # take a random action
observations = r[0]
reward = r[1]
done = r[2]
print(nn.predict(observations))
for _ in range(1000):
env.render()
action = nn.predict(observations)
if action > 0:
action = 1
else:
action = 0
r = env.step(action) # take a random action
observations = r[0]
reward = r[1]
done = r[2] # take a random action
env.close()