control_dim=control_dim,
num_basis_functions=5)
# controller = LinearController(state_dim=state_dim, control_dim=control_dim)
pilco = PILCO(X, Y, controller=controller, horizon=40)
# Example of user provided reward function, setting a custom target state
# R = ExponentialReward(state_dim=state_dim, t=np.array([0.1,0,0,0]))
# pilco = PILCO(X, Y, controller=controller, horizon=40, reward=R)
# Example of fixing a parameter, optional, for a linear controller only
# pilco.controller.b = np.array([[0.0]])
# pilco.controller.b.trainable = False
for rollouts in range(3):
pilco.optimize_models()
pilco.optimize_policy()
X_new, Y_new, _ = rollout(env=env, pilco=pilco, timesteps=100)
# Update dataset
X = np.vstack((X, X_new))
Y = np.vstack((Y, Y_new))
pilco.mgpr.set_XY(X, Y)
input("done training! Press enter to evaluate the policy")
trained_rewards = []
for i in range(1, 100):
X_, Y_, rewards = rollout(env=env, pilco=pilco, timesteps=100)
X = np.vstack((X, X_))
Y = np.vstack((Y, Y_))
trained_rewards.append(sum(rewards))
pilco = PILCO((X, Y), controller=controller, horizon=T, m_init=m_init)
pilco.controller.max_action = e
# for numerical stability
for model in pilco.mgpr.models:
model.likelihood.variance.assign(0.001)
set_trainable(model.likelihood.variance, False)
# model.likelihood.fixed=True
return_lst = []
task_length = []
for rollouts in range(100):
print("**** ITERATION no.", rollouts, " ****")
try:
pilco.optimize_models(maxiter=maxiter)
pilco.optimize_policy(maxiter=maxiter)
except:
# for i in range(len(return_lst)):
# return_lst[i] = str(return_lst[i])
# df = pd.DataFrame(return_lst, columns=['Return per epoch'])
# df.to_csv(('/home/lab/Github/PILCO/log/Return/{}.csv'.format(time.time())))
print('Start Error!!!!!!!!!!!!!!!!!!')
X_new, Y_new, _, ep_return_lst, ep_length = rollout(env=env,
pilco=pilco,
timesteps=1000,
render=False,
verbose=verbose)
return_lst.append(ep_return_lst)
task_length.append(ep_length)
# Update dataset
def safe_swimmer_run(seed=0, logging=False):
env = SwimmerWrapper()
state_dim = 9
control_dim = 2
SUBS = 2
maxiter = 60
max_action = 1.0
m_init = np.reshape(np.zeros(state_dim),
(1, state_dim)) # initial state mean
S_init = 0.05 * np.eye(state_dim)
J = 1
N = 12
T = 25
bf = 30
T_sim = 100
# Reward function that dicourages the joints from hitting their max angles
weights_l = np.zeros(state_dim)
weights_l[0] = 0.5
max_ang = (100 / 180 * np.pi) * 0.95
R1 = LinearReward(state_dim, weights_l)
C1 = SingleConstraint(1, low=-max_ang, high=max_ang, inside=False)
C2 = SingleConstraint(2, low=-max_ang, high=max_ang, inside=False)
C3 = SingleConstraint(3, low=-max_ang, high=max_ang, inside=False)
R = CombinedRewards(state_dim, [R1, C1, C2, C3],
coefs=[1.0, -10.0, -10.0, -10.0])
th = 0.2
# Initial random rollouts to generate a dataset
X, Y, _, _ = rollout(env,
None,
timesteps=T,
random=True,
SUBS=SUBS,
verbose=True)
for i in range(1, J):
X_, Y_, _, _ = rollout(env,
None,
timesteps=T,
random=True,
SUBS=SUBS,
verbose=True)
X = np.vstack((X, X_))
Y = np.vstack((Y, Y_))
state_dim = Y.shape[1]
control_dim = X.shape[1] - state_dim
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=bf,
max_action=max_action)
pilco = PILCO((X, Y),
controller=controller,
horizon=T,
reward=R,
m_init=m_init,
S_init=S_init)
for model in pilco.mgpr.models:
model.likelihood.variance.assign(0.001)
set_trainable(model.likelihood.variance, False)
new_data = True
eval_runs = T_sim
evaluation_returns_full = np.zeros((N, eval_runs))
evaluation_returns_sampled = np.zeros((N, eval_runs))
X_eval = []
for rollouts in range(N):
print("**** ITERATION no", rollouts, " ****")
if new_data:
pilco.optimize_models(maxiter=100)
new_data = False
pilco.optimize_policy(maxiter=1, restarts=2)
m_p = np.zeros((T, state_dim))
S_p = np.zeros((T, state_dim, state_dim))
predicted_risk1 = np.zeros(T)
predicted_risk2 = np.zeros(T)
predicted_risk3 = np.zeros(T)
for h in range(T):
m_h, S_h, _ = pilco.predict(m_init, S_init, h)
m_p[h, :], S_p[h, :, :] = m_h[:], S_h[:, :]
predicted_risk1[h], _ = C1.compute_reward(m_h, S_h)
predicted_risk2[h], _ = C2.compute_reward(m_h, S_h)
predicted_risk3[h], _ = C3.compute_reward(m_h, S_h)
estimate_risk1 = 1 - np.prod(1.0 - predicted_risk1)
estimate_risk2 = 1 - np.prod(1.0 - predicted_risk2)
estimate_risk3 = 1 - np.prod(1.0 - predicted_risk3)
overall_risk = 1 - (1 - estimate_risk1) * (1 - estimate_risk2) * (
1 - estimate_risk3)
if overall_risk < th:
X_new, Y_new, _, _ = rollout(env,
pilco,
timesteps=T_sim,
verbose=True,
SUBS=SUBS)
new_data = True
# Update dataset
X = np.vstack((X, X_new[:T, :]))
Y = np.vstack((Y, Y_new[:T, :]))
pilco.mgpr.set_data((X, Y))
if estimate_risk1 < th / 10:
R.coefs.assign(R.coefs.value() * [1.0, 0.75, 1.0, 1.0])
if estimate_risk2 < th / 10:
R.coefs.assign(R.coefs.value() * [1.0, 1.0, 0.75, 1.0])
if estimate_risk3 < th / 10:
R.coefs.assign(R.coefs.value() * [1.0, 1.0, 1.0, 0.75])
else:
print("*********CHANGING***********")
if estimate_risk1 > th / 3:
R.coefs.assign(R.coefs.value() * [1.0, 1.5, 1.0, 1.0])
if estimate_risk2 > th / 3:
R.coefs.assign(R.coefs.value() * [1.0, 1.0, 1.5, 1.0])
if estimate_risk3 > th / 3:
R.coefs.assign(R.coefs.value() * [1.0, 1.0, 1.0, 1.5])
_, _, r = pilco.predict(m_init, S_init, T)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = PILCO(X, Y, controller=controller, horizon=T, reward=R, m_init=m_init, S_init=S_init)
# for numerical stability
for model in pilco.mgpr.models:
# model.kern.lengthscales.prior = gpflow.priors.Gamma(1,10) priors have to be included before
# model.kern.variance.prior = gpflow.priors.Gamma(1.5,2) before the model gets compiled
model.likelihood.variance = 0.001
model.likelihood.variance.trainable = False
for rollouts in range(N):
print("**** ITERATION no", rollouts, " ****")
pilco.optimize_models(maxiter=maxiter, restarts=2)
pilco.optimize_policy(maxiter=maxiter, restarts=2)
X_new, Y_new = rollout(env, pilco, timesteps=T_sim, verbose=True, SUBS=SUBS)
# Since we had decide on the various parameters of the reward function
# we might want to verify that it behaves as expected by inspection
# cur_rew = 0
# for t in range(0,len(X_new)):
# cur_rew += reward_wrapper(R, X_new[t, 0:state_dim, None].transpose(), 0.0001 * np.eye(state_dim))[0]
# print('On this episode reward was ', cur_rew)
# Update dataset
X = np.vstack((X, X_new[:T, :])); Y = np.vstack((Y, Y_new[:T, :]))
pilco.mgpr.set_XY(X, Y)
lens.append(len(X_new))
control_dim = X.shape[1] - state_dim
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=10,
max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = PILCO(X,
Y,
controller=controller,
horizon=T,
reward=R,
m_init=m_init,
S_init=S_init)
pilco.optimize_models(maxiter=100)
pilco.optimize_policy(maxiter=20)
# Rollout using the pilco controller
X_new, Y_new = rollout(env, pilco, timesteps=T, SUBS=SUBS, render=False)
for i, m in enumerate(pilco.mgpr.models):
y_pred_test, var_pred_test = m.predict_y(X_new)
plt.plot(range(len(y_pred_test)), y_pred_test, Y_new[:, i])
plt.fill_between(range(len(y_pred_test)),
y_pred_test[:, 0] - 2 * np.sqrt(var_pred_test[:, 0]),
y_pred_test[:, 0] + 2 * np.sqrt(var_pred_test[:, 0]),
alpha=0.3)
plt.show()
np.shape(var_pred_test)
def swimmer_run(name, seed):
env = gym.make('Swimmer-v2').env
#env = SwimmerWrapper()
state_dim = 8
control_dim = 2
SUBS = 5
maxiter = 120
max_action = 1.0
m_init = np.reshape(np.zeros(state_dim),
(1, state_dim)) # initial state mean
S_init = 0.005 * np.eye(state_dim)
J = 10
N = 15
T = 15
bf = 40
T_sim = 50
# Reward function that dicourages the joints from hitting their max angles
weights_l = np.zeros(state_dim)
weights_l[3] = 1.0
max_ang = 95 / 180 * np.pi
t1 = np.zeros(state_dim)
t1[2] = max_ang
w1 = 1e-6 * np.eye(state_dim)
w1[2, 2] = 10
t2 = np.zeros(state_dim)
t2[1] = max_ang
w2 = 1e-6 * np.eye(state_dim)
w2[1, 1] = 10
t5 = np.zeros(state_dim)
#t5[0] = max_ang
#w3 = 1e-6 * np.eye(state_dim)
#w3[0,0] = 5
t3 = np.zeros(state_dim)
t3[2] = -max_ang
t4 = np.zeros(state_dim)
t4[1] = -max_ang
#t6 = np.zeros(state_dim); t6[0] = -max_ang
R2 = LinearReward(state_dim, weights_l)
R3 = ExponentialReward(state_dim, W=w1, t=t1)
R4 = ExponentialReward(state_dim, W=w2, t=t2)
R5 = ExponentialReward(state_dim, W=w1, t=t3)
R6 = ExponentialReward(state_dim, W=w2, t=t4)
#R7 = ExponentialReward(state_dim, W=w3, t=t5)
#R8 = ExponentialReward(state_dim, W=w3, t=t6)
Rew = CombinedRewards(state_dim, [R2, R3, R4, R5, R6],
coefs=[1.0, -1.0, -1.0, -1.0, -1.0])
# Rew = R2
# Initial random rollouts to generate a dataset
X, Y, _, _ = rollout(env,
None,
timesteps=T,
random=True,
SUBS=SUBS,
verbose=True,
render=False)
for i in range(1, J):
X_, Y_, _, _ = rollout(env,
None,
timesteps=T,
random=True,
SUBS=SUBS,
verbose=True,
render=False)
X = np.vstack((X, X_))
Y = np.vstack((Y, Y_))
state_dim = Y.shape[1]
control_dim = X.shape[1] - state_dim
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=bf,
max_action=max_action)
# controller = LinearController(state_dim=state_dim, control_dim=control_dim, max_action=max_action)
pilco = PILCO((X, Y),
controller=controller,
horizon=T,
reward=Rew,
m_init=m_init,
S_init=S_init)
#for model in pilco.mgpr.models:
# model.likelihood.variance = 0.0001
# model.likelihood.variance.trainable = False
logging = True # To save results in .csv files turn this flag to True
eval_runs = 10
evaluation_returns_full = np.zeros((N, eval_runs))
evaluation_returns_sampled = np.zeros((N, eval_runs))
eval_max_timesteps = 1000 // SUBS
X_eval = False
for rollouts in range(N):
print("**** ITERATION no", rollouts, " ****")
pilco.optimize_models(restarts=2)
pilco.optimize_policy(maxiter=maxiter, restarts=2)
X_new, Y_new, _, _ = rollout(env,
pilco,
timesteps=T_sim,
verbose=True,
SUBS=SUBS,
render=False)
cur_rew = 0
for t in range(0, len(X_new)):
cur_rew += Rew.compute_reward(
X_new[t, 0:state_dim, None].transpose(),
0.0001 * np.eye(state_dim))[0]
if t == T:
print(
'On this episode, on the planning horizon, PILCO reward was: ',
cur_rew)
print('On this episode PILCO reward was ', cur_rew)
gym_steps = 1000
T_eval = gym_steps // SUBS
# Update dataset
X = np.vstack((X, X_new[:T, :]))
Y = np.vstack((Y, Y_new[:T, :]))
pilco.mgpr.set_data((X, Y))
if logging:
if eval_max_timesteps is None:
eval_max_timesteps = sim_timesteps
for k in range(0, eval_runs):
[
X_eval_, _, evaluation_returns_sampled[rollouts, k],
evaluation_returns_full[rollouts, k]
] = rollout(env,
pilco,
timesteps=eval_max_timesteps,
verbose=False,
SUBS=SUBS,
render=False)
if rollouts == 0:
X_eval = X_eval_.copy()
else:
X_eval = np.vstack((X_eval, X_eval_))
if not os.path.exists(name):
os.makedirs(name)
np.savetxt(name + "X_" + seed + ".csv", X, delimiter=',')
np.savetxt(name + "X_eval_" + seed + ".csv", X_eval, delimiter=',')
np.savetxt(name + "evaluation_returns_sampled_" + seed + ".csv",
evaluation_returns_sampled,
delimiter=',')
np.savetxt(name + "evaluation_returns_full_" + seed + ".csv",
evaluation_returns_full,
delimiter=',')
def pilco_run(env,
N,
J,
safe=False,
name='',
seed=0,
cont=None,
rew=None,
SUBS=1,
sim_timesteps=50,
plan_timesteps=30,
restarts=1,
maxiter=100,
m_init=None,
S_init=None,
fixed_noise=None,
logging=False,
eval_runs=5,
eval_max_timesteps=None,
variable_episode_length=False):
np.random.seed(seed)
X, Y, _, _ = rollout(env=env,
pilco=None,
timesteps=sim_timesteps,
random=True,
SUBS=SUBS)
for i in range(1, J):
X_, Y_, _, _ = rollout(env=env,
pilco=None,
timesteps=sim_timesteps,
random=True,
SUBS=SUBS)
X = np.vstack((X, X_))
Y = np.vstack((Y, Y_))
state_dim = Y.shape[1]
control_dim = X.shape[1] - state_dim
if cont is None:
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=5)
elif cont['type'] == 'rbf':
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=cont['basis_functions'],
max_action=cont.get('max_action', 1.0))
elif cont['type'] == 'linear':
controller = LinearController(state_dim=state_dim,
control_dim=control_dim,
max_action=cont.get('max_action', 1.0))
else:
ValueError('Invalid Controller')
if rew is None:
reward = None
elif rew['type'] == 'exp':
reward = ExponentialReward(state_dim=state_dim, t=rew['t'], W=rew['W'])
else:
ValueError('This function only handles Exponential rewards for now')
pilco = PILCO((X, Y),
controller=controller,
reward=reward,
horizon=plan_timesteps,
m_init=m_init,
S_init=S_init)
if fixed_noise is not None:
for model in pilco.mgpr.models:
model.likelihood.variance.assign(fixed_noise)
set_trainable(model.likelihood.variance, False)
evaluation_returns_full = np.zeros((N, eval_runs))
evaluation_returns_sampled = np.zeros((N, eval_runs))
if name == '':
from datetime import datetime
current_time = datetime.now()
name = current_time.strftime("%d_%m_%Y_%H_%M_%S")
for rollouts in range(N):
print("**** ITERATION no", rollouts, " ****")
pilco.optimize_models()
pilco.optimize_policy(maxiter=maxiter, restarts=restarts)
X_new, Y_new, _, _ = rollout(env,
pilco,
timesteps=sim_timesteps,
SUBS=SUBS,
verbose=True)
cur_rew = 0
X = np.vstack((X, X_new[:plan_timesteps, :]))
Y = np.vstack((Y, Y_new[:plan_timesteps, :]))
pilco.mgpr.set_data((X, Y))
if logging:
if eval_max_timesteps is None:
eval_max_timesteps = sim_timesteps
for k in range(0, eval_runs):
[
X_eval_, _, evaluation_returns_sampled[rollouts, k],
evaluation_returns_full[rollouts, k]
] = rollout(env,
pilco,
timesteps=eval_max_timesteps,
verbose=False,
SUBS=SUBS,
render=False)
if rollouts == 0 and k == 0:
X_eval = X_eval_.copy()
else:
X_eval = np.vstack((X_eval, X_eval_))
if not os.path.exists("results/" + name):
os.makedirs("results/" + name)
np.savetxt("results/" + name + "X_" + str(seed) + ".csv",
X,
delimiter=',')
np.savetxt("results/" + name + "X_eval_" + str(seed) + ".csv",
X_eval,
delimiter=',')
np.savetxt("results/" + name + "evaluation_returns_sampled_" +
str(seed) + ".csv",
evaluation_returns_sampled,
delimiter=',')
np.savetxt("results/" + name + "evaluation_returns_full_" +
str(seed) + ".csv",
evaluation_returns_full,
delimiter=',')
all_Rs[i,0] = R.compute_reward(X[i,None,:-1], 0.001 * np.eye(state_dim))[0] # Reward for each step
total_rewards = np.zeros((len(X)//T,1))
ep_rewards = np.zeros((len(X)//T,1))
for i in range(len(total_rewards)):
total_rewards[i] = sum(all_Rs[i * T: i*T + T])
ep_rewards[i] = sum(all_Rs[i * T: i*T + T])
for rollouts in range(1,31):
# Optimazation
print("**** ITERATION no", rollouts, " ****")
pilco.optimize_models(maxiter=2000,restarts=3) # Restart to avoid local minima
pilco.optimize_policy(maxiter=2000,restarts=3)
# Initialize
robot_state.set_pose(x=offsetPath[0][0], y=offsetPath[1][0], yaw=offsetPath[2][0], yawRate = 0.0)
robot_state.set_steerAngle(steerAngle=0.0)
pathHandler.reset__index_nn()
# Testing
X_new, Y_new ,j = pilcotrac.rollout(pilco,max_yerror,data_mean=data_mean,data_std=data_std,lookahead= 1.22,timesteps=T_sim,random=False, verbose=False)
r_new = np.zeros((len(X_new), 1))
var_r = np.zeros((len(X_new), 1))
r_tar = np.zeros((len(X_new), 1))
for i in range(len(X_new)):
r_new[i, 0],var_r[i,0] = R.compute_reward(X_new[i,None,:-1], S_init)
r_tar[i, 0] = R.compute_reward(target[None, :], S_init)[0]
X = X.values
Y = Y.values
print('startk = ', startk)
print('X.shape = ', X.shape)
print('Y.shape =', Y.shape)
# X = X.T; Y = Y.T
T = int(30 * 2)
m_init = np.reshape([0.0, 0.0, 0.0, 0.0], (1, 4))
S_init = np.diag([0.01, 0.01, 0.01, 0.01])
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=10)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco_model = PILCO((X, Y),
controller=controller,
horizon=T,
reward=R,
m_init=m_init,
S_init=S_init)
pilco_model.optimize_models(maxiter=100)
pilco_model.optimize_policy(maxiter=20)
with open(out_fname, 'wb') as wf:
frozen_model = gpflow.utilities.freeze(pilco_model)
dill.dump(frozen_model, wf)
