def plot_pilco_source_learning_curve():
env = gym.make('continuous-cartpole-v0')
env.seed(73)
pilcos = ['initial'] + [str(i) for i in range(6)]
rewards = []
for i, p in enumerate(pilcos):
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=bf,
max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco('saved/pilco-continuous-cartpole-{:s}'.format(p),
controller=controller,
reward=R,
sparse=False)
score_logger = ScoreLogger('Score for Model {:d}'.format(i))
state = env.reset()
step = 0
xs = []
angles = []
while True:
xs.append(state[0])
angles.append(state[2])
step += 1
env.render()
u_action = utils.policy(env, pilco, state, False)
state_copy = state
a = np.ndarray.tolist(state_copy)
a.extend(np.ndarray.tolist(u_action))
state_next, reward, terminal, info = env.step(u_action)
reward = reward if not terminal else -reward
state = state_next
if terminal:
print('Run: {:d}, score: {:d}'.format(i, step))
score_logger.add_score(step, i)
break
rewards.append(step)
plt.plot(xs, angles)
plt.savefig('pilco-{:d}_states_plot'.format(i), bbox_inches="tight")
plt.close()
env.close()
plt.plot([i for i, _ in enumerate(pilcos)], rewards)
plt.savefig('pilco_rewards_plot', bbox_inches="tight")
plt.close()
return rewards, xs, angles
def loader(name):
env = gym.make('continuous-cartpole-v99')
env.seed(73)
controller = RbfController(state_dim=state_dim, control_dim=control_dim, num_basis_functions=bf, max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco('saved/pilco-continuous-cartpole-{:s}'.format(name), controller=controller, reward=R, sparse=False)
score_logger = ScoreLogger('PI ADJUST ANALYSISSSSSSS')
# observation_space = env.observation_space.shape[0]
run = 0
while True:
run += 1
state = env.reset()
step = 0
while True:
step += 1
env.render()
#TODO RUN PI ADJUST
action = utils.policy(env, pilco, state, False)
# TODO RUN PI ADJUST COMMENT THE NEXT LINE
state_next, reward, terminal, info = env.step(action)
# reward = reward if not terminal else -reward
state = state_next
if terminal:
print("Run: " + str(run) + ", score: " + str(step))
score_logger.add_score(step, run)
break
env.env.close()
def get_RewardFun_and_Controller(self, state_dim, control_dim, data_mean,
data_std, max_yerror, W):
t1 = np.divide([max_yerror, 0.0, 0.0, 0.0] - data_mean, data_std)
t2 = np.divide([-max_yerror, 0.0, 0.0, 0.0] - data_mean, data_std)
R1 = ExponentialReward(state_dim, W, t=t1)
R2 = ExponentialReward(state_dim, W, t=t2)
target = np.divide([0.0, 0.0, 0.0, 0.0] - data_mean, data_std)
Re = ExponentialReward(state_dim=state_dim, t=target, W=W)
R = CombinedRewards(state_dim, [Re, R1, R2], coefs=[2, -1, -1])
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=30)
# num_basis_functions: number of RBFuntions, a large number of functions is flexber but increases computational demands.
# If v==4: num_basis_functions=50
return R, controller
def load_and_run_model(env, name):
controller = RbfController(state_dim=state_dim, control_dim=control_dim, num_basis_functions=bf, max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco('saved/pilco-continuous-cartpole-{:s}'.format(name), controller=controller, reward=R, sparse=False)
print('Running {:s}'.format(name))
rollout(env, pilco, timesteps=T_sim, verbose=False, SUBS=SUBS)
def piadjust(NT, name):
controller = RbfController(state_dim=state_dim, control_dim=control_dim, num_basis_functions=bf, max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco('saved/pilco-continuous-cartpole-{:s}'.format(name), controller=controller, reward=R, sparse=False)
env_S = gym.make('continuous-cartpole-v0')
env_S.seed(73)
env_T = gym.make('continuous-cartpole-v99')
env_T.seed(73)
D_S = sampler(pilco, env_S, samples_n=30, trials=50)
# print('D_S sampling done')
D_T = None
pi_adj = pilco
for i in range(NT):
print('{:d}/{:d}'.format(i + 1, NT))
D_adj = []
if i == 0:
D_i_T = sampler(pilco, env_T, samples_n=30)
elif i != 0:
D_i_T = sampler_adj(pi_adj, pilco, env_T, 30)
if D_T is not None:
D_T = np.concatenate((D_i_T, D_T))
elif D_T is None:
D_T = D_i_T
# print('Going for inverse dyn')
gpr = inverse_dyn(D_T)
# print('inverse dyn done')
for samp in D_S:
x_s = list(samp[0])
x_s1 = list(samp[2])
u_t_S = samp[1]
a = np.array(x_s + x_s1).reshape(1, 8)
u_t_T = gpr.predict(a, return_std=False)
D_adj.append((x_s, u_t_S, u_t_T - u_t_S))
# print('Going for L3')
pi_adj = L3(D_adj)
# print('L3 Done')
# i = i + 1
# if (i % 1 == 0):
save_object(pi_adj, 'transfer-save/pilco-{:s}-transfer-{:d}.pkl'.format(name, i))
env_S.env.close()
env_T.env.close()
return pi_adj
def see_progression(pilco_name='saved/pilco-continuous-cartpole-5',
transfer_name='{:d}true_dyn_pi_adj.pkl',
adjust=True):
env = gym.make('continuous-cartpole-v99')
env.seed(1)
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=bf,
max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco(pilco_name,
controller=controller,
reward=R,
sparse=False)
rewards = []
for i in range(10):
print('Running {:s}'.format(transfer_name.format(i)))
if adjust:
with open(transfer_name.format(i), 'rb') as inp2:
pi_adjust = pickle.load(inp2)
score_logger = ScoreLogger('Score for Model {:d}'.format(i))
state = env.reset()
step = 0
while True:
step += 1
env.render()
u_action = utils.policy(env, pilco, state, False)
state_copy = state
a = np.ndarray.tolist(state_copy)
a.extend(np.ndarray.tolist(u_action))
if adjust:
pi_adjust_action = pi_adjust.predict(
np.array(a).reshape(1, -1))[0]
else:
pi_adjust_action = 0 # ENABLE THIS TO SEE IT RUN WITHOUT THE ADJUSTMENT
state_next, reward, terminal, info = env.step(u_action +
pi_adjust_action)
reward = reward if not terminal else -reward
state = state_next
if terminal:
print('Run: {:d}, score: {:d}'.format(i, step))
score_logger.add_score(step, i)
break
rewards.append(step)
env.close()
return rewards
def test_reward():
'''
Test reward function by comparing to reward.m
'''
k = 2 # state dim
m = np.random.rand(1, k)
s = np.random.rand(k, k)
s = s.dot(s.T)
reward = ExponentialReward(k)
W = reward.W.numpy()
t = reward.t.numpy()
M, S = reward.compute_reward(m, s)
M_mat, _, _, S_mat = octave.reward(m.T, s, t.T, W, nout=4)
np.testing.assert_allclose(M, M_mat)
np.testing.assert_allclose(S, S_mat)
def run_transfer_model_and_plot_pos(env_name, pilco_name, fig_file_name, fig_title, transfer_name=None, save=True):
env = gym.make(env_name)
env.seed(1)
controller = RbfController(state_dim=state_dim, control_dim=control_dim, num_basis_functions=bf, max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco('saved/pilco-continuous-cartpole-{:s}'.format(pilco_name), controller=controller, reward=R, sparse=False)
if transfer_name is not None:
with open(transfer_name, 'rb') as inp2:
pi_adjust = pickle.load(inp2)
xs = []
angles = []
state = env.reset()
for _ in range(1000):
xs.append(state[0])
angles.append(state[2])
env.render()
u_action = policy(env, pilco, state, False)
state_copy = state
a = np.ndarray.tolist(state_copy)
a.extend(np.ndarray.tolist(u_action))
if transfer_name is not None:
pi_adjust_action = pi_adjust.predict(np.array(a).reshape(1, -1))[0]
else:
pi_adjust_action = 0
state_next, reward, terminal, info = env.step(u_action + pi_adjust_action)
state = state_next
if terminal:
break
env.close()
xs = np.array(xs)
angles = np.array(angles)
plt.plot(xs, angles)
plt.quiver(xs[:-1], angles[:-1], xs[1:] - xs[:-1], angles[1:] - angles[:-1], scale_units='xy', angles='xy', scale=1, color='blue', width=1e-2)
plt.xlabel('position')
plt.ylabel('angle')
plt.title(fig_title)
plt.xlim(-0.2, 0.2)
plt.ylim(-0.2, 0.2)
if save:
plt.savefig(fig_file_name, bbox_inches="tight")
plt.close()
def test_reward():
'''
Test reward function by comparing to reward.m
'''
k = 2 # state dim
m = np.random.rand(1, k)
s = np.random.randn(k, k)
s = s.dot(s.T)
reward = ExponentialReward(k)
W = reward.W.data.cpu().numpy()
t = reward.t.data.cpu().numpy()
M, S = reward.compute_reward(
torch.tensor(m).float().cuda(),
torch.tensor(s).float().cuda())
M_mat, _, _, S_mat = octave.reward(m.T, s, t.T, W, nout=4)
import pdb
pdb.set_trace()
np.testing.assert_allclose(M.cpu().numpy(), M_mat)
np.testing.assert_allclose(S.cpu().numpy(), S_mat)
def souce_loader(name):
Rs = np.empty(10).reshape(1, 10)
env = gym.make('continuous-cartpole-v99')
env.seed(73)
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=bf,
max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco('saved/pilco-continuous-cartpole-{:s}'.format(name),
controller=controller,
reward=R,
sparse=False)
env = gym.make('continuous-cartpole-v99')
pi_adjust = None
score_logger = ScoreLogger('PI ADJUST ANALYSISSSSSSS')
run = 0
avg_reward = 0
while run != 101:
run += 1
if (run % 20 == 0):
print('run: ', run)
state = env.reset()
# print(state)
# input()
step = 0
while True:
step += 1
# env.render()
# TODO RUN PI ADJUST
u_action = utils.policy(env, pilco, state, False)
state_copy = state
# TODO RUN PI ADJUST COMMENT THE NEXT LINE
state_next, reward, terminal, info = env.step(u_action)
reward = reward if not terminal else -reward
state = state_next
if terminal:
# print("Run: " + ", score: " + str(step))
score_logger.add_score(step, run)
avg_reward = avg_reward + step
break
avg_reward = avg_reward / run
env.env.close()
return (avg_reward)
def true_loader(name):
env = gym.make('continuous-cartpole-v99')
env.seed(73)
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=bf,
max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco('saved/pilco-continuous-cartpole-{:s}'.format(name),
controller=controller,
reward=R,
sparse=False)
with open('9true_dyn_pi_adj.pkl', 'rb') as inp2:
pi_adjust = pickle.load(inp2)
# with open('10_pi_adj.pkl', 'rb') as inp2:
# good_pi = pickle.load(inp2)
score_logger = ScoreLogger('PI ADJUST ANALYSISSSSSSS')
run = 0
while True:
run += 1
state = env.reset()
# print(state)
# input()
step = 0
while True:
step += 1
env.render()
u_action = utils.policy(env, pilco, state, False)
state_copy = state
a = np.ndarray.tolist(state_copy)
a.extend(np.ndarray.tolist(u_action))
action = pi_adjust.predict(np.array(a).reshape(1, -1))[0]
state_next, reward, terminal, info = env.step(action + u_action)
reward = reward if not terminal else -reward
state = state_next
if terminal:
print("Run: " + ", score: " + str(step))
score_logger.add_score(step, run)
break
env.env.close()
timesteps=T,
random=True,
SUBS=SUBS,
verbose=True,
render=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)
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, we can set the likelihood variance parameters of the GP models
for model in pilco.mgpr.models:
model.likelihood.variance.assign(0.001)
set_trainable(model.likelihood.variance, False)
r_new = np.zeros((T, 1))
for rollouts in range(N):
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=',')
X = np.vstack((X, X_))
Y = np.vstack((Y, Y_))
print('experience collected. ')
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=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(1):
print('optimizing models')
pilco.optimize_models()
print('optimizing policy')
pilco.optimize_policy()
# import pdb; pdb.set_trace()
X_new, Y_new = rollout(env=env,
pilco=pilco,
timesteps=100,
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
max_ang = 95 / 180 * np.pi
rewards = []
rewards.append(LinearReward(state_dim, [0, 0, 0, 1.0, 0, 0, 0, 0]))
rewards.append(
ExponentialReward(state_dim,
W=np.diag(np.array([0, 0, 10, 0, 0, 0, 0, 0]) + 1e-6),
t=[0, 0, max_ang, 0, 0, 0, 0, 0]))
rewards.append(
ExponentialReward(state_dim,
W=np.diag(np.array([0, 0, 10, 0, 0, 0, 0, 0]) + 1e-6),
t=[0, 0, -max_ang, 0, 0, 0, 0, 0]))
rewards.append(
ExponentialReward(state_dim,
W=np.diag(np.array([0, 10, 0, 0, 0, 0, 0, 0]) + 1e-6),
t=[0, max_ang, 0, 0, 0, 0, 0, 0]))
rewards.append(
ExponentialReward(state_dim,
W=np.diag(np.array([0, 10, 0, 0, 0, 0, 0, 0]) + 1e-6),
t=[0, -max_ang, 0, 0, 0, 0, 0, 0]))
combined_reward = CombinedRewards(state_dim,
rewards,
np.std(X1[:, :2], 0))
X = np.zeros(X1.shape)
X[:, :2] = np.divide(X1[:, :2] - np.mean(X1[:, :2], 0), np.std(X1[:, :2], 0))
X[:, 2] = X1[:, -1] # control inputs are not normalised
Y = np.divide(Y1, np.std(X1[:, :2], 0))
state_dim = Y.shape[1]
control_dim = X.shape[1] - state_dim
m_init = np.transpose(X[0, :-1, None])
S_init = 0.5 * np.eye(state_dim)
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=25)
R = ExponentialReward(state_dim=state_dim,
t=np.divide([0.5, 0.0] - env.m, env.std),
W=np.diag([0.5, 0.1]))
pilco = PILCO((X, Y),
controller=controller,
horizon=T,
reward=R,
m_init=m_init,
S_init=S_init)
best_r = 0
all_Rs = np.zeros((X.shape[0], 1))
for i in range(len(all_Rs)):
all_Rs[i, 0] = R.compute_reward(X[i, None, :-1],
0.001 * np.eye(state_dim))[0]
ep_rewards = np.zeros((len(X) // T, 1))
X = np.vstack((X, X_))
Y = np.vstack((Y, Y_))
state_dim = Y.shape[1]
control_dim = X.shape[1] - state_dim
controller = LinearController(state_dim=state_dim,
control_dim=control_dim,
max_action=1.0)
if safe:
if experim_num == 1:
C1 = SingleConstraint(0, high=max_temp, inside=False)
else:
C1 = SingleConstraint(1, high=max_temp, inside=False)
R1 = ExponentialReward(state_dim=state_dim, t=targets)
R = CombinedRewards(state_dim, [R1, C1], coefs=[1.0, -15.0])
else:
R1 = ExponentialReward(state_dim=state_dim, t=targets, W=weights)
t2 = targets.copy()
t2[1] = 20.5
w2 = weights.copy()
w2[1, 1] = 4.0
R2 = ExponentialReward(state_dim=state_dim, t=t2, W=w2)
R = CombinedRewards(state_dim, [R1, R2], coefs=[1.0, -2.0])
pilco = SafePILCO((X, Y),
controller=controller,
horizon=T,
reward_add=R1,
reward_mult=C1,
def loader(name):
Rs = np.empty(10).reshape(1, 10)
env = gym.make('continuous-cartpole-v99')
env.seed(73)
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=bf,
max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco('saved/pilco-continuous-cartpole-{:s}'.format(name),
controller=controller,
reward=R,
sparse=False)
for pick in range(1, 11):
env = gym.make('continuous-cartpole-v99')
with open(str(pick) + '_pi_adj.pkl', 'rb') as inp2:
pi_adjust = pickle.load(inp2)
score_logger = ScoreLogger('PI ADJUST ANALYSISSSSSSS')
run = 0
avg_reward = 0
while run != 101:
run += 1
if (run % 20 == 0):
print('run: ', run)
state = env.reset()
# print(state)
# input()
step = 0
while True:
step += 1
#env.render()
#TODO RUN PI ADJUST
u_action = utils.policy(env, pilco, state, False)
state_copy = state
a = np.ndarray.tolist(state_copy)
a.extend(np.ndarray.tolist(u_action))
action = pi_adjust.predict(np.array(a).reshape(1, -1))
action = action[0]
if action[0] > 1:
action[0] = 1
elif action[0] < -1:
action[0] = -1
# TODO RUN PI ADJUST COMMENT THE NEXT LINE
state_next, reward, terminal, info = env.step(action)
reward = reward if not terminal else -reward
state = state_next
if terminal:
# print("Run: " + ", score: " + str(step))
score_logger.add_score(step, run)
avg_reward = avg_reward + step
break
avg_reward = avg_reward / run
env.env.close()
Rs[0][pick - 1] = avg_reward
return (Rs)
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=',')
Y = np.vstack((Y, Y_)).astype(np.float64)
for i in range(1,4): #No action sampling; u := 0
X_, Y_ = rollout(env=env, pilco=None, random=None, timesteps=80, SUBS=subs, render=False)
X = np.vstack((X, X_)).astype(np.float64)
Y = np.vstack((Y, Y_)).astype(np.float64)
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=18, max_action=env.action_space.high)
#controller = LinearController(state_dim=state_dim, control_dim=control_dim)
#print(X)
#pilco = PILCO(X, Y, controller=controller, horizon=40, num_induced_points=72)
# Example of user provided reward function, setting a custom target state
R = ExponentialReward(state_dim=state_dim, t=target)
pilco = PILCO(X, Y, controller=controller, horizon=20, reward=R,
num_induced_points=72, m_init=m_init)
# Example of fixing a parameter, optional, for a linear controller only
#pilco.controller.b = np.array([[0.0]])
#pilco.controller.b.trainable = False
plot_model = False # Plot model set to false until iteration 5 is reached
for rollouts in range(20):
print("Optimizing models")
pilco.optimize_models()
print("Optimizing controller")
pilco.optimize_policy(maxiter=20)
#import pdb; pdb.set_trace()
X_new, Y_new = rollout(env=env, pilco=pilco, timesteps=120, SUBS=subs, render=False)
print("No of ops:", len(tf.get_default_graph().get_operations()))
if __name__ == '__main__':
bf = 10
max_action = 1.0
target = np.array([0., 0., 0., 0.])
weights = np.diag([0.5, 0.1, 0.5, 0.25])
T = 400
state_dim = 4
control_dim = 1
with tf.Session() as sess:
env = gym.make('continuous-cartpole-v0')
controller = RbfController(state_dim=state_dim,
control_dim=control_dim,
num_basis_functions=bf,
max_action=max_action)
R = ExponentialReward(state_dim=state_dim, t=target, W=weights)
pilco = load_pilco('saved/pilco-continuous-cartpole-5',
controller=controller,
reward=R,
sparse=False)
pilco.act = lambda x: pilco.compute_action(x[None, :])[0, :]
env_s = gym.make('continuous-cartpole-v0')
env_t = gym.make('continuous-cartpole-v1')
padjust(env_s, env_t, pilco)
weights_l[0] = 0.1
max_ang = 100 / 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[3] = max_ang
w2 = 1e-6 * np.eye(state_dim)
w2[3, 3] = 10
t3 = np.zeros(state_dim)
t3[2] = -max_ang
t4 = np.zeros(state_dim)
t4[3] = -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)
R = CombinedRewards(state_dim, [R2, R3, R4, R5, R6],
coefs=[1.0, -1.0, -1.0, -1.0, -1.0])
# Initial random rollouts to generate a dataset
X, Y = rollout(env, None, timesteps=T, random=True, SUBS=SUBS)
for i in range(1, J):
X_, Y_ = rollout(env,
None,
timesteps=T,
random=True,
SUBS=SUBS,
verbose=True)