def test_PGPE():
distribution = learn(PGPE, learning_rate=AdaptiveParameter(1.5))
w = distribution.get_parameters()
w_test = np.array([
0.02489092, 0.31062211, 0.2051433, 0.05959651, -0.78302236, 0.77381954,
0.23676176, -0.29855654
])
assert np.allclose(w, w_test)
def experiment(alg, n_runs, n_iterations, ep_per_run, use_tensorflow):
np.random.seed()
# MDP
mdp = ShipSteering()
# Policy
if use_tensorflow:
tensor_list = gaussian_tensor.generate(
[3, 3, 6, 2], [[0., 150.], [0., 150.], [-np.pi, np.pi],
[-np.pi / 12, np.pi / 12]])
phi = Features(tensor_list=tensor_list,
name='phi',
input_dim=mdp.info.observation_space.shape[0])
else:
basis = GaussianRBF.generate([3, 3, 6, 2],
[[0., 150.], [0., 150.], [-np.pi, np.pi],
[-np.pi / 12, np.pi / 12]])
phi = Features(basis_list=basis)
input_shape = (phi.size, )
approximator_params = dict(input_dim=phi.size)
approximator = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape,
params=approximator_params)
sigma = np.array([[.05]])
policy = MultivariateGaussianPolicy(mu=approximator, sigma=sigma)
# Agent
learning_rate = AdaptiveParameter(value=.01)
algorithm_params = dict(learning_rate=learning_rate)
fit_params = dict()
agent_params = {
'algorithm_params': algorithm_params,
'fit_params': fit_params
}
agent = alg(policy, mdp.info, agent_params, phi)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
print('J at start : ' + str(np.mean(J)))
for i in xrange(n_runs):
core.learn(n_episodes=n_iterations * ep_per_run,
n_episodes_per_fit=ep_per_run)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
print('J at iteration ' + str(i) + ': ' + str(np.mean(J)))
np.save('ship_steering.npy', dataset_eval)
def test_PGPE():
distribution = GaussianDiagonalDistribution(mu, sigma)
agent = PGPE(distribution, policy, mdp.info,
learning_rate=AdaptiveParameter(1.5), features=phi)
agent.episode_start()
agent.fit(dataset)
w_1 = .54454343
w_2 = .5449792
w = agent.policy.get_weights()
assert np.allclose(w_1, w[10])
assert np.allclose(w_2, w[18])
def experiment(alg, n_epochs, n_iterations, ep_per_run):
np.random.seed()
# MDP
mdp = LQR.generate(dimensions=1)
approximator_params = dict(input_dim=mdp.info.observation_space.shape)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape,
params=approximator_params)
sigma = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape,
params=approximator_params)
sigma_weights = 2 * np.ones(sigma.weights_size)
sigma.set_weights(sigma_weights)
policy = StateStdGaussianPolicy(approximator, sigma)
# Agent
learning_rate = AdaptiveParameter(value=.01)
algorithm_params = dict(learning_rate=learning_rate)
agent = alg(policy, mdp.info, **algorithm_params)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
print('policy parameters: ', policy.get_weights())
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
print('J at start : ' + str(np.mean(J)))
for i in range(n_epochs):
core.learn(n_episodes=n_iterations * ep_per_run,
n_episodes_per_fit=ep_per_run)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
print('policy parameters: ', policy.get_weights())
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
print('J at iteration ' + str(i) + ': ' + str(np.mean(J)))
def experiment(alg, n_runs, n_iterations, ep_per_run):
np.random.seed()
# MDP
mdp = LQR.generate(dimensions=1)
approximator_params = dict(input_dim=mdp.info.observation_space.shape)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape,
params=approximator_params)
sigma = .1 * np.eye(1)
policy = MultivariateGaussianPolicy(mu=approximator, sigma=sigma)
# Agent
learning_rate = AdaptiveParameter(value=.01)
algorithm_params = dict(learning_rate=learning_rate)
fit_params = dict()
agent_params = {
'algorithm_params': algorithm_params,
'fit_params': fit_params
}
agent = alg(policy, mdp.info, agent_params)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
print 'policy parameters: ', policy.get_weights()
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
print('J at start : ' + str(np.mean(J)))
for i in xrange(n_runs):
core.learn(n_episodes=n_iterations * ep_per_run,
n_episodes_per_fit=ep_per_run)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
print 'policy parameters: ', policy.get_weights()
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
print('J at iteration ' + str(i) + ': ' + str(np.mean(J)))
np.save('ship_steering.npy', dataset_eval)
ep_per_run_hier = ep_per_epoch // n_iterations_hier
print('High: ', alg_h.__name__, ' Low: ', alg_l.__name__)
res = Parallel(n_jobs=n_jobs)(delayed(hierarchical_experiment)
(mdp, agent_l, agent_h,
n_epochs, n_iterations_hier,
ep_per_run_hier, ep_per_eval,
ep_per_run_low)
for _ in range(how_many))
J, L = parse_joblib(res)
np.save(subdir + '/J_H_' + alg_h.__name__ + '_' + alg_l.__name__, J)
np.save(subdir + '/L_H_' + alg_h.__name__ + '_' + alg_l.__name__, L)'''
# GHAVAMZADEH
params_high = {'learning_rate': Parameter(value=8e-2), 'lambda_coeff': 0.9}
agent_high = build_high_level_ghavamzadeh(QLambdaDiscrete, params_high,
mdp)
params_low = {'learning_rate': AdaptiveParameter(value=1e-2)}
agent_cross = build_low_level_ghavamzadeh(GPOMDP, params_low, mdp)
agent_plus = build_low_level_ghavamzadeh(GPOMDP, params_low, mdp)
print('ghavamzadeh')
res = Parallel(n_jobs=n_jobs)(delayed(ghavamzadeh_experiment)(
mdp, agent_plus, agent_cross, agent_high, n_epochs, ep_per_epoch,
ep_per_eval, ep_per_run_low_ghavamzadeh) for _ in range(how_many))
J, L = parse_joblib(res)
np.save(subdir + '/J_ghavamzadeh', J)
np.save(subdir + '/L_ghavamzadeh', L)
name='phi',
input_dim=mdp.info.observation_space.shape[0])
input_shape = (phi.size, )
approximator_params = dict(input_dim=phi.size)
approximator = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape,
params=approximator_params)
sigma = np.eye(2) * 1e-1
policy = MultivariateGaussianPolicy(mu=approximator, sigma=sigma)
# Agent
learning_rate = AdaptiveParameter(value=5)
algorithm_params = dict(learning_rate=learning_rate)
fit_params = dict()
agent_params = {'algorithm_params': algorithm_params, 'fit_params': fit_params}
agent = REINFORCE(policy, mdp.info, agent_params, phi)
# Train
core = Core(agent, mdp)
print 'Initial evaluation'
dataset_eval = core.evaluate(n_episodes=ep_per_run)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
print('J at start : ' + str(np.mean(J)))
for i in xrange(n_runs):
print 'iteration', i
print 'learn'
mk_dir_recursive('./' + subdir + str(i))
np.save(subdir + str(i) + '/low_level_dataset_file',
low_level_dataset_eval)
np.save(subdir + str(i) + '/parameter_dataset1_file', parameter_dataset1)
np.save(subdir + str(i) + '/parameter_dataset2_file', parameter_dataset2)
np.save(subdir + str(i) + '/dataset_eval_file', dataset_eval)
if __name__ == '__main__':
subdir = datetime.datetime.now().strftime(
'%Y-%m-%d_%H-%M-%S') + '_big_hierarchical/'
alg_high = GPOMDP
alg_low = PGPE
learning_rate_high = AdaptiveParameter(value=50)
learning_rate_low = AdaptiveParameter(value=5e-4)
n_jobs = -1
how_many = 1
n_runs = 25
n_iterations = 20
ep_per_run = 40
eval_run = 50
mk_dir_recursive('./' + subdir)
force_symlink('./' + subdir, 'latest')
params = {
'learning_rate_high': learning_rate_high,
'learning_rate_low': learning_rate_low
}
np.save(subdir + '/algorithm_params_dictionary', params)
print('ITERATION', n)
if n == 2:
control_block1.unset_mask()
core.learn(n_episodes=n_iterations*n_ep_per_fit, skip=True)
dataset_eval_run = core.evaluate(n_episodes=eval_run, render=False)
J = compute_J(dataset_eval_run, gamma=mdp.info.gamma)
print('J at iteration ' + str(n) + ': ' + str(np.mean(J)))
print('dist H:', dist1.get_parameters())
print('dist L mu:', dist2.get_parameters()[:3])
print('dist L sigma:', dist2.get_parameters()[3:])
if __name__ == '__main__':
learning_rate_high = Parameter(value=1e-5)
learning_rate_low = AdaptiveParameter(value=1e-1)
eps_high = 0.05
eps_low = 0.05
beta_high = 0.01
beta_low = 2e-3
algs_params = [
#(REPS, REPS, {'eps': eps_high}, {'eps': eps_low}),
(RWR, RWR, {'beta': beta_high}, {'beta': beta_low}),
#(PGPE, PGPE, {'learning_rate': learning_rate_high},
#{'learning_rate': learning_rate_low}),
#(PGPE, RWR, {'learning_rate' : learning_rate_high}, {'beta' :
# beta_low})
]
n_jobs = 1
def experiment():
np.random.seed()
# Model Block
mdp = ShipSteeringMultiGate()
#State Placeholder
state_ph = PlaceHolder(name='state_ph')
#Reward Placeholder
reward_ph = PlaceHolder(name='reward_ph')
# Function Block 1
function_block1 = fBlock(name='f1 (angle difference)', phi=phi)
# Function Block 2
function_block2 = squarednormBlock(name='f2 (squared norm)')
# Function Block 3
function_block3 = addBlock(name='f3 (summation)')
#Features
features = Features(basis_list=[PolynomialBasis()])
# Policy 1
sigma1 = np.array([38, 38])
approximator1 = Regressor(LinearApproximator,
input_shape=(features.size, ),
output_shape=(2, ))
approximator1.set_weights(np.array([75, 75]))
pi1 = DiagonalGaussianPolicy(mu=approximator1, sigma=sigma1)
# Policy 2
sigma2 = Parameter(value=.01)
approximator2 = Regressor(LinearApproximator,
input_shape=(1, ),
output_shape=mdp.info.action_space.shape)
pi2 = GaussianPolicy(mu=approximator2, sigma=sigma2)
# Agent 1
learning_rate = AdaptiveParameter(value=10)
algorithm_params = dict(learning_rate=learning_rate)
fit_params = dict()
agent_params = {
'algorithm_params': algorithm_params,
'fit_params': fit_params
}
mdp_info_agent1 = MDPInfo(observation_space=mdp.info.observation_space,
action_space=spaces.Box(0, 150, (2, )),
gamma=mdp.info.gamma,
horizon=50)
agent1 = GPOMDP(policy=pi1,
mdp_info=mdp_info_agent1,
params=agent_params,
features=features)
# Agent 2
learning_rate = AdaptiveParameter(value=.001)
algorithm_params = dict(learning_rate=learning_rate)
fit_params = dict()
agent_params = {
'algorithm_params': algorithm_params,
'fit_params': fit_params
}
mdp_info_agent2 = MDPInfo(observation_space=spaces.Box(
-np.pi, np.pi, (1, )),
action_space=mdp.info.action_space,
gamma=mdp.info.gamma,
horizon=100)
agent2 = GPOMDP(policy=pi2,
mdp_info=mdp_info_agent2,
params=agent_params,
features=None)
# Control Block 1
parameter_callback1 = CollectPolicyParameter(pi1)
control_block1 = ControlBlock(name='Control Block 1',
agent=agent1,
n_eps_per_fit=5,
callbacks=[parameter_callback1])
# Control Block 2
dataset_callback = CollectDataset()
parameter_callback2 = CollectPolicyParameter(pi2)
control_block2 = ControlBlock(
name='Control Block 2',
agent=agent2,
n_eps_per_fit=10,
callbacks=[dataset_callback, parameter_callback2])
#Reward Accumulator
reward_acc = reward_accumulator_block(gamma=mdp_info_agent1.gamma,
name='reward_acc')
# Algorithm
blocks = [
state_ph, reward_ph, control_block1, control_block2, function_block1,
function_block2, function_block3, reward_acc
]
#order = [0, 1, 7, 2, 4, 5, 6, 3]
state_ph.add_input(control_block2)
reward_ph.add_input(control_block2)
control_block1.add_input(state_ph)
reward_acc.add_input(reward_ph)
reward_acc.add_alarm_connection(control_block2)
control_block1.add_reward(reward_acc)
control_block1.add_alarm_connection(control_block2)
function_block1.add_input(control_block1)
function_block1.add_input(state_ph)
function_block2.add_input(function_block1)
function_block3.add_input(function_block2)
function_block3.add_input(reward_ph)
control_block2.add_input(function_block1)
control_block2.add_reward(function_block3)
computational_graph = ComputationalGraph(blocks=blocks, model=mdp)
core = HierarchicalCore(computational_graph)
# Train
#dataset_learn_visual = core.learn(n_episodes=2000)
dataset_learn_visual = list()
for n in range(4):
dataset_learn = core.learn(n_episodes=500)
last_ep_dataset = pick_last_ep(dataset_learn)
dataset_learn_visual += last_ep_dataset
del dataset_learn
# Evaluate
dataset_eval = core.evaluate(n_episodes=10)
# Visualize
low_level_dataset = dataset_callback.get()
parameter_dataset1 = parameter_callback1.get_values()
parameter_dataset2 = parameter_callback2.get_values()
visualize_policy_params(parameter_dataset1, parameter_dataset2)
visualize_control_block(low_level_dataset, ep_count=20)
visualize_ship_steering(dataset_learn_visual, name='learn', n_gates=4)
visualize_ship_steering(dataset_eval, 'evaluate', n_gates=4)
plt.show()
return
if __name__ == '__main__':
how_many = 100
n_jobs = -1
n_runs = 25
n_iterations = 10
ep_per_run = 20
algs_and_params = [
(REPS, {
'eps': 1.0
}),
(RWR, {
'beta': 0.7
}),
(PGPE, {
'learning_rate': AdaptiveParameter(value=1.5)
}),
]
base_dir = datetime.datetime.now().strftime(
'%Y-%m-%d_%H-%M-%S') + '_small_bbo/'
mk_dir_recursive('./' + base_dir)
force_symlink(base_dir, './latest')
for alg, params in algs_and_params:
subdir = base_dir + alg.__name__ + '/'
mk_dir_recursive('./' + subdir)
np.save(subdir + '/algorithm_params_dictionary', params)
experiment_params = {
'how_many': how_many,
def test_GPOMDP():
params = dict(learning_rate=AdaptiveParameter(value=.01))
policy = learn(GPOMDP, params)
w = np.array([-0.07623939, 2.05232858])
assert np.allclose(w, policy.get_weights())
print(alg.__name__)
res = Parallel(n_jobs=n_jobs)(delayed(flat_experiment)(mdp,
agent,
n_epochs,
n_iterations_bbo,
ep_per_run_bbo,
ep_per_eval)
for _ in range(how_many))
J, L = parse_joblib(res)
np.save(subdir + '/J_' + alg.__name__, J)
np.save(subdir + '/L_' + alg.__name__, L)'''
# HIERARCHICAL
algs_and_params_hier = [
(GPOMDP, {'learning_rate': AdaptiveParameter(value=10)},
PGPE, {'learning_rate': AdaptiveParameter(value=5e-4)})
]
mu = np.array([75, 75])
sigma = np.array([40, 40])
for alg_h, params_h, alg_l, params_l in algs_and_params_hier:
agent_h = build_high_level_agent(alg_h, params_h, mdp, mu, sigma)
agent_l = build_low_level_agent(alg_l, params_l, mdp)
ep_per_run_hier = ep_per_epoch // n_iterations_hier
print('High: ', alg_h.__name__, ' Low: ', alg_l.__name__)
res = Parallel(n_jobs=n_jobs)(delayed(hierarchical_experiment)
(mdp, agent_l, agent_h,
def test_REINFORCE():
params = dict(learning_rate=AdaptiveParameter(value=.01))
policy = learn(REINFORCE, params)
w = np.array([-0.0084793, 2.00536528])
assert np.allclose(w, policy.get_weights())
# MDP
mdp = ShipSteeringMultiGate(n_steps_action=3, small=True)
# directory
name = 'multigate_ship_steering'
subdir = name + '_' + datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\
+ '/'
mk_dir_recursive('./' + subdir)
force_symlink('./' + subdir, name + '_latest')
# Hierarchical
algs_and_params_hier = [
(GPOMDP, {'learning_rate': Parameter(value=1e-5)},
PGPE, {'learning_rate': AdaptiveParameter(value=5e-4)})
]
for alg_h, params_h, alg_l, params_l in algs_and_params_hier:
mu = 0
sigma = 0.15
agent_h = build_high_level_agent(alg_h, params_h, mdp, mu, sigma)
agent_l = build_low_level_agent(alg_l, params_l, mdp)
ep_per_run_hier = ep_per_epoch_train // n_iterations
print('High: ', alg_h.__name__, ' Low: ', alg_l.__name__)
mdp = ShipSteeringMultiGate(n_steps_action=3, small=True)
# directory
name = 'multigate_ship_steering'
subdir = name + '_' + datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\
+ '/'
mk_dir_recursive('./' + subdir)
force_symlink('./' + subdir, name + '_latest')
# Hierarchical
algs_and_params_hier = [(QLearning, {
'learning_rate': Parameter(value=0.6)
}, GPOMDP, {
'learning_rate':
AdaptiveParameter(value=25) if mdp.small else AdaptiveParameter(
value=50)
}, PGPE, {
'learning_rate': AdaptiveParameter(value=5e-4)
})]
for alg_h, params_h, alg_m, params_m, alg_l, params_l in algs_and_params_hier:
epsilon = Parameter(value=1)
agent_h = build_high_level_agent(alg_h, params_h, mdp, epsilon)
mu = 250 if mdp.small else 500
sigma = 125 if mdp.small else 250
agent_m1 = build_mid_level_agent(alg_m, params_m, mdp, mu, sigma)
agent_m2 = build_mid_level_agent(alg_m, params_m, mdp, mu, sigma)
agent_m3 = build_mid_level_agent(alg_m, params_m, mdp, mu, sigma)
def test_eNAC():
params = dict(learning_rate=AdaptiveParameter(value=.01))
policy = learn(eNAC, params)
w = np.array([-0.03668018, 2.05112355])
assert np.allclose(w, policy.get_weights())
