def test_deterministic_policy():
np.random.seed(88)
n_dims = 5
approximator = Regressor(LinearApproximator,
input_shape=(n_dims,),
output_shape=(2,))
pi = DeterministicPolicy(approximator)
w_new = np.random.rand(pi.weights_size)
w_old = pi.get_weights()
pi.set_weights(w_new)
assert np.array_equal(w_new, approximator.get_weights())
assert not np.array_equal(w_old, w_new)
assert np.array_equal(w_new, pi.get_weights())
s_test_1 = np.random.randn(5)
s_test_2 = np.random.randn(5)
a_test = approximator.predict(s_test_1)
assert pi.get_regressor() == approximator
assert pi(s_test_1, a_test) == 1
assert pi(s_test_2, a_test) == 0
a_stored = np.array([-1.86941072, -0.1789696])
assert np.allclose(pi.draw_action(s_test_1), a_stored)
def experiment(alg, params, n_epochs, fit_per_run, ep_per_run):
np.random.seed()
# MDP
mdp = LQR.generate(dimensions=1)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
policy = DeterministicPolicy(mu=approximator)
mu = np.zeros(policy.weights_size)
sigma = 1e-3 * np.eye(policy.weights_size)
distribution = GaussianCholeskyDistribution(mu, sigma)
# Agent
agent = alg(mdp.info, distribution, policy, **params)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
print('distribution parameters: ', distribution.get_parameters())
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=fit_per_run * ep_per_run,
n_episodes_per_fit=ep_per_run)
dataset_eval = core.evaluate(n_episodes=ep_per_run)
print('distribution parameters: ', distribution.get_parameters())
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
print('J at iteration ' + str(i) + ': ' + str(np.mean(J)))
def experiment(alg, params, n_epochs, fit_per_epoch, ep_per_fit):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
mdp = ShipSteering()
# Policy
high = [150, 150, np.pi]
low = [0, 0, -np.pi]
n_tiles = [5, 5, 6]
low = np.array(low, dtype=np.float)
high = np.array(high, dtype=np.float)
n_tilings = 1
tilings = Tiles.generate(n_tilings=n_tilings,
n_tiles=n_tiles,
low=low,
high=high)
phi = Features(tilings=tilings)
input_shape = (phi.size, )
approximator = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
policy = DeterministicPolicy(approximator)
mu = np.zeros(policy.weights_size)
sigma = 4e-1 * np.ones(policy.weights_size)
distribution = GaussianDiagonalDistribution(mu, sigma)
# Agent
agent = alg(mdp.info, distribution, policy, features=phi, **params)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=ep_per_fit)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(0, J=np.mean(J))
for i in range(n_epochs):
core.learn(n_episodes=fit_per_epoch * ep_per_fit,
n_episodes_per_fit=ep_per_fit)
dataset_eval = core.evaluate(n_episodes=ep_per_fit)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(i + 1, J=np.mean(J))
def experiment(alg, params, n_epochs, n_iterations, ep_per_run):
np.random.seed()
# MDP
mdp = ShipSteering()
# Policy
high = [150, 150, np.pi]
low = [0, 0, -np.pi]
n_tiles = [5, 5, 6]
low = np.array(low, dtype=np.float)
high = np.array(high, dtype=np.float)
n_tilings = 1
tilings = Tiles.generate(n_tilings=n_tilings,
n_tiles=n_tiles,
low=low,
high=high)
phi = Features(tilings=tilings)
input_shape = (phi.size, )
approximator = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
policy = DeterministicPolicy(approximator)
mu = np.zeros(policy.weights_size)
sigma = 4e-1 * np.ones(policy.weights_size)
distribution = GaussianDiagonalDistribution(mu, sigma)
# Agent
agent = alg(mdp.info, distribution, policy, features=phi, **params)
# Train
print(alg.__name__)
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 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)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
print('J at iteration ' + str(i) + ': ' + str(np.mean(J)))
def experiment(alg, params, n_epochs, n_episodes, n_ep_per_fit):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
mdp = Segway()
# Policy
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
n_weights = approximator.weights_size
mu = np.zeros(n_weights)
sigma = 2e-0 * np.ones(n_weights)
policy = DeterministicPolicy(approximator)
dist = GaussianDiagonalDistribution(mu, sigma)
agent = alg(mdp.info, dist, policy, **params)
# Train
dataset_callback = CollectDataset()
core = Core(agent, mdp, callbacks_fit=[dataset_callback])
for i in trange(n_epochs, leave=False):
core.learn(n_episodes=n_episodes,
n_episodes_per_fit=n_ep_per_fit,
render=False)
J = compute_J(dataset_callback.get(), gamma=mdp.info.gamma)
dataset_callback.clean()
p = dist.get_parameters()
logger.epoch_info(i + 1,
J=np.mean(J),
mu=p[:n_weights],
sigma=p[n_weights:])
logger.info('Press a button to visualize the segway...')
input()
core.evaluate(n_episodes=3, render=True)
def experiment(alg, params, n_epochs, n_episodes, n_ep_per_fit):
np.random.seed()
# MDP
mdp = Segway()
# Policy
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
n_weights = approximator.weights_size
mu = np.zeros(n_weights)
sigma = 2e-0 * np.ones(n_weights)
policy = DeterministicPolicy(approximator)
dist = GaussianDiagonalDistribution(mu, sigma)
agent = alg(mdp.info, dist, policy, **params)
# Train
print(alg.__name__)
dataset_callback = CollectDataset()
core = Core(agent, mdp, callbacks=[dataset_callback])
for i in range(n_epochs):
core.learn(n_episodes=n_episodes,
n_episodes_per_fit=n_ep_per_fit,
render=False)
J = compute_J(dataset_callback.get(), gamma=mdp.info.gamma)
dataset_callback.clean()
p = dist.get_parameters()
print('mu: ', p[:n_weights])
print('sigma: ', p[n_weights:])
print('Reward at iteration ' + str(i) + ': ' + str(np.mean(J)))
print('Press a button to visualize the segway...')
input()
core.evaluate(n_episodes=3, render=True)
def experiment(alg, params, n_epochs, fit_per_epoch, ep_per_fit):
np.random.seed()
logger = Logger(alg.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + alg.__name__)
# MDP
mdp = LQR.generate(dimensions=1)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
policy = DeterministicPolicy(mu=approximator)
mu = np.zeros(policy.weights_size)
sigma = 1e-3 * np.eye(policy.weights_size)
distribution = GaussianCholeskyDistribution(mu, sigma)
# Agent
agent = alg(mdp.info, distribution, policy, **params)
# Train
core = Core(agent, mdp)
dataset_eval = core.evaluate(n_episodes=ep_per_fit)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(0,
J=np.mean(J),
distribution_parameters=distribution.get_parameters())
for i in trange(n_epochs, leave=False):
core.learn(n_episodes=fit_per_epoch * ep_per_fit,
n_episodes_per_fit=ep_per_fit)
dataset_eval = core.evaluate(n_episodes=ep_per_fit)
J = compute_J(dataset_eval, gamma=mdp.info.gamma)
logger.epoch_info(
i + 1,
J=np.mean(J),
distribution_parameters=distribution.get_parameters())
def learn(alg, **alg_params):
np.random.seed(1)
torch.manual_seed(1)
# MDP
mdp = LQR.generate(dimensions=2)
approximator = Regressor(LinearApproximator,
input_shape=mdp.info.observation_space.shape,
output_shape=mdp.info.action_space.shape)
policy = DeterministicPolicy(mu=approximator)
mu = np.zeros(policy.weights_size)
sigma = 1e-3 * np.ones(policy.weights_size)
distribution = GaussianDiagonalDistribution(mu, sigma)
agent = alg(mdp.info, distribution, policy, **alg_params)
core = Core(agent, mdp)
core.learn(n_episodes=5, n_episodes_per_fit=5)
return agent
