def experiment(n_epochs, n_episodes):
np.random.seed()
logger = Logger(COPDAC_Q.__name__, results_dir=None)
logger.strong_line()
logger.info('Experiment Algorithm: ' + COPDAC_Q.__name__)
# MDP
n_steps = 5000
mdp = InvertedPendulum(horizon=n_steps)
# Agent
n_tilings = 10
alpha_theta = Parameter(5e-3 / n_tilings)
alpha_omega = Parameter(0.5 / n_tilings)
alpha_v = Parameter(0.5 / n_tilings)
tilings = Tiles.generate(n_tilings, [10, 10],
mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
phi = Features(tilings=tilings)
input_shape = (phi.size, )
mu = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
sigma = 1e-1 * np.eye(1)
policy = GaussianPolicy(mu, sigma)
agent = COPDAC_Q(mdp.info,
policy,
mu,
alpha_theta,
alpha_omega,
alpha_v,
value_function_features=phi,
policy_features=phi)
# Train
dataset_callback = CollectDataset()
visualization_callback = Display(agent._V, mu,
mdp.info.observation_space.low,
mdp.info.observation_space.high, phi, phi)
core = Core(agent, mdp, callbacks_fit=[dataset_callback])
for i in trange(n_epochs, leave=False):
core.learn(n_episodes=n_episodes, n_steps_per_fit=1, render=False)
J = compute_J(dataset_callback.get(), gamma=1.0)
dataset_callback.clean()
visualization_callback()
logger.epoch_info(i + 1, R_mean=np.sum(J) / n_steps / n_episodes)
logger.info('Press a button to visualize the pendulum...')
input()
sigma = 1e-8 * np.eye(1)
policy.set_sigma(sigma)
core.evaluate(n_steps=n_steps, render=True)
def test_copdac_q():
n_steps = 50
mdp = InvertedPendulum(horizon=n_steps)
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
# Agent
n_tilings = 1
alpha_theta = Parameter(5e-3 / n_tilings)
alpha_omega = Parameter(0.5 / n_tilings)
alpha_v = Parameter(0.5 / n_tilings)
tilings = Tiles.generate(n_tilings, [2, 2], mdp.info.observation_space.low,
mdp.info.observation_space.high + 1e-3)
phi = Features(tilings=tilings)
input_shape = (phi.size, )
mu = Regressor(LinearApproximator,
input_shape=input_shape,
output_shape=mdp.info.action_space.shape)
sigma = 1e-1 * np.eye(1)
policy = GaussianPolicy(mu, sigma)
agent = COPDAC_Q(mdp.info,
policy,
mu,
alpha_theta,
alpha_omega,
alpha_v,
value_function_features=phi,
policy_features=phi)
# Train
core = Core(agent, mdp)
core.learn(n_episodes=2, n_episodes_per_fit=1)
w = agent.policy.get_weights()
w_test = np.array([0, -6.62180045e-7, 0, -4.23972882e-2])
assert np.allclose(w, w_test)