'min_samples_split': 5,
'min_samples_leaf': 2,
'input_scaled': False,
'output_scaled': False}
discrete_actions = mdp.action_space.values
# ExtraTrees
regressor = Regressor(ExtraTreesRegressor, **regressor_params)
# Action regressor of Ensemble of ExtraTreesEnsemble
# regressor = Ensemble(ExtraTreesRegressor, **regressor_params)
regressor = ActionRegressor(regressor, discrete_actions=discrete_actions,
tol=5, **regressor_params)
dataset = evaluation.collect_episodes(mdp, n_episodes=1000)
check_dataset(dataset, state_dim, action_dim, reward_dim) # this is just a
# check, it can be removed in experiments
print('Dataset has %d samples' % dataset.shape[0])
# reward_idx = state_dim + action_dim
# sast = np.append(dataset[:, :reward_idx],
# dataset[:, reward_idx + reward_dim:-1],
# axis=1)
# r = dataset[:, reward_idx]
sast, r = split_data_for_fqi(dataset, state_dim, action_dim, reward_dim)
fqi_iterations = mdp.horizon # this is usually less than the horizon
fqi = FQI(estimator=regressor,
state_dim=state_dim,
action_dim=action_dim,
discrete_actions=discrete_actions,
from ifqi.models.regressor import Regressor
from ifqi.models.mlp import MLP
from ifqi.models.linear import Ridge
from ifqi.algorithms.pbo.pbo import PBO
"""
Simple script to quickly run pbo. It solves the LQG environment.
"""
mdp = envs.LQG1D()
state_dim, action_dim, reward_dim = envs.get_space_info(mdp)
reward_idx = state_dim + action_dim
discrete_actions = np.linspace(-8, 8, 20)
dataset = evaluation.collect_episodes(mdp, n_episodes=100)
check_dataset(dataset, state_dim, action_dim, reward_dim)
sast, r = split_data_for_fqi(dataset, state_dim, action_dim, reward_dim)
### Q REGRESSOR ##########################
class LQG_Q():
def __init__(self):
self.w = np.array([1., 0.])
def predict(self, sa):
k, b = self.w
#print(k,b)
return - b * b * sa[:, 0] * sa[:, 1] - 0.5 * k * sa[:, 1] ** 2 - 0.4 * k * sa[:, 0] ** 2
def get_weights(self):
return self.w
dest="INDEPENDENT", default=False,
help="Independent.")
op.add_option("--activ", default="tanh",
dest="ACTIVATION", type="str",
help="NN activation")
(opts, args) = op.parse_args()
# np.random.seed(6652)
mdp = envs.LQG1D()
# mdp.seed(2897270658018522815)
state_dim, action_dim, reward_dim = envs.get_space_info(mdp)
reward_idx = state_dim + action_dim
discrete_actions = np.linspace(-8, 8, 20)
dataset = evaluation.collect_episodes(mdp, n_episodes=100)
check_dataset(dataset, state_dim, action_dim, reward_dim)
INCREMENTAL = opts.INCREMENTAL
ACTIVATION = opts.ACTIVATION
STEPS_AHEAD = opts.STEPS_HEAD
UPDATE_EVERY = opts.UPDATE_EVERY
INDEPENDENT = opts.INDEPENDENT
EPOCH = opts.EPOCH
NORM_VALUE = np.inf
print('INCREMENTAL: {}'.format(INCREMENTAL))
print('ACTIVATION: {}'.format(ACTIVATION))
print('STEPS_AHEAD: {}'.format(STEPS_AHEAD))
print('UPDATE_EVERY: {}'.format(UPDATE_EVERY))
print('INDEPENDENT: {}'.format(INDEPENDENT))
print('NORM_VALUE: {}'.format(NORM_VALUE))
'output_scaled': False
}
discrete_actions = mdp.action_space.values
# ExtraTrees
regressor = Regressor(ExtraTreesRegressor, **regressor_params)
# Action regressor of Ensemble of ExtraTreesEnsemble
# regressor = Ensemble(ExtraTreesRegressor, **regressor_params)
regressor = ActionRegressor(regressor,
discrete_actions=discrete_actions,
tol=5,
**regressor_params)
dataset = evaluation.collect_episodes(mdp, n_episodes=1000)
check_dataset(dataset, state_dim, action_dim, reward_dim) # this is just a
# check, it can be removed in experiments
print('Dataset has %d samples' % dataset.shape[0])
# reward_idx = state_dim + action_dim
# sast = np.append(dataset[:, :reward_idx],
# dataset[:, reward_idx + reward_dim:-1],
# axis=1)
# r = dataset[:, reward_idx]
sast, r = split_data_for_fqi(dataset, state_dim, action_dim, reward_dim)
fqi_iterations = mdp.horizon # this is usually less than the horizon
fqi = FQI(estimator=regressor,
state_dim=state_dim,
action_dim=action_dim,
discrete_actions=discrete_actions,
