def __init__(self, input_pars):
self.input_pars = input_pars
self.N_MC_eval_samples = 250
self.episode_length = 500
self.data_columns = (
"x",
"theta",
"xdot",
"thetadot",
"u",
"r",
) # assume the 2nd to last is u and the last is r
self.n_dim = 4
self.bounds = np.array(
[[-XBOUND, XBOUND], [-THETABOUND, THETABOUND], [-XDOTBOUND, XDOTBOUND], [-THETADOTBOUND, THETADOTBOUND]]
).transpose()
self.goal = np.array(
[[-np.inf, np.inf], [-THETABOUND, THETABOUND], [-np.inf, np.inf], [-np.inf, np.inf]]
).transpose()
self.initstate = INITSTATE
self.action_centers = np.array([-5, 5])
self.n_x_centers = 1
self.n_theta_centers = 50
self.n_xdot_centers = 30
self.n_thetadot_centers = 30
self.true_pars = (1, 1, 1)
self.optimization_pars = {
"initial step size": np.array([5, 5, 5]),
"start": np.array([1, 10, 1]),
"maximum evaluations": 50,
"only positive": True,
}
# self.initial_par_search_space = [[p1, p2] for p1 in np.linspace(-0.003, -.002, 5) for p2 in np.linspace(2, 4, 5)] # TODO
self.noise = input_pars
self.value_iteration_threshold = 1e-5
self.state_centers = self.construct_discrete_policy_centers()
self.dim_centers = rl_tools.split_states_on_dim(self.state_centers)
self.pi_init = 1 - np.int8((np.sign(self.state_centers[:, 1]) + 1) / 2)
self.training_data_random_start = rnd_start
def __init__(self, input_pars):
self.input_pars = input_pars
self.N_MC_eval_samples = 250
self.episode_length = 500
self.data_columns = ('x','xdot','u','r') # assume the 2nd to last is u and the last is r
self.n_dim = 2
self.bounds = np.array([[XMIN, XMAX],[XDOTMIN, XDOTMAX]]).transpose()
self.goal = np.array([[-np.inf, XMAX],[-np.inf, np.inf]]).transpose()
self.initstate = INITSTATE
self.action_centers = np.array([-1, 1])
self.n_x_centers = 150
self.n_xdot_centers = 150
self.true_pars = (-0.0025, 3)
self.initial_par_search_space = [[p1, p2] for p1 in np.linspace(-0.003, -.002, 5) for p2 in np.linspace(2, 4, 5)]
self.noise = input_pars
self.value_iteration_threshold = 1e-5
self.optimization_pars = {'initial step size':np.array([.0024, 1]),
'start':np.array([-0.0025, 3]),
'maximum evaluations':75,
'only positive':False}
self.state_centers = self.construct_discrete_policy_centers()
self.dim_centers = rl_tools.split_states_on_dim(self.state_centers)
self.pi_init = None
self.training_data_random_start = rnd_start
def __init__(self, state_centers, action_centers, states_to_actions):
self.state_centers = state_centers
self.action_centers = action_centers
self.states_to_actions = states_to_actions
self.dim_centers = rl_tools.split_states_on_dim(state_centers)
