def run_experiment(args):
grid_n = int(np.sqrt(args.n))
P = P_matrices.constant_gridworld(grid_n, 0.3, 0.1, 0.1, 0.3, 0.2)
R_mat = np.zeros_like(P)
R_mat[:, -1] = 1
env = environment.MRP(args.gamma, P, R_mat)
# build features
Phi = np.stack([np.array(sum([[i]*grid_n for i in range(grid_n)], [])), np.array(list(range(grid_n))*grid_n)], axis = 1)
Phi = np.concatenate([Phi, np.ones((args.n,1))], axis = 1)
np.random.seed(args.seed)
V = mlp.MLP(Phi, [args.width]*args.depth, biases = False, activation='ReLU')
if args.online:
Vs, thetas, _, _ = online_td0.TD0(V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
else:
thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
mlp_V_to_V_star = utils.dist_mu(env.mu, env.V_star, jnp.array(Vs))
mlp_V_to_origin = utils.mu_norm(env.mu, jnp.array(Vs))
ts = thetas[args.plot_start::args.plot_step]
condition_numbers = []
for i in range(len(ts)):
V.theta = ts[i]
condition_numbers.append(utils.jac_cond(V.jacobian()))
bound = utils.overparam_cond_number_bound(env.P, env.mu, env.gamma, args.k)
smoothness = max([abs(env.V_star[i] - env.V_star[i-1]) for i in range(args.n)])
return mlp_V_to_V_star, mlp_V_to_origin, condition_numbers, bound, smoothness
def run_experiment(args):
P = P_matrices.build_cyclic_P(args.n, args.delta)
R_mat = np.zeros_like(P)
R_mat[0, 1] = 1
env = environment.MRP(args.gamma, P, R_mat)
# V_star = np.zeros(args.n)
# for i in range(0, args.n, 2):
# V_star[i] = 1
# env = environment.MRP(args.gamma, P, V_star=V_star)
# build features
angles = np.linspace(0, 2 * np.pi, args.n, endpoint=False)
Phi = np.concatenate([
np.expand_dims(np.sin(angles), 1),
np.expand_dims(np.cos(angles), 1),
np.ones((args.n, 1))
],
axis=1)
np.random.seed(args.seed)
V = mlp.MLP(Phi, [args.width] * args.depth, biases=False)
Vs, thetas, _, _ = online_td0.TD0(V, env, args.stepsize, args.steps,
args.log_idx, args.plot_step)
online_mlp_V_to_V_star = utils.dist_mu(env.mu, env.V_star, jnp.array(Vs))
online_mlp_V_to_origin = utils.mu_norm(env.mu, jnp.array(Vs))
ts = thetas[args.plot_start::args.plot_step]
online_condition_numbers = []
for i in range(len(ts)):
V.theta = ts[i]
online_condition_numbers.append(utils.jac_cond(V.jacobian()))
np.random.seed(args.seed)
V = mlp.MLP(Phi, [args.width] * args.depth, biases=False)
thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize, args.steps,
args.log_idx, args.plot_step)
mlp_V_to_V_star = utils.dist_mu(env.mu, env.V_star, jnp.array(Vs))
mlp_V_to_origin = utils.mu_norm(env.mu, jnp.array(Vs))
ts = thetas[args.plot_start::args.plot_step]
condition_numbers = []
for i in range(len(ts)):
V.theta = ts[i]
condition_numbers.append(utils.jac_cond(V.jacobian()))
# bound = utils.overparam_cond_number_bound(env.P, env.mu, env.gamma, args.k)
# smoothness = max([abs(env.V_star[i] - env.V_star[i-1]) for i in range(args.n)])
return online_mlp_V_to_V_star, online_mlp_V_to_origin, mlp_V_to_V_star, mlp_V_to_origin, condition_numbers, online_condition_numbers
def run_experiment(args):
P = P_matrices.build_cyclic_P(args.n, args.delta)
R_mat = np.zeros_like(P)
R_mat[0, 1] = 1
env = environment.MRP(args.gamma, P, R_mat)
# build features
angles = np.linspace(0, 2 * np.pi, args.n, endpoint=False)
Phi = np.concatenate([np.expand_dims(np.sin(angles), 1), np.expand_dims(np.cos(angles), 1), np.ones((args.n,1))], axis = 1)
# np.random.seed(args.seed)
# V = mlp.MLP(Phi, [args.width]*args.depth, biases = False, activation='tanh')
# if args.online:
# Vs, thetas, _, _ = online_td0.TD0(V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
# else:
# thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
# tanh_mlp_V_to_V_star = utils.dist_mu(env.mu, env.V_star, jnp.array(Vs))
# ts = thetas[args.plot_start::args.plot_step]
# tanh_dynamics = []
# for i in range(len(ts)):
# V.theta = ts[i]
# tanh_dynamics.append(utils.dynamics_norm(V, env.A, env.V_star))
# tanh_params = []
# for i in range(len(ts)):
# theta = np.concatenate([x.flatten() for x in ts[i]]).ravel()
# tanh_params.append(np.linalg.norm(theta))
tanh_params, tanh_dynamics, tanh_mlp_V_to_V_star = [],[],[]
np.random.seed(args.seed)
V = mlp.MLP(Phi, [args.width]*args.depth, biases = False, activation='ReLU')
if args.online:
Vs, thetas, _, _ = online_td0.TD0(V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
else:
thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
mlp_V_to_V_star = utils.dist_mu(env.mu, env.V_star, jnp.array(Vs))
ts = thetas[args.plot_start::args.plot_step]
dynamics = []
for i in range(len(ts)):
V.theta = ts[i]
dynamics.append(utils.norm_dynamics(V, env.A, env.V_star))
params = []
for i in range(len(ts)):
theta = np.concatenate([x.flatten() for x in ts[i]]).ravel()
params.append(np.linalg.norm(theta))
bound = utils.overparam_cond_number_bound(env.P, env.mu, env.gamma, args.k)
return tanh_mlp_V_to_V_star, tanh_dynamics, tanh_params, mlp_V_to_V_star, dynamics, params, bound
def run_experiment(args):
np.random.seed(args.seed)
P = build_P(args.n, args.delta)
# R_mat = np.zeros_like(P)
# R_mat[1, 0] = 1
# env = environment.MRP(args.gamma, P, R_mat)
V_star = np.zeros(args.n)
for i in range(0, args.n, 2):
V_star[i] = 1
env = environment.MRP(args.gamma, P, V_star=V_star)
bound = utils.overparam_cond_number_bound(env.P, env.mu, env.gamma, args.k)
angles = np.linspace(0, 2 * np.pi, args.n, endpoint=False)
Phi = np.concatenate([
np.expand_dims(np.sin(angles), 1),
np.expand_dims(np.cos(angles), 1),
np.ones((args.n, 1))
],
axis=1)
V = simple.Tabular(np.zeros(args.n))
thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize, args.steps,
args.log_idx)
tabular_Vs = utils.dist_mu(env,
np.array(Vs)[args.plot_start::args.plot_step])
V = mlp.MLP(Phi, [args.width] * args.depth, biases=False)
thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize, args.steps,
args.log_idx)
mlp_Vs = utils.dist_mu(env, np.array(Vs)[args.plot_start::args.plot_step])
ts = thetas[args.plot_start::args.plot_step]
condition_numbers = []
for i in range(len(ts)):
V.theta = ts[i]
condition_numbers.append(utils.jac_cond(V.jacobian()))
V = simple.Linear(np.zeros(Phi.shape[1]), Phi)
thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize, args.steps,
args.log_idx)
linear_Vs = utils.dist_mu(env,
np.array(Vs)[args.plot_start::args.plot_step])
smoothness = max(
[abs(env.V_star[i] - env.V_star[i - 1]) for i in range(args.n)])
return tabular_Vs, linear_Vs, mlp_Vs, condition_numbers, bound, smoothness
def run_experiment(args):
np.random.seed(args.seed)
P = P_matrices.build_cyclic_P(args.n, args.delta)
R_mat = np.zeros_like(P)
env = environment.MRP(args.gamma, P, R_mat)
bound = utils.overparam_cond_number_bound(env.P, env.mu, env.gamma, args.k)
orientation = -1 * np.ones(args.n)
orientation[-1] = args.n - 1
orientation = 10.0 * orientation
init_conditions = -20.0
print(orientation)
epsilon = 0.05
P_spir = P_matrices.build_cyclic_P(args.n, 0.5)
V = spiral.Spiral(init_conditions, P_spir, orientation, epsilon)
if args.online:
Vs, thetas, _, _ = online_td0.TD0(V, env, args.stepsize, args.steps,
args.log_idx, args.plot_step)
else:
thetas, Vs = numpy_expected_tdk.TDk(args.k, V, env, args.stepsize,
args.steps, args.log_idx,
args.plot_step)
spiral_Vs = utils.dist_mu(env.mu, env.V_star, np.array(Vs))
ts = thetas[args.plot_start::args.plot_step]
condition_numbers = []
for i in range(len(ts)):
V.theta = ts[i]
condition_numbers.append(utils.jac_cond(V.jacobian()))
V = simple.Tabular(Vs[0])
if args.online:
Vs, thetas, _, _ = online_td0.TD0(V, env, args.stepsize, args.steps,
args.log_idx, args.plot_step)
else:
thetas, Vs = numpy_expected_tdk.TDk(args.k, V, env, args.stepsize,
args.steps, args.log_idx,
args.plot_step)
tabular_Vs = utils.dist_mu(env.mu, env.V_star, np.array(Vs))
smoothness = max(
[abs(env.V_star[i] - env.V_star[i - 1]) for i in range(args.n)])
return tabular_Vs, spiral_Vs, condition_numbers, bound, smoothness
def run_experiment(args):
np.random.seed(args.seed)
P = P_matrices.build_cyclic_P(args.n, args.delta)
if args.hard:
V_star = np.zeros(args.n)
for i in range(0, args.n, 2):
V_star[i] = 1
env = environment.MRP(args.gamma, P, V_star=V_star)
else:
R_mat = np.zeros_like(P)
R_mat[0, 1] = 1
# env = environment.MRP(args.gamma, P, R_mat=R_mat)
#fixed V_star
PV_star = P_matrices.build_cyclic_P(args.n, 0.5)
V_star = environment.MRP(args.gamma, PV_star, R_mat).V_star
env = environment.MRP(args.gamma, P, V_star=V_star)
bound = utils.overparam_cond_number_bound(env.P, env.mu, env.gamma, args.k)
angles = np.linspace(0, 2 * np.pi, args.n, endpoint=False)
Phi = np.concatenate([
np.expand_dims(np.sin(angles), 1),
np.expand_dims(np.cos(angles), 1),
np.ones((args.n, 1))
],
axis=1)
# V = simple.Tabular(np.zeros(args.n))
# if args.online:
# Vs, thetas, _, _ = online_td0.TD0(V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
# else:
# thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
# tabular_Vs = utils.dist_mu(env.mu, env.V_star, np.array(Vs))
tabular_Vs = []
V = mlp.MLP(Phi, [args.width] * args.depth, biases=False)
if args.online:
Vs, thetas, _, _ = online_td0.TD0(V, env, args.stepsize, args.steps,
args.log_idx, args.plot_step)
else:
thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize,
args.steps, args.log_idx, args.plot_step)
mlp_Vs = utils.dist_mu(env.mu, env.V_star, np.array(Vs))
ts = thetas[args.plot_start::args.plot_step]
condition_numbers = []
for i in range(len(ts)):
V.theta = ts[i]
condition_numbers.append(utils.jac_cond(V.jacobian()))
# V = simple.Linear(np.zeros(Phi.shape[1]), Phi)
# if args.online:
# Vs, thetas, _, _ = online_td0.TD0(V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
# else:
# thetas, Vs = expected_tdk.TDk(args.k, V, env, args.stepsize, args.steps, args.log_idx, args.plot_step)
# linear_Vs = utils.dist_mu(env.mu, env.V_star, np.array(Vs))
linear_Vs = []
smoothness = max(
[abs(env.V_star[i] - env.V_star[i - 1]) for i in range(args.n)])
return tabular_Vs, linear_Vs, mlp_Vs, condition_numbers, bound, smoothness
from td.funcs import spiral, simple, two_layers from td.envs import environment from td.algs import expected_td0, online_td0 import numpy as np from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D P = np.array([[0.5, 0, 0.5], [0.5, 0.5, 0], [0, 0.5, 0.5]]) gamma = 0.9 R_mat = np.zeros_like(P) env = environment.MRP(gamma, P, R_mat) # Spiral V = spiral.Spiral(-7.0, P, np.array([-10, -10, 20]), 0.05) thetas, Vs = expected_td0.TD0(V, env, 0.00001, 25000, 10000) Vs = np.array(Vs) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.plot(Vs[:, 0], Vs[:, 1], Vs[:, 2]) #Tabular expected V = simple.Tabular(Vs[0, :]) thetas, Vs = expected_td0.TD0(V, env, 0.1, 1000, 1000) Vs = np.array(Vs)