def wikenheiser2015_lookahead(goal_no,
start,
alpha,
tau,
goal_weight=20.,
goal_absorb=True,
backmove=False,
opt_policy_weight=0.9,
sigma=0.5,
n_samp=n_samp,
n_step=n_step):
ENVlh = CircularTrack(n_state=n_state_circ,
start=start,
goal_no=goal_no,
goal_weight=goal_weight,
goal_absorb=goal_absorb,
backmove=backmove,
opt_policy_weight=opt_policy_weight)
GENlh = Generator(ENV=ENVlh, symmetrize=symmetrize, jump_rate=jump_rate)
PROPlh = Propagator(GEN=GENlh, sigma=sigma, tau=tau, alpha=alpha)
EXPlh = Explorer(PROP=PROPlh, rho_init=start, no_dwell=no_dwell)
EXPlh.sample_sequences(n_samp=n_samp, n_step=n_step)
EXPlh.traj_width = 0
EXPlh.start_pos = True
EXPlh.state_msize = state_msize
return EXPlh, PROPlh, GENlh, ENVlh
def circulartrack_lookahead(start,
alpha,
tau,
sigma=1,
n_samp=n_samp,
n_step=n_step):
ENVlh = CircularTrack(n_state=n_state_circ, start=start, goal_no=None)
GENlh = Generator(ENV=ENVlh, symmetrize=symmetrize, jump_rate=jump_rate)
PROPlh = Propagator(GEN=GENlh, sigma=sigma, tau=tau, alpha=alpha)
EXPlh = Explorer(PROP=PROPlh, rho_init=start, no_dwell=no_dwell)
EXPlh.sample_sequences(n_samp=n_samp, n_step=n_step)
EXPlh.traj_width = 0
EXPlh.start_pos = True
EXPlh.state_msize = state_msize
return EXPlh, PROPlh, GENlh, ENVlh
ENV.start = start
GEN = Generator(ENV=ENV, symmetrize=symmetrize, jump_rate=jump_rate)
# generate trajectories under diffusion (alpha) and turbulence (+ spectral noise)
PROPt = Propagator(GEN=GEN,
sigma=sigma,
tau=tau,
alpha=alpha_turb,
spec_noise=spec_noise)
PROPd = Propagator(GEN=GEN,
sigma=sigma,
tau=tau,
alpha=alpha_diff,
spec_noise=0.)
PROPd.plot_prop_kernels(n=6)
EXPt = Explorer(PROP=PROPt, rho_init=ENV.start, mass=mass, no_dwell=no_dwell)
EXPd = Explorer(PROP=PROPd, rho_init=ENV.start, mass=mass, no_dwell=no_dwell)
EXPt.set_viz_scheme(**kwargs)
EXPd.set_viz_scheme(**kwargs)
EXPt.sample_sequences(n_samp=n_samp, n_step=n_step)
EXPd.sample_sequences(n_samp=n_samp, n_step=n_step)
EXPt.plot_trajectory()
EXPd.plot_trajectory()
EXPt.crosscorr_rho(smooth_sigma=0.)
EXPd.crosscorr_rho(smooth_sigma=0.5)
# SAMPLE TRAJECTORIES
ENV_OB = OpenBox(scale=scale_OB)
GEN_OB = Generator(ENV=ENV_OB, symmetrize=symmetrize, jump_rate=jump_rate)
PROPt_OB = Propagator(GEN=GEN_OB,
sigma=sigma,
exp_eff_s = []
exp_eff_d = []
exp_eff_o = []
cov_visits_s = []
cov_visits_d = []
cov_visits_o = []
traj_cost_sum_s = []
traj_cost_sum_d = []
traj_cost_sum_o = []
n_visits_s = []
n_visits_d = []
n_visits_o = []
for worker in range(n_workers_exp):
EXPd = Explorer(PROP=PROPd,
rho_init=rho_init_exp,
no_dwell=no_dwell_exp,
label='DIFF_w%i_%i_%i' %
(worker, n_step_exp, n_samp_exp),
target_dir=simdir)
EXPs = Explorer(PROP=PROPs,
rho_init=rho_init_exp,
no_dwell=no_dwell_exp,
label='SUPERDIFF_w%i_%i_%i' %
(worker, n_step_exp, n_samp_exp),
target_dir=simdir)
EXPo = Explorer(PROP=PROPo,
rho_init=rho_init_exp,
no_dwell=no_dwell_exp,
label='ACMIN_w%i_%i_%i' %
(worker, n_step_exp, n_samp_exp),
target_dir=simdir)
EXPd.sample_sequences(n_samp=n_samp_exp, n_step=n_step_exp)
GEN = Generator(ENV=ENV, symmetrize=symmetrize, jump_rate=jump_rate)
GENh = Generator(ENV=ENV, symmetrize=symmetrize, jump_rate=jump_rate)
GENh.highlight_states(states=states_home, weight=goal_weight)
PROPd = Propagator(GEN=GENh, tau=tau_diff, alpha=alpha_diff)
PROPs = Propagator(GEN=GENh, tau=tau_sdiff, alpha=alpha_sdiff)
# multi-start away explorers
EXPds = []
EXPss = []
PROPds = []
PROPss = []
for start in states_away:
EXPd = Explorer(PROP=PROPd,
rho_init=start,
no_dwell=no_dwell,
label='diff-start%i' % start)
EXPs = Explorer(PROP=PROPs,
rho_init=start,
no_dwell=no_dwell,
label='sdiff-start%i' % start)
PROPds.append(PROPd.etO[start, :])
PROPss.append(PROPs.etO[start, :])
EXPd.sample_sequences(n_samp=n_samp_away, n_step=n_step_away)
EXPs.sample_sequences(n_samp=n_samp_away, n_step=n_step_away)
EXPds.append(EXPd)
EXPss.append(EXPs)
# single-start away explorers
EXPda = Explorer(PROP=PROPd,
rho_init=state_away_default,
alpha=alpha_diff)
PROP_sdiff_base = Propagator(GEN=GEN,
sigma=sigma,
tau=tau_sdiff,
alpha=alpha_sdiff)
PROP_diff_taus = [
Propagator(GEN=GEN, sigma=sigma, tau=tau, alpha=alpha_diff) for tau in taus
]
PROP_sdiff_taus = [
Propagator(GEN=GEN, sigma=sigma, tau=tau, alpha=alpha_sdiff)
for tau in taus
]
# parallel explorer
EXP_diff_base = Explorer(PROP=PROP_diff_base,
rho_init=start_prop,
no_dwell=no_dwell,
label='diffusion')
EXP_sdiff_base = Explorer(PROP=PROP_sdiff_base,
rho_init=start_prop,
no_dwell=no_dwell,
label='superdiffusion')
EXP_diff_base.sample_sequences(n_samp=n_samp, n_step=n_step)
EXP_sdiff_base.sample_sequences(n_samp=n_samp, n_step=n_step)
EXP_diff_base.compute_diagnostics(target_coverage=target_coverage,
flight_vision=flight_vision)
EXP_sdiff_base.compute_diagnostics(target_coverage=target_coverage,
flight_vision=flight_vision)
# serial explorer
EXP_diff_oneshot = Explorer(PROP=PROP_diff_base,
rho_init=start_prop,
autoprop_off=False,
cbar=False)
PROP_sdiff.plot_prop_kernels(n=5,
first_state=start,
wrap_col=5,
autoprop_off=False,
cbar=False)
PROP_sdiff_sr.plot_prop_kernels(n=5,
first_state=start,
wrap_col=5,
autoprop_off=False,
cbar=False)
# %% explorers - sample trajectories
EXP_diff = Explorer(PROP=PROP_diff,
rho_init=start,
no_dwell=no_dwell,
label='diffusion [complete]')
EXP_diff_sr = Explorer(PROP=PROP_diff_sr,
rho_init=start,
no_dwell=no_dwell,
label='diffusion [scale ratio]')
EXP_sdiff = Explorer(PROP=PROP_sdiff,
rho_init=start,
no_dwell=no_dwell,
label='superdiffusion [complete]')
EXP_sdiff_sr = Explorer(PROP=PROP_sdiff_sr,
rho_init=start,
no_dwell=no_dwell,
label='superdiffusion [scale ratio]')
EXP_diff.sample_sequences(n_samp=n_samp, n_step=n_step_diff)