simdata['speed_log'].append((v_l_act, v_r_act))
simdata['desired_speed_log'].append((v_l_des, v_r_des))
# Save motor neurons firing rates
simdata['motor_fr'].append(motor_firing_rates)
# Save linear and angualr velocities
simdata['linear_velocity_log'].append(v_t)
simdata['angular_velocity_log'].append(w_t)
# Move robot according to the read-out speeds from motor neurons
x_cur, y_cur, theta_cur = env.move(x_cur, y_cur, theta_cur, v_t, w_t)
nrns_st, rctrs_st = learning.get_spike_times(nrns_sd, rctrs_sd)
rec_nrn_tags, nrn_nrn_tags, rctr_t, nrn_t, pt = learning.get_eligibility_trace(
nrns_st, rctrs_st, simtime,
simdata['rctr_nrn_trace'][t], simdata['nrn_nrn_trace'][t])
simdata['rctr_nrn_trace'].append(rec_nrn_tags)
simdata['nrn_nrn_trace'].append(nrn_nrn_tags)
if (t+1) % 10 == 0:
fitness = ev.get_fitness_value(simdata['speed_log'][t-10:])
print "Fitness"
print fitness
print "Reward"
reward = learning.get_reward(reward, fitness, x_cur, y_cur)
print reward
simdata['reward'].append(reward)
delta_w_rec, delta_w_nrn = learning.get_weight_changes(reward, rec_nrn_tags,
nrn_nrn_tags)
learning.update_weights(delta_w_rec, delta_w_nrn, nrns, rctrs)
def simulate_learning(neurons, receptors, nrns_sd, rctrs_sd, arena):
"""
Run simulation for 40 seconds based on network topology encoded in
individual.
@param individual: Binary string encoding network topology.
@param arena: The arena object in which the simulation is carried
out.
@param n_step: Number of steps the simulation is divided in.
@param t_step: Number of milliseconds in each step.
@param reset: bool triggering the reset of the nest kernel.
"""
global simtime
simdata = create_empty_data_lists()
if data['init'] == 'random':
simdata['x_init'], simdata['y_init'], theta_init = \
select_random_pose(arena)
x_cur = simdata['x_init']
y_cur = simdata['y_init']
theta_cur = theta_init
err_l, err_r = 0, 0
simdata['rctr_nrn_trace'].append(np.zeros((18, 10)))
simdata['nrn_nrn_trace'].append(np.zeros((10, 10)))
motor_spks = nrns_sd[6:]
for t in range(n_step):
ev.update_refratory_perioud()
simdata['traj'].append((x_cur, y_cur))
px = network.set_receptors_firing_rate(x_cur, y_cur, theta_cur,
err_l, err_r)
simdata['pixel_values'].append(px)
# Run simulation for time-step
nest.Simulate(t_step)
simtime += t_step
motor_firing_rates = network.get_motor_neurons_firing_rates(motor_spks)
v_l_act, v_r_act, v_t, w_t, err_l, err_r, col, v_l_des, v_r_des = \
motion.update_wheel_speeds(x_cur, y_cur, theta_cur, motor_firing_rates)
# Save dsired and actual speeds
simdata['speed_log'].append((v_l_act, v_r_act))
simdata['desired_speed_log'].append((v_l_des, v_r_des))
# Save motor neurons firing rates
simdata['motor_fr'].append(motor_firing_rates)
# Save linear and angualr velocities
simdata['linear_velocity_log'].append(v_t)
simdata['angular_velocity_log'].append(w_t)
# Move robot according to the read-out speeds from motor neurons
x_cur, y_cur, theta_cur = motion.move(x_cur, y_cur, theta_cur, v_t,
w_t)
nrns_st, rctrs_st = learning.get_spike_times(nrns_sd, rctrs_sd)
rec_nrn_tags, nrn_nrn_tags, rctr_t, nrn_t, pt = \
learning.get_eligibility_trace(nrns_st, rctrs_st, simtime,
simdata['rctr_nrn_trace'][t], simdata['nrn_nrn_trace'][t])
print simtime
simdata['rctr_nrn_trace'].append(rec_nrn_tags)
simdata['nrn_nrn_trace'].append(nrn_nrn_tags)
# Stop simulation if collision occurs
if col:
simdata['speed_log'].extend((0, 0) for k in range(t, 400))
break
simdata['fitness'] = ev.get_fitness_value(simdata['speed_log'])
return simdata, rctr_t, nrn_t, pt, col
