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)
weights.append(learning.get_weights(rctrs, nrns))
# Stop simulation if collision occurs
if col:
# simdata['speed_log'].extend((0, 0) for k in range(t, 400))
# break
print "COLLISION"
break
simdata['fitness'] = ev.get_fitness_value(simdata['speed_log'])
data.append(simdata)
# reward = get_reward(reward, simdata['speed_log'], x_cur, y_cur)
# simdata['reward'].append(reward)
top_performers = ev.get_top_performers(generation_log)
best_of_generation.append(top_performers[0])
update_elite()
results.save_generation_results(top_performers[0], average_fitness,
average_connectivity, gen)
population = ev.evolve_new_generation(top_performers)
results.save_fitness(average_fitness,
[best_of_generation[i][1] for i in range(len(best_of_generation))])
else:
# Learning
results.set_results_path(data['init'], data['model'], data['arena'],
0)
fitness, rewards, weights = [], [], []
simtime = 0
fitness.append(0)
rewards.append(0)
nrns, nrns_sd, rctrs, rctrs_sd, pop_spikes = \
network.create_learning_network(data['model'])
for i in range(50):
weights.append(learning.get_weights(rctrs, nrns))
simdata, rt, nt, pt, col = simulate_learning(nrns, rctrs, nrns_sd,
rctrs_sd, arena)
fitness.append(simdata['fitness'])
reward = learning.get_reward(rewards[-1], fitness[-1],
simdata['traj'][-1][0], simdata['traj'][-1][1])
rewards.append(reward)
delta_w_rec, delta_w_nrn = learning.get_weight_changes(reward,
simdata['rctr_nrn_trace'][-1], simdata['nrn_nrn_trace'][-1])
learning.update_weights(delta_w_rec, delta_w_nrn, nrns, rctrs)
results.save_rl_results(simdata, pop_spikes, i)
