def cum_softmax_direction_prop(state):
# calculates the cumulated softmax propability for every possible action
current_policy = policy[state['y'], state['x'], :] # prop in this agent_pos
softmax_prop = numpy.exp(current_policy)
softmax_prop = softmax_prop / numpy.sum(softmax_prop) # softmax: (e^prop) / (sum(e^prop))
cum_softmax_prop = numpy.cumsum(softmax_prop) # cumulating
return (cum_softmax_prop)
def pick_action(state):
cum_softmax_prop = cum_softmax_direction_prop(state)
r = numpy.random.rand()
for i in range(len(cum_softmax_prop)):
if cum_softmax_prop[i] > r:
return i
while True:
possible_actions = env.get_possible_actions()
direction = pick_action(state)
last_state = state.copy()
outcome = 0
state, outcome, in_end_pos = env.move(possible_actions[direction])
time.sleep(0.02)
state = env.getState().copy()
plot(fig, ax, nest.GetStatus(sd_wta, keys='events')[0])
max_rate = -1
chosen_action = -1
for i in range(len(sd_actions)):
rate = len([e for e in nest.GetStatus([sd_actions[i]], keys='events')[0]['times'] if e > last_action_time]) # calc the "firerate" of each actor population
if rate > max_rate:
max_rate = rate # the population with the hightes rate wins
chosen_action = i
nest.SetStatus(stimulus, {'rate': 5000.})
possible_actions = env.get_possible_actions()
new_position, outcome, in_end_position = env.move(possible_actions[chosen_action])
nest.SetStatus(wta_noise, {'rate': 0.})
for t in range(4):
nest.Simulate(5)
time.sleep(0.01)
last_action_time += 60
actions_executed += 1
else:
position = env.get_agent_pos().copy()
_, in_end_position = env.init_new_trial()
nest.SetStatus(nest.GetConnections(stimulus, states[position['x']][position['y']]), {'weight': 0.})
rplt.from_device(sd_wta, title="WTA circuit")
