def do_running_printout(self):
clear()
print ("Simname: " + str(p["simname"]))
print ("Episodes Elapsed: " + str(self.episode))
print ("Average Reward Per Episode: " + str(self.r_sum_avg))
print ("Average Number of Steps Spent Balancing Pole: " + str(self.steps_balancing_pole_avg))
print ("Max Number of Steps Spent Balancing Pole: " + str(np.max(np.array(self.steps_balancing_pole_avg_list))))
print ("Epsilon: " + str(self.epsilon))
print ("Epsilon Min: " + str(p["epsilon_min"]))
print ("Alpha (learning rate): " + str(self.alpha * p["learning_rate"]))
if p.has_key("learning_rate_decay"):
print ("Alpha (learning rate) decay: " + str(p["learning_rate_decay"]))
if p["qsa_type"] == "cluster_nnet":
print ("num_hidden: " + str(p["num_hidden"]))
print ("num_selected: " + str(self.qsa.net.layer[0].num_selected))
if p["qsa_type"] == "nnet":
print ("Activation function: " + str(p["activation_function"]))
print ("num_hidden: " + str(p["num_hidden"]))
if p["action_type"] == "noisy_qsa":
print ("Average QSA Standard Deviation: " + str(self.qsa_std_avg))
print ("Probability of taking different action: " + str(self.prob_of_different_action))
if p["qsa_type"] == "cartpole_nnet":
print ("state given to nnet:\n" + str(np.array(self.qsa.net.input).transpose()))
print ("Average Steps Per Second: " + str(1.0 / self.avg_step_duration))
print ("Action Type: " + str(p["action_type"]))
print ("a_list: " + str(self.tmp_a_list))
m, s = divmod(time.time() - self.start_time, 60)
h, m = divmod(m, 60)
print "Elapsed Time %d:%02d:%02d" % (h, m, s)
sys.stdout.flush()
print_update_timer = time.time()
def do_running_printout(self):
clear()
print("Simname: " + str(p['simname']))
print("Episodes Elapsed: " + str(self.episode))
print("Average Reward Per Episode: " + str(self.r_sum_avg))
print("Average Number of Steps Spent Balancing Pole: " + str(self.steps_balancing_pole_avg))
print("Max Number of Steps Spent Balancing Pole: " + str(np.max(np.array(self.steps_balancing_pole_avg_list))))
print("Epsilon: " + str(self.epsilon))
print("Epsilon Min: " + str(p['epsilon_min']))
print("Alpha (learning rate): " + str(self.alpha*p['learning_rate']))
if(p.has_key('learning_rate_decay')):
print("Alpha (learning rate) decay: " + str(p['learning_rate_decay']))
if(p['qsa_type'] == 'cluster_nnet'):
print("num_hidden: " + str(p['num_hidden']))
print('num_selected: ' + str(self.qsa.net.layer[0].num_selected))
if(p['qsa_type'] == 'nnet'):
print("Activation function: " + str(p['activation_function']))
print("num_hidden: " + str(p['num_hidden']))
if(p['action_type'] == 'noisy_qsa'):
print("Average QSA Standard Deviation: " + str(self.qsa_std_avg))
print("Probability of taking different action: " + str(self.prob_of_different_action))
if(p['qsa_type'] == 'cartpole_nnet'):
print("state given to nnet:\n" + str(np.array(self.qsa.net.input).transpose()))
print("Average Steps Per Second: " + str(1.0/self.avg_step_duration))
print("Action Type: " + str(p['action_type']))
print("a_list: " + str(self.tmp_a_list))
m, s = divmod(time.time() - self.start_time, 60)
h, m = divmod(m, 60)
print "Elapsed Time %d:%02d:%02d" % (h, m, s)
sys.stdout.flush()
print_update_timer = time.time()
def run_sim(self,p):
sim = cartpole_environment()
reward_type = p.get('reward_type',0)
negative_reward = p['negative_reward']
positive_reward = p['positive_reward']
sim.init(p['vel_bound'],p['angle_vel_bound'],p['pos_bound'],p['g'],p['l'],p['mp'],p['mc'],p['dt'],p['negative_reward'],p['positive_reward'],p['no_reward'],reward_type)
v = visualize_sdl()
v.init_vis(p['display_width'],p['display_height'],p['axis_x_min'],p['axis_x_max'],p['axis_y_min'],p['axis_y_max'],p['fps'])
push_force = p['push_force']
self.vel_bound = p['vel_bound']
self.pos_bound = p['pos_bound']
self.angle_vel_bound = p['angle_vel_bound']
self.mins = np.array([0.0, -self.vel_bound, -self.pos_bound, -self.angle_vel_bound])
self.maxs = np.array([2*math.pi, self.vel_bound, self.pos_bound, self.angle_vel_bound])
self.mins = np.append(np.array([-1.0,-1.0]),self.mins[1:])
self.maxs = np.append(np.array([1.0,1.0]),self.maxs[1:])
self.incorrect_target = p['incorrect_target']
self.correct_target = p['correct_target']
self.num_actions = 3
action=0
while 1:
if(p.has_key('print_state_debug')):
clear()
action_list = np.ones((1,self.num_actions))*self.incorrect_target
action_list[0,action] = self.correct_target
state = sim.sim.state
s = np.append(np.array([math.sin(state[0]),math.cos(state[0])]),state[1:])
s = (np.array(s) - self.mins)/(self.maxs - self.mins)
s = s-0.5
s = s*2.25
s = np.append(s,action_list)
np.set_printoptions(precision=4)
print(str(s[:,np.newaxis]))
print(str(np.array(sim.sim.state)[:,np.newaxis]))
v.delay_vis()
k = v.get_keys()
u = 0.0;
action = 0
if(k[0]):
action = 2
u = -push_force;
if(k[1]):
action = 1
u = push_force;
sim.set_action(u)
sim.step()
#if(sim.state[2] < -4.0):
# sim.state[2] = -4.0
#if(sim.state[2] > 4.0):
# sim.state[2] = 4.0
if(sim.is_terminal):
sim.reset_state()
v.draw_cartpole(sim.get_state(),action,sim.get_reward(reward_type)/positive_reward)
exit = v.update_vis()
if(exit):
break
return
