def calculate_score_for_leaderboard(): """ Evaluate the performance of the network. This is the function to be used for the final ranking on the course-wide leader-board, only with a different set of seeds. Better not change it. """ # action variables a = np.array([0.0, 0.0, 0.0]) # init environement env = CarRacing() env.render() env.reset() seeds = [ 22597174, 68545857, 75568192, 91140053, 86018367, 49636746, 66759182, 91294619, 84274995, 31531469 ] total_reward = 0 for episode in range(10): env.seed(seeds[episode]) observation = env.reset() # init modules of the pipeline LD_module = LaneDetection(gradient_threshold=25, spline_smoothness=20) LatC_module = LateralController(gain_constant=1.8, damping_constant=0.05) LongC_module = LongitudinalController(KD=0.001) reward_per_episode = 0 for t in range(600): # perform step s, r, done, speed, info = env.step(a) # lane detection lane1, lane2 = LD_module.lane_detection(s) # waypoint and target_speed prediction waypoints = waypoint_prediction(lane1, lane2) target_speed = target_speed_prediction(waypoints, max_speed=60, exp_constant=6) # control a[0] = LatC_module.stanley(waypoints, speed) a[1], a[2] = LongC_module.control(speed, target_speed) # reward reward_per_episode += r env.render() print('episode %d \t reward %f' % (episode, reward_per_episode)) total_reward += np.clip(reward_per_episode, 0, np.infty) print('---------------------------') print(' total score: %f' % (total_reward / 10)) print('---------------------------')
def multiple_runs(v, on): env = CarRacing() z_set = [] action_set = [] for run in range(MAX_RUNS): zs = [] actions = [] state = env.reset() env.render() # must have! # done = False counter = 0 for game_time in range(MAX_GAME_TIME): # env.render() action = generate_action() obs = state_to_1_batch_tensor(state) _, _, _, z = v(obs) z = z.detach().numpy() z = z.reshape(32) # print(z.shape) # if game_time == 5: # plt.imshow(state) # plt.show() # state = _process_frame(state) # plt.imshow(state) # plt.show() zs.append(z) actions.append(action) state, r, done, _ = env.step(action) # print(r) print( 'RUN:{},GT:{},DATA:{}'.format( run, game_time, len(actions) ) ) # if counter == REST_NUM: # # position = np.random.randint(len(env.track)) # env.car = Car(env.world, *env.track[position][1:4]) # counter = 0 # counter += 1 zs = np.array(zs, dtype=np.float16) # print(zs.shape) actions = np.array(actions, dtype=np.float16) # print(actions.shape) # np.save(dst + '/' + save_name, frame_and_action) # np.savez_compressed(dst + '/' + save_name, action=actions, z=zs) z_set.append(zs) action_set.append(actions) z_set = np.array(z_set) # print(z_set.shape) action_set = np.array(action_set) # print(action_set.shape) save_name = name_this + '_{}.npz'.format(on) np.savez_compressed(dst + '/' + save_name, action=action_set, z=z_set)
def game_runner(): from pyglet.window import key a = np.array([0.0, 0.0, 0.0]) def key_press(k, mod): global restart if k == 0xff0d: restart = True if k == key.LEFT: a[0] = -1.0 if k == key.RIGHT: a[0] = +1.0 if k == key.UP: a[1] = +1.0 if k == key.DOWN: a[2] = +0.8 def key_release(k, mod): if k == key.LEFT and a[0] == -1.0: a[0] = 0 if k == key.RIGHT and a[0] == +1.0: a[0] = 0 if k == key.UP: a[1] = 0 if k == key.DOWN: a[2] = 0 env = CarRacing() env.render() env.viewer.window.on_key_press = key_press env.viewer.window.on_key_release = key_release while True: env.reset() total_reward = 0.0 steps = 0 restart = False while True: s, r, done, info = env.step(a) total_reward += r if steps == 900: print("\n") print("_______________________________") print("\n") print("Human Intelligence Result:") print("Total Steps: {}".format(steps)) print("Total Reward: {:.0f}".format(total_reward)) print("\n") print("_______________________________") print("\n") break steps += 1 env.render() if restart: break env.monitor.close()
def evaluate(): """ """ # action variables a = np.array([0.0, 0.0, 0.0]) # init environement env = CarRacing() env.render() env.reset() for episode in range(5): observation = env.reset() # init modules of the pipeline LD_module = LaneDetection() LatC_module = LateralController() LongC_module = LongitudinalController() reward_per_episode = 0 for t in range(500): # perform step s, r, done, speed, info = env.step(a) # lane detection lane1, lane2 = LD_module.lane_detection(s) # waypoint and target_speed prediction waypoints = waypoint_prediction(lane1, lane2) target_speed = target_speed_prediction(waypoints, max_speed=60, exp_constant=4.5) # control a[0] = LatC_module.stanley(waypoints, speed) a[1], a[2] = LongC_module.control(speed, target_speed) # reward reward_per_episode += r env.render() print('episode %d \t reward %f' % (episode, reward_per_episode))
def run_caracing_by_hunman(): a = np.array([0.0, 0.0, 0.0]) def key_press(k, mod): global restart if k == 0xff0d: restart = True if k == key.LEFT: a[0] = -1.0 if k == key.RIGHT: a[0] = +1.0 if k == key.UP: a[1] = +1.0 if k == key.DOWN: a[2] = +0.8 # set 1.0 for wheels to block to zero rotation def key_release(k, mod): if k == key.LEFT and a[0] == -1.0: a[0] = 0 if k == key.RIGHT and a[0] == +1.0: a[0] = 0 if k == key.UP: a[1] = 0 if k == key.DOWN: a[2] = 0 env = CarRacing() env.render() env.viewer.window.on_key_press = key_press env.viewer.window.on_key_release = key_release while True: env.reset() total_reward = 0.0 steps = 0 restart = False while True: s, r, done, info = env.step(a) total_reward += r if steps % 200 == 0 or done: print("\naction " + str(["{:+0.2f}".format(x) for x in a])) print("step {} total_reward {:+0.2f}".format( steps, total_reward)) steps += 1 env.render() if done or restart: break env.monitor.close()
global restart if k==0xff0d: restart = True if k==key.LEFT: a[0] = -1.0 if k==key.RIGHT: a[0] = +1.0 if k==key.UP: a[1] = +1.0 if k==key.DOWN: a[2] = +0.8 # set 1.0 for wheels to block to zero rotation def key_release(k, mod): if k==key.LEFT and a[0]==-1.0: a[0] = 0 if k==key.RIGHT and a[0]==+1.0: a[0] = 0 if k==key.UP: a[1] = 0 if k==key.DOWN: a[2] = 0 # init environement env = CarRacing() env.render() env.viewer.window.on_key_press = key_press env.viewer.window.on_key_release = key_release env.reset() # define variables total_reward = 0.0 steps = 0 restart = False # init modules of the pipeline LD_module = LaneDetection() # init extra plot fig = plt.figure() plt.ion()