From d6735267b6e78e8ff6af5207abe4eb3d10c7c09e Mon Sep 17 00:00:00 2001 From: pjfernandecabo Date: Mon, 6 Mar 2023 13:38:06 -0500 Subject: [PATCH 01/63] fixed windows size, print messsages --- .../train_followlane_qlearn_carla.py | 39 +- rl_studio/agents/utils.py | 7 +- rl_studio/algorithms/qlearn.py | 21 +- ...nfig_training_followlane_qlearn_carla.yaml | 4 +- .../carla/followlane/followlane_qlearn.py | 770 +++--------------- rl_studio/envs/carla/followlane/utils.py | 19 +- 6 files changed, 167 insertions(+), 693 deletions(-) diff --git a/rl_studio/agents/auto_carla/train_followlane_qlearn_carla.py b/rl_studio/agents/auto_carla/train_followlane_qlearn_carla.py index 945c50b91..d84686848 100755 --- a/rl_studio/agents/auto_carla/train_followlane_qlearn_carla.py +++ b/rl_studio/agents/auto_carla/train_followlane_qlearn_carla.py @@ -145,7 +145,7 @@ def main(self): step = 0 observation = env.reset() - state = "".join(map(str, observation)) + state = "-".join(map(str, observation)) print_messages( "in episode", episode=episode, @@ -166,7 +166,7 @@ def main(self): new_observation, reward, done, _ = env.step(action) cumulated_reward += reward - next_state = "".join(map(str, new_observation)) + next_state = "-".join(map(str, new_observation)) print_messages( "", step=step, @@ -187,6 +187,41 @@ def main(self): state=state, ) env.display_manager.render() + # render params + """ + render_params( + action=action, + episode=episode, + step=step, + v=self.global_params.actions_set[action][ + 0 + ], # this case for discrete + w=self.global_params.actions_set[action][ + 1 + ], # this case for discrete + epsilon=epsilon, + observation=observation, + reward_in_step=reward, + cumulated_reward=cumulated_reward, + current_max_reward=current_max_reward, + done=done, + ) + """ + # End epoch + if step > self.env_params.estimated_steps: + done = True + np.save( + f"{self.global_params.models_dir}/{time.strftime('%Y%m%d-%H%M%S')}_Circuit-{self.environment.environment['town']}_States-{self.environment.environment['states']}_Actions-{self.environment.environment['action_space']}_Rewards-{self.environment.environment['reward_function']}_epsilon-{round(epsilon,3)}_epoch-{episode}_step-{step}_reward-{int(cumulated_reward)}-qtable.npy", + qlearn.q_table, + ) + + # updating epsilon for exploration + if epsilon > self.environment.environment["epsilon_min"]: + # self.epsilon *= self.epsilon_discount + epsilon -= epsilon_decay + epsilon = qlearn.update_epsilon( + max(self.environment.environment["epsilon_min"], epsilon) + ) ## ------------ destroy actors diff --git a/rl_studio/agents/utils.py b/rl_studio/agents/utils.py index 7010ba446..92cb06c0e 100644 --- a/rl_studio/agents/utils.py +++ b/rl_studio/agents/utils.py @@ -92,7 +92,7 @@ def print_dictionary(dic): def render_params(**kwargs): font = cv2.FONT_HERSHEY_SIMPLEX - canvas = np.zeros((400, 400, 3), dtype="uint8") + canvas = np.zeros((500, 500, 3), dtype="uint8") # blue = (255, 0, 0) # green = (0, 255, 0) # red = (0, 0, 255) @@ -112,7 +112,10 @@ def render_params(**kwargs): ) i += 25 - cv2.imshow("Control Board", canvas) + window_name = "Control Board" + cv2.namedWindow(window_name) # Create a named window + cv2.moveWindow(window_name, 0, 0) # Move it to (40,30) + cv2.imshow(window_name, canvas) cv2.waitKey(100) diff --git a/rl_studio/algorithms/qlearn.py b/rl_studio/algorithms/qlearn.py index f8284ffa0..4de873cbd 100644 --- a/rl_studio/algorithms/qlearn.py +++ b/rl_studio/algorithms/qlearn.py @@ -312,17 +312,16 @@ def __init__(self, actions, epsilon, alpha, gamma): def select_action(self, state): q_list = [self.q_table.get((state, a), 0.0) for a in self.actions] - # print(f"\n{q_list = }") + print(f"\n{q_list = }") max_q = max(q_list) - # print(f"{max_q}") + print(f"{max_q = }") count_max = q_list.count(max_q) - # print(f"{count_max}") - if count_max > 1: - best = [i for i in range(len(q_list)) if q_list[i] == max_q] - max_q = random.choice(best) - # print(f"{len(self.actions)=}") - if np.random.random() > self.epsilon: - action = q_list.index(max_q) + print(f"{count_max= }") + best_index = [index for index, value in enumerate(q_list) if value == max_q] + + if count_max > 1 or np.random.random() > self.epsilon: + # best = [i for i in range(len(q_list)) if q_list[i] == max_q] + action = random.choice(best_index) else: action = np.random.randint(0, len(self.actions)) @@ -338,7 +337,7 @@ def learn(self, state, action, reward, next_state): 2) Q(s, a) = Q(s,a) + alpha * (reward(s,a) + gamma * Q(s',a) - Q(s,a)) """ - # print(f"q_table al entrar al learn() = {self.q_table}") + # print(f"init q_table in learn() = {self.q_table}") # print(f"{state = }") # print(f"{next_state = }") @@ -361,7 +360,7 @@ def learn(self, state, action, reward, next_state): self.q_table[(state, action)] = ( 1 - self.alpha ) * current_q + self.alpha * (reward + self.gamma * max_q_new) - # print(f"q_table al salir del learn() {self.q_table = }") + # print(f"output q_table learn() {self.q_table = }") def inference(self, state): return np.argmax(self.q_table[state]) diff --git a/rl_studio/config/config_training_followlane_qlearn_carla.yaml b/rl_studio/config/config_training_followlane_qlearn_carla.yaml index 9ec99d912..62399e338 100644 --- a/rl_studio/config/config_training_followlane_qlearn_carla.yaml +++ b/rl_studio/config/config_training_followlane_qlearn_carla.yaml @@ -10,7 +10,7 @@ settings: states: sp4 # sp1 (simplified perception with 1 point), sp3 (simplified perception with 3 points), spn (simplified perception with n points) rewards: follow_right_lane_only_center # framework: _ - total_episodes: 3 + total_episodes: 100 training_time: 6 models_dir: "./checkpoints" logs_dir: "./logs" @@ -115,7 +115,7 @@ carla_environments: traffic_pedestrians: False city_lights: False car_lights: False - estimated_steps: 5 + estimated_steps: 100 save_episodes: 10 save_every_step: 1000 init_pose: diff --git a/rl_studio/envs/carla/followlane/followlane_qlearn.py b/rl_studio/envs/carla/followlane/followlane_qlearn.py index 108ab1d4c..6bff298d7 100644 --- a/rl_studio/envs/carla/followlane/followlane_qlearn.py +++ b/rl_studio/envs/carla/followlane/followlane_qlearn.py @@ -14,6 +14,7 @@ from rl_studio.agents.utils import ( print_messages, + render_params, ) from rl_studio.envs.carla.followlane.followlane_env import FollowLaneEnv from rl_studio.envs.carla.followlane.settings import FollowLaneCarlaConfig @@ -77,6 +78,8 @@ def __init__(self, **config): self.perfect_distance_pixels = None self.perfect_distance_normalized = None + self._control = carla.VehicleControl() + self.params = {} def reset(self): @@ -126,92 +129,6 @@ def reset(self): ## --- Sensor collision self.setup_col_sensor() - ## --- Cameras - # self.camera_spectator = SensorManager( - # self.world, - # self.display_manager, - # "RGBCamera", - # carla.Transform(carla.Location(x=-5, z=2.8), carla.Rotation(yaw=+00)), - # self.car, - # {}, - # display_pos=[0, 0], - # ) - # self.camera_spectator_segmentated = SensorManager( - # self.world, - # self.display_manager, - # "SemanticCamera", - # carla.Transform(carla.Location(x=-5, z=2.8), carla.Rotation(yaw=+00)), - # self.car, - # {}, - # display_pos=[1, 0], - # ) - # self.sergio_camera_spectator = SensorManager( - # self.world, - # self.display_manager, - # "SemanticCameraSergio", - # carla.Transform(carla.Location(x=-5, z=2.8), carla.Rotation(yaw=+00)), - # self.car, - # {}, - # display_pos=[2, 0], - # ) - # self.front_camera = SensorManager( - # self.world, - # self.display_manager, - # "RGBCamera", - # carla.Transform(carla.Location(x=2, z=1), carla.Rotation(yaw=+00)), - # self.car, - # {}, - # display_pos=[0, 1], - # ) - - # self.front_camera_segmentated = SensorManager( - # self.world, - # self.display_manager, - # "SemanticCamera", - # carla.Transform(carla.Location(x=2, z=1), carla.Rotation(yaw=+00)), - # self.car, - # {}, - # display_pos=[1, 1], - # ) - - # self.sergio_front_camera = SensorManager( - # self.world, - # self.display_manager, - # "SemanticCameraSergio", - # carla.Transform(carla.Location(x=2, z=1), carla.Rotation(yaw=+00)), - # self.car, - # {}, - # display_pos=[2, 1], - # ) - # self.front_camera_mas_baja = SensorManager( - # self.world, - # self.display_manager, - # "RGBCamera", - # carla.Transform(carla.Location(x=2, z=0.5), carla.Rotation(yaw=+00)), - # self.car, - # {}, - # display_pos=[0, 2], - # ) - # self.front_camera_mas_baja_segmentated = SensorManager( - # self.world, - # self.display_manager, - # "SemanticCamera", - # carla.Transform(carla.Location(x=2, z=0.5), carla.Rotation(yaw=+00)), - # self.car, - # {}, - # display_pos=[1, 2], - # ) - - # self.sergio_front_camera_mas_baja = SensorManager( - # self.world, - # self.display_manager, - # "SemanticCameraSergio", - # carla.Transform(carla.Location(x=2, z=0.5), carla.Rotation(yaw=+0)), - # self.car, - # {}, - # display_pos=[2, 2], - # ) - self.front_camera_1_5_bev = SensorManager( self.world, self.display_manager, @@ -257,12 +174,14 @@ def reset(self): self.episode_start = time.time() self.car.apply_control(carla.VehicleControl(throttle=0.0, brake=0.0)) - AutoCarlaUtils.show_image("image", self.front_camera_1_5.front_camera, 1) + # AutoCarlaUtils.show_image("image", self.front_camera_1_5.front_camera, 1, 0, 10) mask = self.preprocess_image( self.front_camera_1_5_red_mask.front_camera_red_mask ) - AutoCarlaUtils.show_image("mask", mask, 1) + # AutoCarlaUtils.show_image( + # "mask", mask, 1, self.front_camera_1_5.front_camera.shape[1], 10 + # ) # Wait for world to get the vehicle actor self.world.tick() @@ -285,6 +204,8 @@ def reset(self): return states #################################################### + # + # Reset Methods #################################################### def calculate_states(self, mask): width = mask.shape[1] @@ -357,26 +278,8 @@ def draw_waypoints(self, spawn_points, init, target, lane_id, life_time): return filtered_waypoints - def get_target_waypoint(self, target_waypoint, life_time): - """ - draw target point - """ - self.world.debug.draw_string( - target_waypoint.transform.location, - "O", - draw_shadow=False, - color=carla.Color(r=255, g=0, b=0), - life_time=life_time, - persistent_lines=True, - ) - def setup_car_fix_pose(self, init): car_bp = self.world.get_blueprint_library().filter("vehicle.*")[0] - # location = random.choice(self.world.get_map().get_spawn_points()) - # location = carla.Transform( - # carla.Location(x=-14.130021, y=69.766624, z=4), - # carla.Rotation(pitch=360.000000, yaw=0.073273, roll=0.000000), - # ) location = carla.Transform( carla.Location( x=init.transform.location.x, @@ -429,12 +332,14 @@ def destroy_all_actors(self): # [carla.command.DestroyActor(x) for x in self.actor_list[::-1]] # ) - ################################################################################# - def step(self, action): - # print(f"=============== STEP ===================") + ################################################# + # + # Step + ################################################# + def step(self, action): ### -------- send action - params = self.control(action) + self.control(action) # print(f"params = {params}") # params["pos"] = 270 @@ -448,14 +353,23 @@ def step(self, action): self.front_camera_1_5_red_mask.front_camera_red_mask ) - AutoCarlaUtils.show_image("mask", mask, 1) - ( states, distance_to_center, distance_to_center_normalized, ) = self.calculate_states(mask) + # AutoCarlaUtils.show_image("mask", mask, 1, 500, 10) + AutoCarlaUtils.show_image_with_centrals( + "mask", + mask, + 1, + distance_to_center, + distance_to_center_normalized, + self.x_row, + 600, + 10, + ) # print(f"states:{states}\n") AutoCarlaUtils.show_image_with_centrals( "image", @@ -464,6 +378,8 @@ def step(self, action): distance_to_center, distance_to_center_normalized, self.x_row, + self.front_camera_1_5.front_camera.shape[1] + 600, + 10, ) ## ------ calculate distance error and states @@ -479,34 +395,55 @@ def step(self, action): states_16 = counter_states.get(16) ## -------- Rewards - reward = self.rewards_easy(error, params) + reward = self.rewards_easy(error, self.params) ## ----- hacemos la salida done = False if states_16 is not None and ( states_16 > (len(states) // 2) ): # salimos porque no detecta linea a la derecha + print(f"no red line detected") done = True if len(self.collision_hist) > 0: # te has chocado, baby done = True - - # print_messages( - # "in step()", - # height=mask.shape[0], - # width=mask.shape[1], - # velocity=params["velocity"], - # steering_angle=params["steering_angle"], - # states=states, - # distance_to_center=distance_to_center, - # distance_to_center_normalized=distance_to_center_normalized, - # self_perfect_distance_pixels=self.perfect_distance_pixels, - # self_perfect_distance_normalized=self.perfect_distance_normalized, - # error=error, - # done=done, - # reward=reward, - # states_16=states_16, - # self_collision_hist=self.collision_hist, - # ) + print(f"crash") + + render_params( + action=action, + speed_kmh=self.params["speed"], + # steering_angle=self.params["steering_angle"], + Steer=self.params["Steer"], + location=self.params["location"], + Throttle=self.params["Throttle"], + Brake=self.params["Brake"], + height=self.params["height"], + _="------------------------", + reward=reward, + done=done, + ) + print_messages( + "in step()", + height=mask.shape[0], + width=mask.shape[1], + action=action, + velocity=self.params["speed"], + # steering_angle=self.params["steering_angle"], + Steer=self.params["Steer"], + location=self.params["location"], + Throttle=self.params["Throttle"], + Brake=self.params["Brake"], + _="------------------------", + states=states, + distance_to_center=distance_to_center, + distance_to_center_normalized=distance_to_center_normalized, + self_perfect_distance_pixels=self.perfect_distance_pixels, + self_perfect_distance_normalized=self.perfect_distance_normalized, + error=error, + done=done, + reward=reward, + states_16=states_16, + self_collision_hist=self.collision_hist, + ) return states, reward, done, {} @@ -514,32 +451,39 @@ def control(self, action): steering_angle = 0 if action == 0: - self.car.apply_control(carla.VehicleControl(throttle=1, steer=-0.2)) - steering_angle = -0.2 + # self.car.apply_control(carla.VehicleControl(throttle=1, steer=-0.2)) + self._control.throttle = min(self._control.throttle + 0.1, 1.00) + self._control.steer = -0.2 + # steering_angle = -0.2 elif action == 1: - self.car.apply_control(carla.VehicleControl(throttle=1, steer=0.0)) - steering_angle = 0 + # self.car.apply_control(carla.VehicleControl(throttle=1, steer=0.0)) + self._control.throttle = min(self._control.throttle + 0.1, 1.00) + self._control.steer = 0.0 + # steering_angle = 0 elif action == 2: - self.car.apply_control(carla.VehicleControl(throttle=1, steer=0.2)) - steering_angle = 0.2 - # elif action == 3: - # self.vehicle.apply_control(carla.VehicleControl(throttle=0.2, steer=-0.4)) - # steering_angle = 0.4 - # elif action == 4: - # self.vehicle.apply_control(carla.VehicleControl(throttle=0.2, steer=0.4)) - # steering_angle = 0.4 - else: - print("error in action") - pass - params = {} + # self.car.apply_control(carla.VehicleControl(throttle=1, steer=0.2)) + self._control.throttle = min(self._control.throttle + 0.1, 1.00) + self._control.steer = 0.2 + # steering_angle = 0.2 - v = self.car.get_velocity() - params["velocity"] = math.sqrt(v.x**2 + v.y**2 + v.z**2) + self.car.apply_control(self._control) + t = self.car.get_transform() + v = self.car.get_velocity() + c = self.car.get_control() w = self.car.get_angular_velocity() - params["steering_angle"] = steering_angle - - return params + self.params["speed"] = int(3.6 * math.sqrt(v.x**2 + v.y**2 + v.z**2)) + self.params["steering_angle"] = w + # print(f"{self.params['steering_angle'].x = }") + # print(f"{self.params['steering_angle'].y = }") + # print(f"{self.params['steering_angle'].z = }") + + # self.params["steering_angle"] = steering_angle + self.params["Steer"] = c.steer + self.params["location"] = (t.location.x, t.location.y) + self.params["Throttle"] = c.throttle + self.params["Brake"] = c.brake + self.params["height"] = t.location.z def rewards_followlane_dist_v_angle(self, error, params): # rewards = [] @@ -572,8 +516,10 @@ def rewards_easy(self, error, params): rewards.append(-100) function_reward = sum(rewards) / len(rewards) - function_reward += params["velocity"] * 0.5 - function_reward -= params["steering_angle"] * 1.02 + + # TODO: remove next comments + # function_reward += params["velocity"] * 0.5 + # function_reward -= params["steering_angle"] * 1.02 return function_reward @@ -593,181 +539,9 @@ def rewards_followlane_center_v_w(self): return reward, done - -########################################################################################################### -############################################################################################################ -############################################################################################################# -class OLD_FollowLaneQlearnStaticWeatherNoTraffic(FollowLaneEnv): - def __init__(self, **config): - - print(f"in FollowLaneQlearnStaticWeatherNoTraffic -> launching FollowLaneEnv\n") - ###### init F1env - FollowLaneEnv.__init__(self, **config) - ###### init class variables - print(f"leaving FollowLaneEnv\n ") - print(f"launching FollowLaneCarlaConfig\n ") - FollowLaneCarlaConfig.__init__(self, **config) - - self.client = carla.Client( - config["carla_server"], - config["carla_client"], - ) - self.client.set_timeout(5.0) - self.world = self.client.get_world() - - settings = self.world.get_settings() - settings.fixed_delta_seconds = 0.05 - - if config["sync"]: - settings.synchronous_mode = True - else: - print(f"activo async") - settings.synchronous_mode = False - - self.world.apply_settings(settings) - - self.camera = None - self.vehicle = None - self.display = None - self.image = None - - self.timer = CustomTimer() - self.time_processing = 0.0 - self.tics_processing = 0 - - self.image_dict = {} - - self.blueprint_library = self.world.get_blueprint_library() - self.model_3 = self.blueprint_library.filter("model3")[0] - # car_bp = self.blueprint_library.filter("vehicle.*")[0] - - ## --- Pygame Display - pygame.init() - pygame.font.init() - self.gameDisplay = pygame.display.set_mode( - (1500, 800), pygame.HWSURFACE | pygame.DOUBLEBUF - ) - self.clock = pygame.time.Clock() - self.surface = None - def __del__(self): print("__del__ called") - def reset(self): - """ - reset for - - Algorithm: Q-learn - - State: Simplified perception - - tasks: FollowLane - """ - - self.collision_hist = [] - self.actor_list = [] - self.image_dict = {} - - ## --- CAR - self.setup_car() - """ - self.transform = random.choice(self.world.get_map().get_spawn_points()) - self.vehicle = self.world.spawn_actor(self.model_3, self.transform) - while self.vehicle is None: - self.vehicle = self.world.spawn_actor(self.model_3, self.transform) - self.actor_list.append(self.vehicle) - time.sleep(2.0) - """ - - ## --- CAMERA FRONT - self.rgb_cam = self.blueprint_library.find("sensor.camera.rgb") - self.rgb_cam.set_attribute("image_size_x", "640") - self.rgb_cam.set_attribute("image_size_y", "480") - self.rgb_cam.set_attribute("fov", "110") - transform = carla.Transform( - carla.Location(x=2.5, z=0.7), carla.Rotation(yaw=+00) - ) - self.sensor_front_camera = self.world.spawn_actor( - self.rgb_cam, transform, attach_to=self.vehicle - ) - ## In case of problem in getting rid off sensors #### - ## We need to pass the lambda a weak reference to self to avoid circular - ## reference. - # weak_self = weakref.ref(self) - # self.sensor_front_camera.listen( - # lambda data: FollowLaneQlearnStaticWeatherNoTraffic._weak_process_image( - # weak_self, data - # ) - # ) - self.sensor_front_camera.listen(lambda data: self.process_image(data)) - while self.sensor_front_camera is None: - time.sleep(0.01) - self.actor_list.append(self.sensor_front_camera) - self.vehicle.apply_control(carla.VehicleControl(throttle=0.0, brake=0.0)) - time.sleep(2.0) - - ## --- Collision Sensor - colsensor = self.blueprint_library.find("sensor.other.collision") - self.colsensor = self.world.spawn_actor( - colsensor, transform, attach_to=self.vehicle - ) - self.actor_list.append(self.colsensor) - self.colsensor.listen(lambda event: self.collision_data(event)) - - self.episode_start = time.time() - self.vehicle.apply_control(carla.VehicleControl(throttle=0.0, brake=0.0)) - - #### VAMOs a enganar al step - stados = random.randint(0, 16) - stados = [stados] - # print(f"stados = {stados}") - return stados - - # return self.front_camera - - def setup_car(self): - self.transform = random.choice(self.world.get_map().get_spawn_points()) - self.vehicle = self.world.spawn_actor(self.model_3, self.transform) - while self.vehicle is None: - self.vehicle = self.world.spawn_actor(self.model_3, self.transform) - self.actor_list.append(self.vehicle) - time.sleep(2.0) - - def process_image(self, image): - """Convert a CARLA raw image to a BGRA numpy array.""" - print(f"--- reading image ------------------") - if not isinstance(image, carla.Image): - raise ValueError("Argument must be a carla.Image") - t_start = self.timer.time() - - image.convert(carla.ColorConverter.Raw) - array = np.frombuffer(image.raw_data, dtype=np.dtype("uint8")) - array = np.reshape(array, (image.height, image.width, 4)) - array = array[:, :, :3] - array = array[:, :, ::-1] - self.image_dict["image"] = array - - # if self.display_manager.render_enabled(): - self.surface = pygame.surfarray.make_surface(array.swapaxes(0, 1)) - - t_end = self.timer.time() - self.time_processing += t_end - t_start - self.tics_processing += 1 - if self.surface is not None: - # offset = self.display_manager.get_display_offset([0, 0]) - self.gameDisplay.blit(self.surface, (640, 480)) - self.clock.tick(60) - - # print(f"self.image_dict = {self.image_dict}") - # cv2.imshow("", array) - # cv2.waitKey(1) - # time.sleep(0.1) - self.front_camera = array - - """ - def render(self): - if self.surface is not None: - offset = self.display_manager.get_display_offset([0, 0]) - self.display_manager.display.blit(self.surface, offset) - """ - @staticmethod def _weak_process_image(weak_self, image): """Convert a CARLA raw image to a BGRA numpy array.""" @@ -788,347 +562,3 @@ def _weak_process_image(weak_self, image): cv2.waitKey(1) time.sleep(0.1) self.front_camera = image3 - - def collision_data(self, event): - self.collision_hist.append(event) - - def destroy_all_actors(self): - for actor in self.actor_list[::-1]: - actor.destroy() - - ################################################# - ################################################# - def step(self, action): - - # print(f"entramos en step()") - ### -------- send action - params = self.control(action) - # print(f"params = {params}") - - ### -------- State get center lane - # weak_self = weakref.ref(self) - # self.sensor_front_camera.listen( - # lambda data: FollowLaneQlearnStaticWeatherNoTraffic.process_image_weak( - # weak_self, data - # ) - # ) - # self.sensor_front_camera.listen(lambda data: self.process_image(data)) - # time.sleep(2.0) - # while self.sensor_front_camera is None: - # time.sleep(0.01) - # print(f"entro") - - # params["pos"] = 270 - center = 270 - stados = random.randint(0, 4) - stados = [stados] - # print(f"stados = {stados}") - ## -------- Rewards - reward, done = self.rewards_followlane_centerline(center) - - return stados, reward, done, {} - - def control(self, action): - - steering_angle = 0 - if action == 0: - self.vehicle.apply_control(carla.VehicleControl(throttle=0.32, steer=-0.2)) - steering_angle = 0.2 - elif action == 1: - self.vehicle.apply_control(carla.VehicleControl(throttle=0.7, steer=0.0)) - steering_angle = 0 - elif action == 2: - self.vehicle.apply_control(carla.VehicleControl(throttle=0.32, steer=0.2)) - steering_angle = 0.2 - # elif action == 3: - # self.vehicle.apply_control(carla.VehicleControl(throttle=0.2, steer=-0.4)) - # steering_angle = 0.4 - # elif action == 4: - # self.vehicle.apply_control(carla.VehicleControl(throttle=0.2, steer=0.4)) - # steering_angle = 0.4 - else: - print("error en action") - pass - params = {} - - v = self.vehicle.get_velocity() - params["velocity"] = math.sqrt(v.x**2 + v.y**2 + v.z**2) - - w = self.vehicle.get_angular_velocity() - params["steering_angle"] = steering_angle - - return params - - def rewards_followlane_centerline(self, center): - """ - works perfectly - rewards in function of center of Line - """ - done = False - if 0.65 >= center > 0.25: - reward = 10 - elif (0.9 > center > 0.65) or (0.25 >= center > 0): - reward = 2 - elif 0 >= center > -0.9: - reward = 1 - else: - reward = -100 - done = True - - return reward, done - - ############################################################################################################ - - def AAAAA_reset(self): - """ - reset for - - Algorithm: Q-learn - - State: Simplified perception - - tasks: FollowLane - """ - if len(self.display_manager.get_sensor_list()) > 0: - print(f"destruyendo sensors_list[]") - print( - f"len self.display_manager.sensor_list = {len(self.display_manager.sensor_list)}" - ) - self.display_manager.destroy() - - # if len(self.actor_list) > 0: - # print(f"destruyendo actors_list[]") - # for actor in self.actor_list: - # actor.destroy() - - self.client.apply_batch( - [carla.command.DestroyActor(x) for x in self.actor_list] - ) - - self.collision_hist = [] - self.actor_list = [] - print(f"----1. in reset() len(self.actor_list) = {len(self.actor_list)}") - # self.display_manager.actor_list = [] - time.sleep(1) - - ## ----------------------------------------------- COCHE - car_bp = self.world.get_blueprint_library().filter("vehicle.*")[0] - location = random.choice(self.world.get_map().get_spawn_points()) - self.car = self.world.try_spawn_actor(car_bp, location) - while self.car is None: - # print(f"entro here {datetime.now()}") - self.car = self.world.try_spawn_actor(car_bp, location) - self.actor_list.append(self.car) - ## ----------------------------------------------- CAMERA FRONT - self.rgb_cam = self.world.get_blueprint_library().find("sensor.camera.rgb") - self.rgb_cam.set_attribute("image_size_x", f"640") - self.rgb_cam.set_attribute("image_size_y", f"480") - self.rgb_cam.set_attribute("fov", f"110") - transform = carla.Transform( - carla.Location(x=2.5, z=0.7), carla.Rotation(yaw=+00) - ) - self.front_camera = self.world.spawn_actor( - self.rgb_cam, transform, attach_to=self.car - ) - self.actor_list.append(self.front_camera) - - # We need to pass the lambda a weak reference to self to avoid circular - # reference. - # weak_self = weakref.ref(self) - # self.front_camera.listen( - # lambda data: FollowLaneQlearnStaticWeatherNoTraffic.process_img( - # weak_self, data - # ) - # ) - self.front_camera.listen(lambda data: self.process_img(data)) - while self.front_camera is None: - time.sleep(0.01) - ## ----------------------------------------------- CAMERA FRONT - camera = SensorManager( - # SensorManager( - self.world, - self.display_manager, - "RGBCamera", - carla.Transform(carla.Location(x=-4, z=2.4), carla.Rotation(yaw=+00)), - self.car, - {}, - display_pos=[0, 0], - ) - # self.actor_list.append(camera.actor_list[0]) - # --------------- actuator - self.car.set_autopilot(True) - - for actor in self.actor_list: - print(f"in reset - actor in self.actor_list: {actor} \n") - - for actor in self.display_manager.sensor_list: - print(f"in reset - actor in self.display_manager.sensor_list: {actor} \n") - - # for actor in sensor_manager.actor_list: - # print(f"in reset - actor: {actor} \n") - # for actor in self.display_manager.actor_list: - # print(f"in reset - actor in self.display_manager.actor_list: {actor}\n") - - return self.front_camera - - def _____reset(self): - """ - esta funcionando pero no mata los sensores de SensorManager - reset for - - Algorithm: Q-learn - - State: Simplified perception - - tasks: FollowLane - """ - - print(f"\nin reset()\n") - # if len(self.bsc.actor_list) > 0: - # print(f"destruyendo actors_list[]") - # for actor in self.bsc.actor_list: - # actor.destroy() - # sensor_manager = SensorManager() - - self.client.apply_batch( - [carla.command.DestroyActor(x) for x in self.actor_list] - ) - self.client.apply_batch( - [carla.command.DestroyActor(x) for x in self.display_manager.actor_list] - ) - print( - f"----in reset() len(self.actor_list) = {len(self.actor_list)} y len(self.display_manager.actor_list) = {len(self.display_manager.actor_list)}" - ) - self.collision_hist = [] - self.actor_list = [] - self.display_manager.actor_list = [] - time.sleep(2) - - ## ----------------------------------------------- COCHE - car_bp = self.world.get_blueprint_library().filter("vehicle.*")[0] - location = random.choice(self.world.get_map().get_spawn_points()) - self.car = self.world.try_spawn_actor(car_bp, location) - while self.car is None: - # print(f"entro here {datetime.now()}") - self.car = self.world.try_spawn_actor(car_bp, location) - self.actor_list.append(self.car) - - ## ----------------------------------------------- CAMERA FRONT - self.rgb_cam = self.world.get_blueprint_library().find("sensor.camera.rgb") - self.rgb_cam.set_attribute("image_size_x", f"640") - self.rgb_cam.set_attribute("image_size_y", f"480") - self.rgb_cam.set_attribute("fov", f"110") - transform = carla.Transform( - carla.Location(x=2.5, z=0.7), carla.Rotation(yaw=+00) - ) - self.front_camera = self.world.spawn_actor( - self.rgb_cam, transform, attach_to=self.car - ) - self.actor_list.append(self.front_camera) - - # We need to pass the lambda a weak reference to self to avoid circular - # reference. - # weak_self = weakref.ref(self) - # self.front_camera.listen( - # lambda data: FollowLaneQlearnStaticWeatherNoTraffic.process_img( - # weak_self, data - # ) - # ) - self.front_camera.listen(lambda data: self.process_img(data)) - while self.front_camera is None: - time.sleep(0.01) - - ## ----------------------------------------------- CAMERA SERGIO - self.sergio_camera = self.world.get_blueprint_library().find( - "sensor.camera.rgb" - ) - self.sergio_camera.set_attribute("image_size_x", f"640") - self.sergio_camera.set_attribute("image_size_y", f"480") - self.sergio_camera.set_attribute("fov", f"110") - transformsergiocamera = carla.Transform( - carla.Location(x=2.5, z=0.7), carla.Rotation(yaw=+00) - ) - self.front_camera_sergio = self.world.spawn_actor( - self.sergio_camera, transformsergiocamera, attach_to=self.car - ) - self.actor_list.append(self.front_camera_sergio) - - # We need to pass the lambda a weak reference to self to avoid circular - # reference. - weak_self_sergio = weakref.ref(self) - self.front_camera_sergio.listen( - lambda data: FollowLaneQlearnStaticWeatherNoTraffic.process_img_sergio( - weak_self_sergio, data - ) - ) - - while self.front_camera is None: - time.sleep(0.01) - - # --------------- actuator - self.car.set_autopilot(True) - - # self.display_manager.add_sensor(self.front_camera) - # self.display_manager.render() - - # ---------------------------------- SHOW UP - - ### TODO: si se bloquea despues de muchos epochs, debe ser porque no esta eliminando estos sensores - SensorManager( - # SensorManager( - self.world, - self.display_manager, - "RGBCamera", - carla.Transform(carla.Location(x=-4, z=2.4), carla.Rotation(yaw=+00)), - self.car, - {}, - display_pos=[0, 0], - ) - # self.actor_list.append(self.camera.actor_list[0]) - - for actor in self.actor_list: - print(f"in reset - actor in self.actor_list: {actor} \n") - # for actor in sensor_manager.actor_list: - # print(f"in reset - actor: {actor} \n") - for actor in self.display_manager.actor_list: - print(f"in reset - actor in self.display_manager.actor_list: {actor}\n") - - SensorManager( - self.world, - self.display_manager, - "SemanticCameraSergio", - carla.Transform(carla.Location(x=2, z=1), carla.Rotation(yaw=+00)), - self.car, - {}, - display_pos=[1, 2], - ) - - return self.front_camera - - #################################################### - #################################################### - - """ - def process_img(self, image): - print( - f"\n\nholaaaaaaaaaaaaaa-----------------------------------------------------------\n\n" - ) - """ - """ - @staticmethod - def process_img(weak_self, image): - self = weak_self - if not self: - return - - i = np.array(image.raw_data) - # print(i.shape) - i2 = i.reshape((480, 640, 4)) - i3 = i2[:, :, :3] - cv2.imshow("", i3) - cv2.waitKey(1) - self.front_camera = i3 - """ - - @staticmethod - def process_img_sergio(weak_self, image): - """ - esta es la funcion callback que procesa la imagen y la segmenta - """ - print(image) - print("process_img_sergio") - pass diff --git a/rl_studio/envs/carla/followlane/utils.py b/rl_studio/envs/carla/followlane/utils.py index d4df1a3d4..5473c6d64 100644 --- a/rl_studio/envs/carla/followlane/utils.py +++ b/rl_studio/envs/carla/followlane/utils.py @@ -8,7 +8,7 @@ def __init__(self): @staticmethod def show_image_with_centrals( - name, img, waitkey, centrals_in_pixels, centrals_normalized, x_row + name, img, waitkey, centrals_in_pixels, centrals_normalized, x_row, x, y ): window_name = f"{name}" img = np.array(img) @@ -17,7 +17,10 @@ def show_image_with_centrals( cv2.putText( img, str(f"{int(centrals_in_pixels[index])}"), - (int(centrals_in_pixels[index]) + 20, int(x_row[index])), + ( + (img.shape[1] // 2) + int(centrals_in_pixels[index]) + 20, + int(x_row[index]), + ), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), @@ -27,7 +30,7 @@ def show_image_with_centrals( cv2.putText( img, str(f"[{centrals_normalized[index]}]"), - (320, int(x_row[index])), + (img.shape[1] - 50, int(x_row[index])), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), @@ -38,22 +41,26 @@ def show_image_with_centrals( img, (0, int(x_row[index])), (int(img.shape[1]), int(x_row[index])), - color=(10, 10, 10), + color=(200, 200, 200), thickness=1, ) cv2.line( img, (int(img.shape[1] // 2), 0), (int(img.shape[1] // 2), int(img.shape[0])), - color=(30, 30, 30), + color=(200, 200, 200), thickness=1, ) + cv2.namedWindow(window_name) # Create a named window + cv2.moveWindow(window_name, x, y) # Move it to (40,30) cv2.imshow(window_name, img) cv2.waitKey(waitkey) @staticmethod - def show_image(name, img, waitkey): + def show_image(name, img, waitkey, x, y): window_name = f"{name}" + cv2.namedWindow(window_name) # Create a named window + cv2.moveWindow(window_name, x, y) # Move it to (40,30) cv2.imshow(window_name, img) cv2.waitKey(waitkey) From b0a504e8ff048f5e43176844149a849a5c3ea51b Mon Sep 17 00:00:00 2001 From: pjfernandecabo Date: Tue, 7 Mar 2023 16:31:29 -0500 Subject: [PATCH 02/63] first training steps: fixing distance to center and clipping Throttle --- ...nfig_training_followlane_qlearn_carla.yaml | 4 +- .../carla/followlane/followlane_qlearn.py | 93 ++++++++++++++----- 2 files changed, 72 insertions(+), 25 deletions(-) diff --git a/rl_studio/config/config_training_followlane_qlearn_carla.yaml b/rl_studio/config/config_training_followlane_qlearn_carla.yaml index 62399e338..9f7e36abb 100644 --- a/rl_studio/config/config_training_followlane_qlearn_carla.yaml +++ b/rl_studio/config/config_training_followlane_qlearn_carla.yaml @@ -7,7 +7,7 @@ settings: env: follow_lane # Town01, simple, nurburgring, montreal, curves, simple_laser, manual, autoparking agent: auto_carla # f1, autoparkingRL, auto_carla, mountain_car, robot_mesh, cartpole, turtlebot actions: carla_simple # simple, medium, hard, test, autoparking_simple - states: sp4 # sp1 (simplified perception with 1 point), sp3 (simplified perception with 3 points), spn (simplified perception with n points) + states: sp1 # sp1 (simplified perception with 1 point), sp3 (simplified perception with 3 points), spn (simplified perception with n points) rewards: follow_right_lane_only_center # framework: _ total_episodes: 100 @@ -115,7 +115,7 @@ carla_environments: traffic_pedestrians: False city_lights: False car_lights: False - estimated_steps: 100 + estimated_steps: 500 save_episodes: 10 save_every_step: 1000 init_pose: diff --git a/rl_studio/envs/carla/followlane/followlane_qlearn.py b/rl_studio/envs/carla/followlane/followlane_qlearn.py index 6bff298d7..deb12be5e 100644 --- a/rl_studio/envs/carla/followlane/followlane_qlearn.py +++ b/rl_studio/envs/carla/followlane/followlane_qlearn.py @@ -78,7 +78,7 @@ def __init__(self, **config): self.perfect_distance_pixels = None self.perfect_distance_normalized = None - self._control = carla.VehicleControl() + # self._control = carla.VehicleControl() self.params = {} def reset(self): @@ -207,7 +207,11 @@ def reset(self): # # Reset Methods #################################################### + def calculate_states(self, mask): + """ + from right, search for red line. It could be weakness to a bad masking + """ width = mask.shape[1] center_image = width // 2 ## get total lines in every line point @@ -225,9 +229,9 @@ def calculate_states(self, mask): width - inv_index_right[x] - center_image for x, _ in enumerate(inv_index_right) ] - ## normalized distances + ## normalized distances NO ABS distance_to_center_normalized = [ - abs(float((center_image - index_right[i]) / center_image)) + float((center_image - index_right[i]) / center_image) for i, _ in enumerate(index_right) ] pixels_in_state = mask.shape[1] / self.num_regions @@ -394,17 +398,21 @@ def step(self, action): counter_states = Counter(states) states_16 = counter_states.get(16) + done = False + ## -------- Rewards - reward = self.rewards_easy(error, self.params) + reward, done = self.rewards_easy(error, self.params) + # reward, done = self.rewards_followlane_error_center( + # distance_to_center_normalized, self.rewards + # ) - ## ----- hacemos la salida - done = False + ## -------- Finish by... if states_16 is not None and ( states_16 > (len(states) // 2) - ): # salimos porque no detecta linea a la derecha + ): # not red right line print(f"no red line detected") done = True - if len(self.collision_hist) > 0: # te has chocado, baby + if len(self.collision_hist) > 0: # crash you, baby done = True print(f"crash") @@ -418,6 +426,9 @@ def step(self, action): Brake=self.params["Brake"], height=self.params["height"], _="------------------------", + states=states, + distance_to_center=distance_to_center, + distance_to_center_normalized=distance_to_center_normalized, reward=reward, done=done, ) @@ -451,22 +462,22 @@ def control(self, action): steering_angle = 0 if action == 0: - # self.car.apply_control(carla.VehicleControl(throttle=1, steer=-0.2)) - self._control.throttle = min(self._control.throttle + 0.1, 1.00) - self._control.steer = -0.2 - # steering_angle = -0.2 + self.car.apply_control(carla.VehicleControl(throttle=0.3, steer=-0.1)) + # self._control.throttle = min(self._control.throttle + 0.01, 1.00) + # self._control.steer = -0.02 + steering_angle = -0.02 elif action == 1: - # self.car.apply_control(carla.VehicleControl(throttle=1, steer=0.0)) - self._control.throttle = min(self._control.throttle + 0.1, 1.00) - self._control.steer = 0.0 - # steering_angle = 0 + self.car.apply_control(carla.VehicleControl(throttle=0.6, steer=0.0)) + # self._control.throttle = min(self._control.throttle + 0.01, 1.00) + # self._control.steer = 0.0 + steering_angle = 0 elif action == 2: - # self.car.apply_control(carla.VehicleControl(throttle=1, steer=0.2)) - self._control.throttle = min(self._control.throttle + 0.1, 1.00) - self._control.steer = 0.2 - # steering_angle = 0.2 + self.car.apply_control(carla.VehicleControl(throttle=0.3, steer=-0.1)) + # self._control.throttle = min(self._control.throttle + 0.01, 1.0) + # self._control.steer = 0.02 + steering_angle = 0.02 - self.car.apply_control(self._control) + # self.car.apply_control(self._control) t = self.car.get_transform() v = self.car.get_velocity() @@ -478,7 +489,7 @@ def control(self, action): # print(f"{self.params['steering_angle'].y = }") # print(f"{self.params['steering_angle'].z = }") - # self.params["steering_angle"] = steering_angle + self.params["Steering_angle"] = steering_angle self.params["Steer"] = c.steer self.params["location"] = (t.location.x, t.location.y) self.params["Throttle"] = c.throttle @@ -503,8 +514,43 @@ def rewards_followlane_dist_v_angle(self, error, params): return function_reward + def rewards_followline_center(self, center, rewards): + """ + original for Following Line + """ + done = False + if center > 0.9: + done = True + reward = rewards["penal"] + elif 0 <= center <= 0.2: + reward = rewards["from_10"] + elif 0.2 < center <= 0.4: + reward = rewards["from_02"] + else: + reward = rewards["from_01"] + + return reward, done + + def rewards_followlane_error_center(self, error, rewards): + """ + original for Following Line + """ + done = False + if error > 0.9: + done = True + reward = rewards["penal"] + elif 0 <= error <= 0.2: + reward = rewards["from_10"] + elif 0.2 < error <= 0.4: + reward = rewards["from_02"] + else: + reward = rewards["from_01"] + + return reward, done + def rewards_easy(self, error, params): rewards = [] + done = False for i, _ in enumerate(error): if error[i] < 0.2: rewards.append(10) @@ -514,6 +560,7 @@ def rewards_easy(self, error, params): rewards.append(1) else: rewards.append(-100) + done = True function_reward = sum(rewards) / len(rewards) @@ -521,7 +568,7 @@ def rewards_easy(self, error, params): # function_reward += params["velocity"] * 0.5 # function_reward -= params["steering_angle"] * 1.02 - return function_reward + return function_reward, done def rewards_followlane_center_v_w(self): """esta sin terminar""" From aa3b4781a4946744895b82f9913364a30d3eecf3 Mon Sep 17 00:00:00 2001 From: pjfernandecabo Date: Thu, 9 Mar 2023 16:50:40 -0500 Subject: [PATCH 03/63] first correct training --- .../train_followlane_qlearn_carla.py | 212 ++++++++++++++++-- rl_studio/agents/f1/loaders.py | 4 +- rl_studio/agents/utils.py | 59 ++++- rl_studio/algorithms/qlearn.py | 6 +- ...nfig_training_followlane_qlearn_carla.yaml | 7 +- .../carla/followlane/followlane_qlearn.py | 159 +++++-------- rl_studio/envs/carla/followlane/rewards.py | 79 +++++-- rl_studio/envs/carla/followlane/settings.py | 1 + rl_studio/envs/carla/followlane/utils.py | 23 ++ 9 files changed, 403 insertions(+), 147 deletions(-) diff --git a/rl_studio/agents/auto_carla/train_followlane_qlearn_carla.py b/rl_studio/agents/auto_carla/train_followlane_qlearn_carla.py index d84686848..13b37cb88 100755 --- a/rl_studio/agents/auto_carla/train_followlane_qlearn_carla.py +++ b/rl_studio/agents/auto_carla/train_followlane_qlearn_carla.py @@ -19,7 +19,9 @@ ) from rl_studio.agents.utils import ( render_params, + render_params_left_bottom, save_dataframe_episodes, + save_carla_dataframe_episodes, save_batch, save_best_episode, LoggingHandler, @@ -96,6 +98,11 @@ def main(self): log = LoggingHandler(self.log_file) env = gym.make(self.env_params.env_name, **self.environment.environment) + start_time = datetime.now() + best_epoch = 1 + current_max_reward = 0 + best_step = 0 + best_epoch_training_time = 0 epsilon = self.environment.environment["epsilon"] epsilon_decay = epsilon / (self.env_params.total_episodes) # epsilon = epsilon / 2 @@ -143,6 +150,7 @@ def main(self): done = False cumulated_reward = 0 step = 0 + start_time_epoch = datetime.now() observation = env.reset() state = "-".join(map(str, observation)) @@ -164,11 +172,14 @@ def main(self): action = qlearn.select_action(state) # print(f"{action = }") new_observation, reward, done, _ = env.step(action) + # time.sleep(4) cumulated_reward += reward next_state = "-".join(map(str, new_observation)) + """ print_messages( "", + episode=episode, step=step, action=action, state=state, @@ -176,37 +187,86 @@ def main(self): reward=reward, done=done, next_state=next_state, - # state=state, + current_max_reward=current_max_reward, ) + """ + """ + log.logger.info( + f"\n step = {step}\n" + f"action = {action}\n" + f"actions type = {type(action)}\n" + f"state = {state}\n" + # f"observation[0]= {observation[0]}\n" + # f"observation type = {type(observation)}\n" + # f"observation[0] type = {type(observation[0])}\n" + f"new_observation = {new_observation}\n" + f"type new_observation = {type(new_observation)}\n" + f"reward = {reward}\n" + f"done = {done}\n" + f"next_state = {next_state}\n" + f"type new_observation = {type(new_observation)}\n" + f"current_max_reward = {current_max_reward}\n" + ) + """ qlearn.learn(state, action, reward, next_state) state = next_state - print_messages( - "", - next_state=next_state, - state=state, - ) + + # print_messages( + # "", + # next_state=next_state, + # state=state, + # ) env.display_manager.render() # render params - """ - render_params( - action=action, + + render_params_left_bottom( episode=episode, step=step, - v=self.global_params.actions_set[action][ - 0 - ], # this case for discrete - w=self.global_params.actions_set[action][ - 1 - ], # this case for discrete + action=action, epsilon=epsilon, - observation=observation, + new_observation=new_observation, reward_in_step=reward, cumulated_reward=cumulated_reward, + _="------------------------", current_max_reward=current_max_reward, + best_epoch=best_epoch, + best_step=best_step, done=done, ) - """ + + # best episode and step's stats + if current_max_reward <= cumulated_reward: + current_max_reward = cumulated_reward + best_epoch = episode + best_step = step + best_epoch_training_time = datetime.now() - start_time_epoch + self.global_params.actions_rewards["episode"].append(episode) + self.global_params.actions_rewards["step"].append(step) + self.global_params.actions_rewards["reward"].append(reward) + # Showing stats in screen for monitoring. Showing every 'save_every_step' value + if not step % self.env_params.save_every_step: + log.logger.info( + f"SHOWING BATCH OF STEPS\n" + f"current_max_reward = {current_max_reward}\n" + f"cumulated_reward = {cumulated_reward}\n" + f"current epoch = {episode}\n" + f"current step = {step}\n" + f"best epoch so far = {best_epoch}\n" + f"best step so far = {best_step}\n" + f"best_epoch_training_time = {best_epoch_training_time}\n" + ) + print_messages( + "SHOWING BATCH OF STEPS", + episode=episode, + step=step, + cumulated_reward=cumulated_reward, + epsilon=epsilon, + best_epoch=best_epoch, + best_step=best_step, + current_max_reward=current_max_reward, + best_epoch_training_time=best_epoch_training_time, + ) # End epoch if step > self.env_params.estimated_steps: done = True @@ -214,7 +274,125 @@ def main(self): f"{self.global_params.models_dir}/{time.strftime('%Y%m%d-%H%M%S')}_Circuit-{self.environment.environment['town']}_States-{self.environment.environment['states']}_Actions-{self.environment.environment['action_space']}_Rewards-{self.environment.environment['reward_function']}_epsilon-{round(epsilon,3)}_epoch-{episode}_step-{step}_reward-{int(cumulated_reward)}-qtable.npy", qlearn.q_table, ) + print_messages( + "EPISODE COMPLETED", + episode=episode, + step=step, + cumulated_reward=cumulated_reward, + epsilon=epsilon, + ) + log.logger.info( + f"\nEPISODE COMPLETED\n" + f"in episode = {episode}\n" + f"steps = {step}\n" + f"cumulated_reward = {cumulated_reward}\n" + f"epsilon = {epsilon}\n" + ) + # Save best lap + if ( + cumulated_reward - self.environment.environment["rewards"]["penal"] + ) >= current_max_reward: + self.global_params.best_current_epoch["best_epoch"].append(best_epoch) + self.global_params.best_current_epoch["highest_reward"].append( + cumulated_reward + ) + self.global_params.best_current_epoch["best_step"].append(best_step) + self.global_params.best_current_epoch[ + "best_epoch_training_time" + ].append(best_epoch_training_time) + self.global_params.best_current_epoch[ + "current_total_training_time" + ].append(datetime.now() - start_time) + save_carla_dataframe_episodes( + self.environment.environment, + self.global_params.metrics_data_dir, + self.global_params.best_current_epoch, + ) + + np.save( + f"{self.global_params.models_dir}/{time.strftime('%Y%m%d-%H%M%S')}_Circuit-{self.environment.environment['town']}_States-{self.environment.environment['states']}_Actions-{self.environment.environment['action_space']}_Rewards-{self.environment.environment['reward_function']}_epsilon-{round(epsilon,3)}_epoch-{episode}_step-{step}_reward-{int(cumulated_reward - self.environment.environment['rewards']['penal'])}-qtable.npy", + qlearn.q_table, + ) + log.logger.info( + f"\nsaving best lap\n" + f"in episode = {episode}\n" + f"cumulated_reward = {cumulated_reward}\n" + f"current_max_reward = {current_max_reward}\n" + f"steps = {step}\n" + f"epsilon = {epsilon}\n" + ) + print_messages( + "saving best lap", + episode=episode, + cumulated_reward=cumulated_reward, + current_max_reward=current_max_reward, + step=step, + epsilon=epsilon, + ) + + # end of training by: + # training time setting: 2 hours, 15 hours... + # num epochs + + if ( + datetime.now() - timedelta(hours=self.global_params.training_time) + > start_time + ) or (episode > self.env_params.total_episodes): + if ( + cumulated_reward - self.environment.environment["rewards"]["penal"] + ) >= current_max_reward: + np.save( + f"{self.global_params.models_dir}/{time.strftime('%Y%m%d-%H%M%S')}_Circuit-{self.environment.environment['town']}_States-{self.environment.environment['states']}_Actions-{self.environment.environment['action_space']}_Rewards-{self.environment.environment['reward_function']}_epsilon-{round(epsilon,3)}_epoch-{episode}_step-{step}_reward-{int(cumulated_reward)}-qtable.npy", + qlearn.q_table, + ) + log.logger.info( + f"\nTraining Time over\n" + f"current_max_reward = {current_max_reward}\n" + f"epoch = {episode}\n" + f"step = {step}\n" + f"epsilon = {epsilon}\n" + ) + print_messages( + "Training Time over", + episode=episode, + cumulated_reward=cumulated_reward, + current_max_reward=current_max_reward, + step=step, + epsilon=epsilon, + ) + break + + # save best values every save_episode times + self.global_params.ep_rewards.append(cumulated_reward) + if not episode % self.env_params.save_episodes: + self.global_params.aggr_ep_rewards = save_batch( + episode, + step, + start_time_epoch, + start_time, + self.global_params, + self.env_params, + ) + save_carla_dataframe_episodes( + self.environment.environment, + self.global_params.metrics_data_dir, + self.global_params.aggr_ep_rewards, + ) + log.logger.info( + f"\nsaving BATCH\n" + f"current_max_reward = {current_max_reward}\n" + f"best_epoch = {best_epoch}\n" + f"best_step = {best_step}\n" + f"best_epoch_training_time = {best_epoch_training_time}\n" + ) + print_messages( + "aving BATCH", + best_epoch=best_epoch, + best_epoch_training_time=best_epoch_training_time, + current_max_reward=current_max_reward, + best_step=best_step, + ) # updating epsilon for exploration if epsilon > self.environment.environment["epsilon_min"]: # self.epsilon *= self.epsilon_discount diff --git a/rl_studio/agents/f1/loaders.py b/rl_studio/agents/f1/loaders.py index fbecbc348..a195976ab 100644 --- a/rl_studio/agents/f1/loaders.py +++ b/rl_studio/agents/f1/loaders.py @@ -577,7 +577,9 @@ def __init__(self, config) -> None: self.environment["waypoints_road_id"] = config[self.environment_set][self.env][ "waypoints_road_id" ] - + self.environment["max_target_waypoint_distance"] = config[self.environment_set][ + self.env + ]["max_target_waypoint_distance"] # --------- Image self.environment["height_image"] = config["agents"][self.agent][ "camera_params" diff --git a/rl_studio/agents/utils.py b/rl_studio/agents/utils.py index 92cb06c0e..0cb6e7d90 100644 --- a/rl_studio/agents/utils.py +++ b/rl_studio/agents/utils.py @@ -21,12 +21,12 @@ def __init__(self, log_file): c_handler = logging.StreamHandler() f_handler = logging.FileHandler(log_file) - c_handler.setLevel(logging.INFO) + c_handler.setLevel(logging.DEBUG) f_handler.setLevel(logging.INFO) # Create formatters and add it to handlers c_format = logging.Formatter("%(name)s - %(levelname)s - %(message)s") - f_format: Formatter = logging.Formatter( + f_format = logging.Formatter( "[%(levelname)s] - %(asctime)s, filename: %(filename)s, funcname: %(funcName)s, line: %(lineno)s\n messages ---->\n %(message)s" ) c_handler.setFormatter(c_format) @@ -119,6 +119,38 @@ def render_params(**kwargs): cv2.waitKey(100) +def render_params_left_bottom(**kwargs): + font = cv2.FONT_HERSHEY_SIMPLEX + canvas = np.zeros((400, 500, 3), dtype="uint8") + # blue = (255, 0, 0) + # green = (0, 255, 0) + # red = (0, 0, 255) + white = (255, 255, 255) + # white_darkness = (200, 200, 200) + i = 10 + for key, value in kwargs.items(): + cv2.putText( + canvas, + str(f"{key}: {value}"), + (20, i + 25), + font, + 0.5, + white, + 1, + cv2.LINE_AA, + ) + i += 25 + + window_name = "Stats Board" + cv2.namedWindow(window_name) # Create a named window + cv2.moveWindow(window_name, 0, 00) # Move it to (40,30) + cv2.imshow(window_name, canvas) + cv2.waitKey(100) + + +6 + + def save_dataframe_episodes(environment, outdir, aggr_ep_rewards, actions_rewards=None): """ We save info every certains epochs in a dataframe and .npy format to export or manage @@ -137,6 +169,29 @@ def save_dataframe_episodes(environment, outdir, aggr_ep_rewards, actions_reward np.save(file_npy, actions_rewards) +def save_carla_dataframe_episodes( + environment, outdir, aggr_ep_rewards, actions_rewards=None +): + """ + We save info every certains epochs in a dataframe and .npy format to export or manage + """ + os.makedirs(f"{outdir}", exist_ok=True) + + file_csv = f"{outdir}/{time.strftime('%Y%m%d-%H%M%S')}_Circuit-{environment['town']}_States-{environment['states']}_Actions-{environment['action_space']}_Rewards-{environment['reward_function']}.csv" + file_excel = f"{outdir}/{time.strftime('%Y%m%d')}_Circuit-{environment['town']}_States-{environment['states']}_Actions-{environment['action_space']}_Rewards-{environment['reward_function']}.xlsx" + + df = pd.DataFrame(aggr_ep_rewards) + df.to_csv(file_csv, mode="a", index=False, header=None) + + # with pd.ExcelWriter(file_excel, mode="a") as writer: + # df.to_excel(writer) + df.to_excel(file_excel) + + if actions_rewards is not None: + file_npy = f"{outdir}/{time.strftime('%Y%m%d-%H%M%S')}_Circuit-{environment['town']}_States-{environment['states']}_Actions-{environment['action_space']}_Rewards-{environment['reward_function']}.npy" + np.save(file_npy, actions_rewards) + + def save_best_episode( global_params, cumulated_reward, diff --git a/rl_studio/algorithms/qlearn.py b/rl_studio/algorithms/qlearn.py index 4de873cbd..8847f1537 100644 --- a/rl_studio/algorithms/qlearn.py +++ b/rl_studio/algorithms/qlearn.py @@ -312,11 +312,11 @@ def __init__(self, actions, epsilon, alpha, gamma): def select_action(self, state): q_list = [self.q_table.get((state, a), 0.0) for a in self.actions] - print(f"\n{q_list = }") + # print(f"\n{q_list = }") max_q = max(q_list) - print(f"{max_q = }") + # print(f"{max_q = }") count_max = q_list.count(max_q) - print(f"{count_max= }") + # print(f"{count_max= }") best_index = [index for index, value in enumerate(q_list) if value == max_q] if count_max > 1 or np.random.random() > self.epsilon: diff --git a/rl_studio/config/config_training_followlane_qlearn_carla.yaml b/rl_studio/config/config_training_followlane_qlearn_carla.yaml index 9f7e36abb..78351bdf4 100644 --- a/rl_studio/config/config_training_followlane_qlearn_carla.yaml +++ b/rl_studio/config/config_training_followlane_qlearn_carla.yaml @@ -10,7 +10,7 @@ settings: states: sp1 # sp1 (simplified perception with 1 point), sp3 (simplified perception with 3 points), spn (simplified perception with n points) rewards: follow_right_lane_only_center # framework: _ - total_episodes: 100 + total_episodes: 500 training_time: 6 models_dir: "./checkpoints" logs_dir: "./logs" @@ -115,9 +115,9 @@ carla_environments: traffic_pedestrians: False city_lights: False car_lights: False - estimated_steps: 500 + estimated_steps: 300 save_episodes: 10 - save_every_step: 1000 + save_every_step: 10 init_pose: goal_pose: filter: vehicle.* @@ -131,3 +131,4 @@ carla_environments: waypoints_target: 959 #961 waypoints_lane_id: -1 waypoints_road_id: 8 + max_target_waypoint_distance: 1 # distance to finish waypoint diff --git a/rl_studio/envs/carla/followlane/followlane_qlearn.py b/rl_studio/envs/carla/followlane/followlane_qlearn.py index deb12be5e..397a9adb9 100644 --- a/rl_studio/envs/carla/followlane/followlane_qlearn.py +++ b/rl_studio/envs/carla/followlane/followlane_qlearn.py @@ -56,7 +56,7 @@ def __init__(self, **config): self.original_settings = self.world.get_settings() self.traffic_manager = self.client.get_trafficmanager(8000) settings = self.world.get_settings() - settings.fixed_delta_seconds = 0.05 + settings.fixed_delta_seconds = 0.5 if config["sync"]: self.traffic_manager.set_synchronous_mode(True) else: @@ -70,8 +70,8 @@ def __init__(self, **config): ## -- display manager self.display_manager = DisplayManager( - grid_size=[2, 3], - window_size=[1500, 800], + grid_size=[2, 2], + window_size=[900, 600], ) self.car = None @@ -80,6 +80,7 @@ def __init__(self, **config): self.perfect_distance_normalized = None # self._control = carla.VehicleControl() self.params = {} + self.target_waypoint = None def reset(self): @@ -102,6 +103,14 @@ def reset(self): self.waypoints_lane_id, 2000, ) + self.target_waypoint = filtered_waypoints[-1] + # print(f"{self.target_waypoint=}") + # print(f"{self.target_waypoint.transform.location.x=}") + # print(f"{self.target_waypoint.transform.location.y=}") + # print(f"{self.target_waypoint.road_id=}") + # print(f"{self.target_waypoint.lane_id=}") + # print(f"{self.target_waypoint.id=}") + self.setup_car_fix_pose(init_waypoint) else: # TODO: hacer en el caso que se quiera poner el target con .next() @@ -167,7 +176,7 @@ def reset(self): carla.Transform(carla.Location(x=2, z=1.5), carla.Rotation(yaw=+0)), self.car, {}, - display_pos=[0, 2], + display_pos=[1, 0], ) time.sleep(1) @@ -253,7 +262,15 @@ def preprocess_image(self, red_mask): return white_mask + ######################################################## + # waypoints, car pose + # + ######################################################## + def draw_waypoints(self, spawn_points, init, target, lane_id, life_time): + """ + WArning: target always has to be the last waypoint, since it is the FINISH + """ filtered_waypoints = [] i = init for waypoint in spawn_points[init + 1 : target + 2]: @@ -398,24 +415,31 @@ def step(self, action): counter_states = Counter(states) states_16 = counter_states.get(16) + ## -------- Ending Step()... done = False - ## -------- Rewards - reward, done = self.rewards_easy(error, self.params) + reward, done = self.autocarlarewards.rewards_easy(error, self.params) # reward, done = self.rewards_followlane_error_center( # distance_to_center_normalized, self.rewards # ) - ## -------- Finish by... - if states_16 is not None and ( - states_16 > (len(states) // 2) - ): # not red right line + ## -------- ... or Finish by... + if states_16 is not None and (states_16 >= len(states)): # not red right line print(f"no red line detected") done = True if len(self.collision_hist) > 0: # crash you, baby done = True print(f"crash") + is_finish, dist = AutoCarlaUtils.finish_target( + self.params["location"], + self.target_waypoint, + self.max_target_waypoint_distance, + ) + if is_finish: + print(f"Finish!!!!") + done = True + render_params( action=action, speed_kmh=self.params["speed"], @@ -430,8 +454,13 @@ def step(self, action): distance_to_center=distance_to_center, distance_to_center_normalized=distance_to_center_normalized, reward=reward, + error=error, done=done, + states_16=states_16, + self_collision_hist=self.collision_hist, + distance_to_finish=dist, ) + """ print_messages( "in step()", height=mask.shape[0], @@ -455,27 +484,34 @@ def step(self, action): states_16=states_16, self_collision_hist=self.collision_hist, ) - + """ return states, reward, done, {} + def clip_throttle(self, throttle, curr_speed, target_speed): + """ + limits throttle in function of current speed and target speed + """ + clip_throttle = np.clip(throttle - 0.1 * (curr_speed - target_speed), 0.25, 1.0) + return clip_throttle + def control(self, action): steering_angle = 0 if action == 0: - self.car.apply_control(carla.VehicleControl(throttle=0.3, steer=-0.1)) + self.car.apply_control(carla.VehicleControl(throttle=0.3, steer=-0.01)) # self._control.throttle = min(self._control.throttle + 0.01, 1.00) # self._control.steer = -0.02 - steering_angle = -0.02 + steering_angle = -0.01 elif action == 1: self.car.apply_control(carla.VehicleControl(throttle=0.6, steer=0.0)) # self._control.throttle = min(self._control.throttle + 0.01, 1.00) # self._control.steer = 0.0 steering_angle = 0 elif action == 2: - self.car.apply_control(carla.VehicleControl(throttle=0.3, steer=-0.1)) + self.car.apply_control(carla.VehicleControl(throttle=0.3, steer=0.01)) # self._control.throttle = min(self._control.throttle + 0.01, 1.0) # self._control.steer = 0.02 - steering_angle = 0.02 + steering_angle = 0.01 # self.car.apply_control(self._control) @@ -496,95 +532,10 @@ def control(self, action): self.params["Brake"] = c.brake self.params["height"] = t.location.z - def rewards_followlane_dist_v_angle(self, error, params): - # rewards = [] - # for i,_ in enumerate(error): - # if (error[i] < 0.2): - # rewards.append(10) - # elif (0.2 <= error[i] < 0.4): - # rewards.append(2) - # elif (0.4 <= error[i] < 0.9): - # rewards.append(1) - # else: - # rewards.append(0) - rewards = [0.1 / error[i] for i, _ in enumerate(error)] - function_reward = sum(rewards) / len(rewards) - function_reward += math.log10(params["velocity"]) - function_reward -= 1 / (math.exp(params["steering_angle"])) - - return function_reward - - def rewards_followline_center(self, center, rewards): - """ - original for Following Line - """ - done = False - if center > 0.9: - done = True - reward = rewards["penal"] - elif 0 <= center <= 0.2: - reward = rewards["from_10"] - elif 0.2 < center <= 0.4: - reward = rewards["from_02"] - else: - reward = rewards["from_01"] - - return reward, done - - def rewards_followlane_error_center(self, error, rewards): - """ - original for Following Line - """ - done = False - if error > 0.9: - done = True - reward = rewards["penal"] - elif 0 <= error <= 0.2: - reward = rewards["from_10"] - elif 0.2 < error <= 0.4: - reward = rewards["from_02"] - else: - reward = rewards["from_01"] - - return reward, done - - def rewards_easy(self, error, params): - rewards = [] - done = False - for i, _ in enumerate(error): - if error[i] < 0.2: - rewards.append(10) - elif 0.2 <= error[i] < 0.4: - rewards.append(2) - elif 0.4 <= error[i] < 0.9: - rewards.append(1) - else: - rewards.append(-100) - done = True - - function_reward = sum(rewards) / len(rewards) - - # TODO: remove next comments - # function_reward += params["velocity"] * 0.5 - # function_reward -= params["steering_angle"] * 1.02 - - return function_reward, done - - def rewards_followlane_center_v_w(self): - """esta sin terminar""" - center = 0 - done = False - if 0.65 >= center > 0.25: - reward = 10 - elif (0.9 > center > 0.65) or (0.25 >= center > 0): - reward = 2 - elif 0 >= center > -0.9: - reward = 1 - else: - reward = -100 - done = True - - return reward, done + ######################################################## + # utils + # + ######################################################## def __del__(self): print("__del__ called") diff --git a/rl_studio/envs/carla/followlane/rewards.py b/rl_studio/envs/carla/followlane/rewards.py index 5cc237bbd..108e4b27d 100644 --- a/rl_studio/envs/carla/followlane/rewards.py +++ b/rl_studio/envs/carla/followlane/rewards.py @@ -4,6 +4,68 @@ class AutoCarlaRewards: + def rewards_easy(self, error, params): + rewards = [] + done = False + for i, _ in enumerate(error): + if error[i] < 0.2: + rewards.append(10) + elif 0.2 <= error[i] < 0.4: + rewards.append(2) + elif 0.4 <= error[i] < 0.9: + rewards.append(0) + else: + rewards.append(-100) + done = True + + function_reward = sum(rewards) / len(rewards) + + # TODO: remove next comments + # function_reward += params["velocity"] * 0.5 + # function_reward -= params["steering_angle"] * 1.02 + + return function_reward, done + + def rewards_followline_center(self, center, rewards): + """ + original for Following Line + """ + done = False + if center > 0.9: + done = True + reward = rewards["penal"] + elif 0 <= center <= 0.2: + reward = rewards["from_10"] + elif 0.2 < center <= 0.4: + reward = rewards["from_02"] + else: + reward = rewards["from_01"] + + return reward, done + + def rewards_followlane_dist_v_angle(self, error, params): + # rewards = [] + # for i,_ in enumerate(error): + # if (error[i] < 0.2): + # rewards.append(10) + # elif (0.2 <= error[i] < 0.4): + # rewards.append(2) + # elif (0.4 <= error[i] < 0.9): + # rewards.append(1) + # else: + # rewards.append(0) + rewards = [0.1 / error[i] for i, _ in enumerate(error)] + function_reward = sum(rewards) / len(rewards) + function_reward += math.log10(params["velocity"]) + function_reward -= 1 / (math.exp(params["steering_angle"])) + + return function_reward + + ######################################################## + # So far only Carla exclusively + # + ######################################################## + @staticmethod def rewards_followlane_centerline(center, rewards): """ @@ -70,23 +132,6 @@ def calculate_reward(self, error: float) -> float: reward = np.round(np.exp(-d), 4) return reward - def rewards_followline_center(self, center, rewards): - """ - original for Following Line - """ - done = False - if center > 0.9: - done = True - reward = rewards["penal"] - elif 0 <= center <= 0.2: - reward = rewards["from_10"] - elif 0.2 < center <= 0.4: - reward = rewards["from_02"] - else: - reward = rewards["from_01"] - - return reward, done - def rewards_followline_v_w_centerline( self, vel_cmd, center, rewards, beta_1, beta_0 ): diff --git a/rl_studio/envs/carla/followlane/settings.py b/rl_studio/envs/carla/followlane/settings.py index 47f07ef5a..b55e14b04 100644 --- a/rl_studio/envs/carla/followlane/settings.py +++ b/rl_studio/envs/carla/followlane/settings.py @@ -61,6 +61,7 @@ def __init__(self, **config): self.waypoints_target = config["waypoints_target"] self.waypoints_lane_id = config["waypoints_lane_id"] self.waypoints_road_id = config["waypoints_road_id"] + self.max_target_waypoint_distance = config["max_target_waypoint_distance"] # self.actor_list = [] # self.collision_hist = [] diff --git a/rl_studio/envs/carla/followlane/utils.py b/rl_studio/envs/carla/followlane/utils.py index 5473c6d64..2b7ef1fbc 100644 --- a/rl_studio/envs/carla/followlane/utils.py +++ b/rl_studio/envs/carla/followlane/utils.py @@ -6,6 +6,29 @@ class AutoCarlaUtils: def __init__(self): self.f1 = None + @staticmethod + def finish_target(current_car_pose, target_pose, max_distance): + + current_car_pose_x = current_car_pose[0] + current_car_pose_y = current_car_pose[1] + target_x = target_pose.transform.location.x + target_y = target_pose.transform.location.y + + dist = ((current_car_pose_x - target_x) ** 2) + ( + (current_car_pose_y - target_y) ** 2 + ) + dist = np.sum(dist, axis=0) + dist = np.sqrt(dist) + + # print(f"{current_car_pose = }") + # print(f"{target_x = }, {target_y = }") + # print(f"{dist = }") + + # print(dist) + if dist < max_distance: + return True, dist + return False, dist + @staticmethod def show_image_with_centrals( name, img, waitkey, centrals_in_pixels, centrals_normalized, x_row, x, y From 9c6ff822d120bfd44a90ce03b3e31b4574b2fcd3 Mon Sep 17 00:00:00 2001 From: pjfernandecabo Date: Thu, 9 Mar 2023 17:09:50 -0500 Subject: [PATCH 04/63] seizing PR: adding image png for README --- rl_studio/README.md | 5 +++-- rl_studio/docs/rls-diagram.png | Bin 0 -> 1541700 bytes 2 files changed, 3 insertions(+), 2 deletions(-) create mode 100644 rl_studio/docs/rls-diagram.png diff --git a/rl_studio/README.md b/rl_studio/README.md index 9f1505c95..c9373bc78 100644 --- a/rl_studio/README.md +++ b/rl_studio/README.md @@ -42,7 +42,8 @@ There are several config files to take as example. If you need more information The following graph shows a conceptual diagram of the operation of RL-Studio in training mode. In the case of making inference or retraining, the process is similar -![](./docs/rlstudio-diagram.svg) +![](../rl_studio/docs/rls-diagram.png) + # Run RL Studio @@ -91,4 +92,4 @@ Remaining params should be adjusted too. There are many working yaml files in co > need extend the rl-studio.py to listen for inputs in a port and execute the loaded brain/algorithm to provide > outputs in the desired way. -More info about how to config and launch any task, please go to [agents](agents/README.md) section. +More info about how to config and launch any task, please go to [agents](agents/README.md) se \ No newline at end of file diff --git a/rl_studio/docs/rls-diagram.png b/rl_studio/docs/rls-diagram.png new file mode 100644 index 0000000000000000000000000000000000000000..fdb44c31e40ed10668a84f742622077436dfb8ac GIT binary patch literal 1541700 zcmeEvcOcho_kSs6L@FzhN>)Z>g-D7dD|=)__TDo4R2qr~vRBz#C|OBH$;u`(As?%Z zl=(ZCa^D~K}zOHkf^E$8dI>#Laxw9lhG(?*=Z6Y}*bw+X1 zrX3ydPa}eD@Rf=-_XhaC&Gw3C#W$tb(hhIhbYRoDGpCeYbVj?J9h3*=PLA%|8mO<9 z%JL*pJ@bIB+w1f;;z~}RqW!{lVp(dLZfCx=5r0pAyZJ(DqDv+fyZSC-A|j`rnR~B3 z*IYJwL@0CMQo3=P24VZGXH~9qFTLo9)nu!#*CNHX&3Ic0$PS2I-Sm(CfjLHHa>zt! 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