def build(path, is_virtual=False): # create world world = World(path, thread_num=args.thread) # create agents agents = [] for i in world.intersections: action_space = gym.spaces.Discrete(len(i.phases)) agents.append( PressLightAgent( action_space, LaneVehicleGenerator(world, i, ["lane_count"], in_only=True, average=None), LaneVehicleGenerator(world, i, ["lane_waiting_count"], in_only=True, average="all", negative=True), i.id, world, is_virtual)) if args.load_model: agents[-1].load_model(args.save_dir) # if len(agents) == 5: # break # print(agents[0].ob_length) # print(agents[0].action_space) # create metric metric = TravelTimeMetric(world) # create env env = TSCEnv(world, agents, metric) return world, agents, env
def init(args, test=False): tf_mute_warning() args.save_dir = save_dir + args.config_file[7:-5] if test: args.save_dir = save_dir + args.config_file[7:-10] # config_name = args.config_file.split('/')[1].split('.')[0] # args.agent_save_dir = args.save_dir + "/" + config_name if not os.path.exists(args.save_dir): os.mkdir(args.save_dir) if not os.path.exists(args.log_dir): os.makedirs(args.log_dir) logger = logging.getLogger('main') logger.setLevel(logging.DEBUG) fh = logging.FileHandler( os.path.join(args.log_dir, datetime.now().strftime('%Y%m%d-%H%M%S') + ".log")) fh.setLevel(logging.DEBUG) sh = logging.StreamHandler() sh.setLevel(logging.INFO) logger.addHandler(fh) logger.addHandler(sh) # create world world = World(args.config_file, thread_num=args.thread, silent=True) # create agents agents = [] for i in world.intersections: action_space = gym.spaces.Discrete(len(i.phases)) agents.append( DQNAgent( action_space, LaneVehicleGenerator(world, i, ["lane_count"], in_only=True, average=None), LaneVehicleGenerator(world, i, ["lane_waiting_count"], in_only=True, average="all", negative=True), i.id)) if args.load_model: agents[-1].load_model(args.save_dir) if args.share_weights: model = agents[0].model for agent in agents: agent.model = model # create metric metric = TravelTimeMetric(world) # create env env = TSCEnv(world, agents, metric) return env
def run(args): # create world world = World(args.config_file, thread_num=args.thread, silent=True) # create agents agents = [] for i in world.intersections: action_space = gym.spaces.Discrete(len(i.phases)) agents.append( MaxPressureAgent( action_space, i, world, LaneVehicleGenerator(world, i, ["lane_count"], in_only=True, average=None), LaneVehicleGenerator(world, i, ["lane_waiting_count"], in_only=True, average="all", negative=True))) # create metric metric = TravelTimeMetric(world) # create env env = TSCEnv(world, agents, metric) # simulate obs = env.reset() actions = [] i = 0 reward_sum = 0. reward_cnt = 0. while i < args.steps: if i % args.action_interval == 0: actions = [] for agent_id, agent in enumerate(agents): actions.append(agent.get_action(obs[agent_id])) for _ in range(args.action_interval): obs, rewards, dones, info = env.step(actions) i += 1 for reward in rewards: reward_sum += reward reward_cnt += 1 print("avg queue length: {}".format(reward_sum / reward_cnt)) result = env.eng.get_average_travel_time() return result
def build(path): # create world world = World(path, thread_num=args.thread) # create agents agents = [] for idx, i in enumerate(world.intersections): action_space = gym.spaces.Discrete(len(i.phases)) agents.append( IntelliLightAgent(action_space, [ LaneVehicleGenerator(world, i, ["lane_waiting_count"], in_only=True, average="lane"), LaneVehicleGenerator( world, i, ["lane_count"], in_only=True, average="lane"), LaneVehicleGenerator(world, i, ["lane_waiting_time_count"], in_only=True, average="lane"), IntersectionVehicleGenerator(world, i, targets=["vehicle_map"]) ], [ LaneVehicleGenerator(world, i, [ "lane_waiting_count", "lane_delay", "lane_waiting_time_count" ], in_only=True, average="all"), IntersectionVehicleGenerator( world, i, targets=["passed_count", "passed_time_count"]) ], world, idx)) if args.load_model: agents[-1].load_model(args.save_dir) # create metric metric = TravelTimeMetric(world) # create env env = TSCEnv(world, agents, metric) return world, agents, env
def build(path): world = World(path, thread_num=args.thread) # create observation generator, which is used to construct sample observation_generators = [] for node_dict in world.intersections: node_id = node_dict.id node_id_int = net_node_dict_inter2id[node_id] tmp_generator = LaneVehicleGenerator(world, node_dict, ["lane_count"], in_only=True, average='road') observation_generators.append((node_id_int, tmp_generator)) # if len(observation_generators) == 5: # break sorted( observation_generators, key=lambda x: x[0] ) # sorted the ob_generator based on its corresponding id_int, increasingly # create agent action_space = gym.spaces.Discrete(len(world.intersections[0].phases)) colightAgent = CoLightAgent( action_space, observation_generators, LaneVehicleGenerator(world, world.intersections[0], ["lane_waiting_count"], in_only=True, average="all", negative=True), world, dic_traffic_env_conf, dic_graph_setting, args) if args.load_model: colightAgent.load_model(args.load_dir) print(colightAgent.ob_length) print(colightAgent.action_space) # create metric metric = TravelTimeMetric(world) # create env env = TSCEnv(world, colightAgent, metric) return world, colightAgent, env
def init(args, test=False): tf_mute_warning() args.save_dir = save_dir + args.config_file[7:-5] if test: args.save_dir = save_dir + args.config_file[7:-10] # config_name = args.config_file.split('/')[1].split('.')[0] # args.agent_save_dir = args.save_dir + "/" + config_name if not os.path.exists(args.save_dir): os.mkdir(args.save_dir) # create world world = World(args.config_file, thread_num=args.thread, silent=True) # create agents agents = [] for i in world.intersections: action_space = gym.spaces.Discrete(len(i.phases)) agents.append( FRAP_DQNAgent( action_space, LaneVehicleGenerator(world, i, ["lane_count"], in_only=True, average=None), LaneVehicleGenerator(world, i, ["lane_waiting_count"], in_only=True, average="all", negative=True), world, i.id)) # create metric metric = TravelTimeMetric(world) # create env env = TSCEnv(world, agents, metric) return env
def build(path): # create world world = World(path, thread_num=args.thread) # create agents agents = [] for i in world.intersections: action_space = gym.spaces.Discrete(len(i.phases)) agents.append(DQNAgent( action_space, LaneVehicleGenerator(world, i, ["lane_count"], in_only=True, average=None), LaneVehicleGenerator(world, i, ["lane_waiting_count"], in_only=True, average="all", negative=True), i.id )) if args.load_model: agents[-1].load_model(args.save_dir) # create metric metric = TravelTimeMetric(world) # create env env = TSCEnv(world, agents, metric) return world, agents, env
def test(path): # create world world = World(path, thread_num=args.thread) # create agents agents = [] for i in world.intersections: action_space = gym.spaces.Discrete(len(i.phases)) agents.append( MaxPressureAgent( action_space, i, world, LaneVehicleGenerator(world, i, ["lane_count"], in_only=True))) # create metric metric = TravelTimeMetric(world) # create env env = TSCEnv(world, agents, metric) # simulate obs = env.reset() actions = [] for i in range(args.steps): actions = [] for agent_id, agent in enumerate(agents): actions.append(agent.get_action(obs[agent_id])) obs, rewards, dones, info = env.step(actions) #print(world.intersections[0]._current_phase, end=",") # print(obs, actions) # print(env.eng.get_average_travel_time()) #print(obs) #print(rewards) # print(info["metric"]) logger.info("Final Travel Time is %.4f" % env.eng.get_average_travel_time()) return env.eng.get_average_travel_time()
def build(path): # create world world = World(path, thread_num=args.thread) # create agents agents = [] for i in world.intersections: action_space = gym.spaces.Discrete(len(i.phases)) agents.append( MADDPGAgent( action_space, LaneVehicleGenerator(world, i, ["lane_count"], in_only=True, average=None), LaneVehicleGenerator(world, i, ["lane_waiting_count"], in_only=True, average="all", negative=True), args, i.id)) ob_space_n = [] action_space_n = [] for agent in agents: ob_space_n.append(agent.ob_shape) action_space_n.append(agent.action_space) print(ob_space_n) print(action_space_n) for i, agent in enumerate(agents): agent.build_model(ob_space_n, action_space_n, i) # create metric metric = TravelTimeMetric(world) # create env env = TSCEnv(world, agents, metric) return world, agents, env
'green_v': args.green_v, 'red_v': args.red_v } #Create world world = World(args.config_file, thread_num=args.thread) #Create agents agents = [] for i in world.intersections: action_space = gym.spaces.Discrete(len(i.phases)) agents.append(SOTLAgent(action_space, options, i, world)) # Create metric metric = [ TravelTimeMetric(world), ThroughputMetric(world), SpeedScoreMetric(world), MaxWaitingTimeMetric(world) ] #Create env env = TSCEnv(world, agents, metric) obs = env.reset() actions = [] steps = 0 episodes_rewards = 0 #Walk through the steps while steps < args.steps:
StateOfThreeGenerator(world, i, ["state_of_three"], in_only=True, average=None), LaneVehicleGenerator(world, i, ["lane_waiting_count"], in_only=True, average="all", negative=True), i, world, file_name, info_file )) if args.load_model: agents[-1].load_model(args.save_dir) print(i.phases) # Create metric metric = [TravelTimeMetric(world), ThroughputMetric(world), SpeedScoreMetric(world), MaxWaitingTimeMetric(world)] # create env env = TSCEnv(world, agents, metric) # n_agents = len(agents) # train dqn_agent def train(args, env): for e in range(episodes): for agent in agents: agent.reset_episode_infos() first_obs = np.array(env.reset())*0.01 current_obs = first_obs
args = parser.parse_args() # create world world = World(args.config_file, thread_num=args.thread) # create agents agents = [] for i in world.intersections: action_space = gym.spaces.Discrete(len(i.phases)) agents.append( MaxPressureAgent( action_space, i, world, LaneVehicleGenerator(world, i, ["lane_count"], in_only=True))) # create metric metric = [TravelTimeMetric(world), QueueLengthMetric(world)] world.subscribe("lane_waiting_time_count") # create env env = TSCEnv(world, agents, metric) # simulate obs = env.reset() actions = [] for i in range(args.steps): actions = [] for agent_id, agent in enumerate(agents): actions.append(agent.get_action(obs[agent_id])) obs, rewards, dones, info = env.step(actions) for metric_obj in env.metric: metric_obj.update()
def create_colight_env(args, agent="bc_colight"): config = json.load(open(args.config_file, "r")) road_net_file_path = config["dir"] + config["roadnetFile"] res = build_int_intersection_map(road_net_file_path) net_node_dict_id2inter = res[0] net_node_dict_inter2id = res[1] net_edge_dict_id2edge = res[2] net_edge_dict_edge2id = res[3] node_degree_node = res[4] node_degree_edge = res[5] node_adjacent_node_matrix = res[6] node_adjacent_edge_matrix = res[7] edge_adjacent_node_matrix = res[8] # create world world = World(args.config_file, thread_num=args.thread, silent=True) dic_traffic_env_conf = { "NUM_INTERSECTIONS": len(net_node_dict_id2inter), # used "NUM_ROADS": len(net_edge_dict_id2edge), # used } dic_graph_setting = { "NEIGHBOR_NUM": 4, # standard number of adjacent nodes of each node "NEIGHBOR_EDGE_NUM": 4, # # standard number of adjacent edges of each node "N_LAYERS": 1, # layers of MPNN "INPUT_DIM": [128, 128], # input dimension of each layer of multiheadattention, the first value should == the last value of "NODE_EMB_DIM" "OUTPUT_DIM": [128, 128], # output dimension of each layer of multiheadattention, the first value should == the last value of "NODE_EMB_DIM" "NODE_EMB_DIM": [128, 128], # the firsr two layer of dense to embedding the input "NUM_HEADS": [5, 5], "NODE_LAYER_DIMS_EACH_HEAD": [16, 16], # [input_dim,output_dim] "OUTPUT_LAYERS": [], # "NEIGHBOR_ID": node_adjacent_node_matrix, # adjacent node id of each node "ID2INTER_MAPPING": net_node_dict_id2inter, # id ---> intersection mapping "INTER2ID_MAPPING": net_node_dict_inter2id, # intersection ----->id mapping "NODE_DEGREE_NODE": node_degree_node, # number of adjacent nodes of node } tmp_agents = [] observation_generators = [] for node_dict in world.intersections: node_id = node_dict.id action_space = gym.spaces.Discrete(len(node_dict.phases)) node_id_int = net_node_dict_inter2id[node_id] tmp_generator = LaneVehicleGenerator(world, node_dict, ["lane_count"], in_only=True, average=None) observation_generators.append((node_id_int, tmp_generator)) sorted( observation_generators, key=lambda x: x[0] ) # sorted the ob_generator based on its corresponding id_int, increasingly # create agent action_space = gym.spaces.Discrete(len(world.intersections[0].phases)) if agent == "bc_colight": colightAgent = BCCoLightAgent(action_space, observation_generators, world, dic_traffic_env_conf, dic_graph_setting, args) elif agent == "colight": colightAgent = CoLightAgent( action_space, observation_generators, LaneVehicleGenerator(world, world.intersections[0], ["lane_waiting_count"], in_only=True, average="all", negative=True), world, dic_traffic_env_conf, dic_graph_setting, args) else: colightAgent = None # print(colightAgent.ob_length) # print(colightAgent.action_space) # create metric metric = TravelTimeMetric(world) agents = [colightAgent] # create env env = TSCEnv(world, agents, metric) return env