def experiment(variant): import sys from traffic.make_env import make_env expl_env = make_env(args.exp_name,**variant['env_kwargs']) eval_env = make_env(args.exp_name,**variant['env_kwargs']) obs_dim = eval_env.observation_space.low.size action_dim = eval_env.action_space.n from graph_builder_multi import MultiTrafficGraphBuilder gb = MultiTrafficGraphBuilder(input_dim=4, node_num=expl_env.max_veh_num+1, ego_init=torch.tensor([0.,1.]), other_init=torch.tensor([1.,0.]), ) from gnn_net import GNNNet gnn = GNNNet( pre_graph_builder = gb, node_dim = 16, num_conv_layers=3) from layers import SelectLayer encoders = [] encoders.append(nn.Sequential(gnn,SelectLayer(1,0),nn.ReLU())) sup_learners = [] for i in range(expl_env.max_veh_num): sup_learner = nn.Sequential( gnn, SelectLayer(1,i+1), nn.ReLU(), nn.Linear(16, 2), ) sup_learner = SoftmaxPolicy(sup_learner, learn_temperature=False) sup_learners.append(sup_learner) encoders.append(sup_learner) decoder = Mlp(input_size=int(16+2*expl_env.max_veh_num), output_size=action_dim, hidden_sizes=[], ) from layers import ConcatLayer need_gradients = np.array([True]*len(encoders)) if variant['no_gradient']: need_gradients[1:] = False policy = nn.Sequential( ConcatLayer(encoders, need_gradients=list(need_gradients), dim=1), decoder, ) policy = SoftmaxPolicy(policy, learn_temperature=False) vf = Mlp( hidden_sizes=[32, 32], input_size=obs_dim, output_size=1, ) vf_criterion = nn.MSELoss() eval_policy = ArgmaxDiscretePolicy(policy,use_preactivation=True) expl_policy = policy eval_path_collector = MdpPathCollector( eval_env, eval_policy, ) expl_path_collector = MdpPathCollector( expl_env, expl_policy, ) from sup_replay_buffer import SupReplayBuffer replay_buffer = SupReplayBuffer( observation_dim = obs_dim, label_dims = [1]*expl_env.max_veh_num, max_replay_buffer_size = int(1e6), ) from rlkit.torch.vpg.ppo_sup import PPOSupTrainer trainer = PPOSupTrainer( policy=policy, value_function=vf, vf_criterion=vf_criterion, sup_learners=sup_learners, replay_buffer=replay_buffer, **variant['trainer_kwargs'] ) algorithm = TorchOnlineRLAlgorithm( trainer=trainer, exploration_env=expl_env, evaluation_env=eval_env, exploration_data_collector=expl_path_collector, evaluation_data_collector=eval_path_collector, log_path_function = get_traffic_path_information, **variant['algorithm_kwargs'] ) algorithm.to(ptu.device) algorithm.train()
observe_mode=args.obs, label_mode=args.label, yld=args.yld, driver_sigma=args.ds, ) from traffic.make_env import make_env env = make_env(args.exp_name, **env_kwargs) obs_dim = env.observation_space.low.size action_dim = env.action_space.n label_num = env.label_num label_dim = env.label_dim from graph_builder_multi import MultiTrafficGraphBuilder gb = MultiTrafficGraphBuilder( input_dim=4, node_num=env.max_veh_num + 1, ego_init=torch.tensor([0., 1.]), other_init=torch.tensor([1., 0.]), ) obs = env.reset() obs_batch = torch.tensor([obs]) valid_mask = gb.get_valid_node_mask(obs_batch) print('valid_mask: ', valid_mask) x, edge_index = gb(obs_batch) print('x: ', x) print('edge_index: ', edge_index) data = Data(x=x, edge_index=edge_index) ng = pyg_utils.to_networkx(data) networkx.draw_planar(ng)
def experiment(variant): from traffic.make_env import make_env expl_env = make_env(args.exp_name, **variant['env_kwargs']) eval_env = make_env(args.exp_name, **variant['env_kwargs']) obs_dim = eval_env.observation_space.low.size action_dim = eval_env.action_space.n label_num = expl_env.label_num label_dim = expl_env.label_dim from graph_builder_multi import MultiTrafficGraphBuilder policy_gb = MultiTrafficGraphBuilder( input_dim=4 + label_dim, node_num=expl_env.max_veh_num + 1, ego_init=torch.tensor([0., 1.]), other_init=torch.tensor([1., 0.]), ) if variant['gnn_kwargs']['attention']: from gnn_attention_net import GNNAttentionNet gnn_class = GNNAttentionNet else: from gnn_net import GNNNet gnn_class = GNNNet policy_gnn = gnn_class( pre_graph_builder=policy_gb, node_dim=variant['gnn_kwargs']['node'], num_conv_layers=variant['gnn_kwargs']['layer'], hidden_activation=variant['gnn_kwargs']['activation'], ) from layers import FlattenLayer, SelectLayer policy = nn.Sequential( policy_gnn, SelectLayer(1, 0), FlattenLayer(), nn.ReLU(), nn.Linear(variant['gnn_kwargs']['node'], action_dim)) sup_gb = MultiTrafficGraphBuilder( input_dim=4, node_num=expl_env.max_veh_num + 1, ego_init=torch.tensor([0., 1.]), other_init=torch.tensor([1., 0.]), ) sup_attentioner = None from layers import ReshapeLayer from gnn_net import GNNNet sup_gnn = GNNNet( pre_graph_builder=sup_gb, node_dim=variant['gnn_kwargs']['node'], num_conv_layers=variant['gnn_kwargs']['layer'], hidden_activation=variant['gnn_kwargs']['activation'], ) sup_learner = nn.Sequential( sup_gnn, SelectLayer(1, np.arange(1, expl_env.max_veh_num + 1)), nn.ReLU(), nn.Linear(variant['gnn_kwargs']['node'], label_dim), ) from sup_sep_softmax_policy import SupSepSoftmaxPolicy policy = SupSepSoftmaxPolicy(policy, sup_learner, label_num, label_dim) print('parameters: ', np.sum([p.view(-1).shape[0] for p in policy.parameters()])) vf = Mlp( hidden_sizes=[32, 32], input_size=obs_dim, output_size=1, ) vf_criterion = nn.MSELoss() eval_policy = ArgmaxDiscretePolicy(policy, use_preactivation=True) expl_policy = policy eval_path_collector = MdpPathCollector( eval_env, eval_policy, ) from sup_sep_rollout import sup_sep_rollout expl_path_collector = MdpPathCollector( expl_env, expl_policy, rollout_fn=sup_sep_rollout, ) from sup_replay_buffer import SupReplayBuffer replay_buffer = SupReplayBuffer( observation_dim=obs_dim, label_dim=label_num, max_replay_buffer_size=int(1e6), ) from rlkit.torch.vpg.trpo_sup_sep import TRPOSupSepTrainer trainer = TRPOSupSepTrainer(policy=policy, value_function=vf, vf_criterion=vf_criterion, replay_buffer=replay_buffer, **variant['trainer_kwargs']) algorithm = TorchOnlineRLAlgorithm( trainer=trainer, exploration_env=expl_env, evaluation_env=eval_env, exploration_data_collector=expl_path_collector, evaluation_data_collector=eval_path_collector, log_path_function=get_traffic_path_information, **variant['algorithm_kwargs']) algorithm.to(ptu.device) algorithm.train()
def experiment(variant): from traffic.make_env import make_env expl_env = make_env(args.exp_name,**variant['env_kwargs']) eval_env = make_env(args.exp_name,**variant['env_kwargs']) obs_dim = eval_env.observation_space.low.size action_dim = eval_env.action_space.n label_num = expl_env.label_num label_dim = expl_env.label_dim if variant['load_kwargs']['load']: load_dir = variant['load_kwargs']['load_dir'] load_data = torch.load(load_dir+'/params.pkl',map_location='cpu') policy = load_data['trainer/policy'] vf = load_data['trainer/value_function'] else: from graph_builder_multi import MultiTrafficGraphBuilder gb = MultiTrafficGraphBuilder(input_dim=4, node_num=expl_env.max_veh_num+1, ego_init=torch.tensor([0.,1.]), other_init=torch.tensor([1.,0.]), ) if variant['gnn_kwargs']['attention']: from gnn_attention_net import GNNAttentionNet gnn_class = GNNAttentionNet else: from gnn_net import GNNNet gnn_class = GNNNet gnn = gnn_class( pre_graph_builder=gb, node_dim=variant['gnn_kwargs']['node'], conv_type=variant['gnn_kwargs']['conv_type'], num_conv_layers=variant['gnn_kwargs']['layer'], hidden_activation=variant['gnn_kwargs']['activation'], ) from layers import FlattenLayer, SelectLayer policy = nn.Sequential( gnn, SelectLayer(1,0), FlattenLayer(), nn.ReLU(), nn.Linear(variant['gnn_kwargs']['node'],action_dim) ) policy = SoftmaxPolicy(policy, learn_temperature=False) print('parameters: ',np.sum([p.view(-1).shape[0] for p in policy.parameters()])) vf = Mlp( hidden_sizes=[32, 32], input_size=obs_dim, output_size=1, ) vf_criterion = nn.MSELoss() eval_policy = ArgmaxDiscretePolicy(policy,use_preactivation=True) expl_policy = policy eval_path_collector = MdpPathCollector( eval_env, eval_policy, ) expl_path_collector = MdpPathCollector( expl_env, expl_policy, ) trainer = PPOTrainer( policy=policy, value_function=vf, vf_criterion=vf_criterion, **variant['trainer_kwargs'] ) algorithm = TorchOnlineRLAlgorithm( trainer=trainer, exploration_env=expl_env, evaluation_env=eval_env, exploration_data_collector=expl_path_collector, evaluation_data_collector=eval_path_collector, log_path_function = get_traffic_path_information, **variant['algorithm_kwargs'] ) algorithm.to(ptu.device) algorithm.train()