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()