def experiment(variant):
import sys
from traffic.make_env import make_env
expl_env = make_env(args.exp_name)
eval_env = make_env(args.exp_name)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
gb = TrafficGraphBuilder(input_dim=4,
ego_init=torch.tensor([0.,1.]),
other_init=torch.tensor([1.,0.]),
edge_index=torch.tensor([[0,0,1,2],
[1,2,0,0]]))
qf = GNNNet(
pre_graph_builder = gb,
node_dim = 16,
output_dim = action_dim,
post_mlp_kwargs = variant['qf_kwargs'],
num_conv_layers=3)
target_qf = copy.deepcopy(qf)
eval_policy = ArgmaxDiscretePolicy(qf)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space, variant['epsilon']),
eval_policy,
)
eval_path_collector = MdpPathCollector(
eval_env,
eval_policy,
)
expl_path_collector = MdpPathCollector(
expl_env,
expl_policy,
)
replay_buffer = PrioritizedReplayBuffer(
variant['replay_buffer_size'],
expl_env,
)
qf_criterion = nn.MSELoss()
trainer = DQNTrainer(
qf=qf,
target_qf=target_qf,
qf_criterion=qf_criterion,
replay_buffer=replay_buffer,
**variant['trainer_kwargs']
)
algorithm = TorchBatchRLAlgorithm(
trainer=trainer,
exploration_env=expl_env,
evaluation_env=eval_env,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant['algorithm_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
import sys
from traffic.make_env import make_env
expl_env = make_env(args.exp_name)
eval_env = make_env(args.exp_name)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
gb = TrafficGraphBuilder(input_dim=4,
ego_init=torch.tensor([0., 1.]),
other_init=torch.tensor([1., 0.]),
edge_index=torch.tensor([[0, 0, 1, 2],
[1, 2, 0, 0]]))
module = GNNNet(pre_graph_builder=gb,
node_dim=16,
output_dim=action_dim,
post_mlp_kwargs=dict(hidden_sizes=[32]),
num_conv_layers=3)
policy = SoftmaxPolicy(module, **variant['policy_kwargs'])
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,
**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
max_path_length = variant['trainer_kwargs']['max_path_length']
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:
hidden_dim = variant['lstm_kwargs']['hidden_dim']
num_lstm_layers = variant['lstm_kwargs']['num_layers']
node_dim = variant['gnn_kwargs']['node_dim']
node_num = expl_env.max_veh_num + 1
input_node_dim = int(obs_dim / node_num)
a_0 = np.zeros(action_dim)
h1_0 = np.zeros((node_num, hidden_dim * num_lstm_layers))
c1_0 = np.zeros((node_num, hidden_dim * num_lstm_layers))
h2_0 = np.zeros((node_num, hidden_dim * num_lstm_layers))
c2_0 = np.zeros((node_num, hidden_dim * num_lstm_layers))
latent_0 = (h1_0, c1_0, h2_0, c2_0)
from lstm_net import LSTMNet
lstm1_ego = LSTMNet(input_node_dim, action_dim, hidden_dim,
num_lstm_layers)
lstm1_other = LSTMNet(input_node_dim, 0, hidden_dim, num_lstm_layers)
lstm2_ego = LSTMNet(node_dim, 0, hidden_dim, num_lstm_layers)
lstm2_other = LSTMNet(node_dim, 0, hidden_dim, num_lstm_layers)
from graph_builder import TrafficGraphBuilder
gb = TrafficGraphBuilder(
input_dim=hidden_dim,
node_num=node_num,
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=variant['gnn_kwargs']['node_dim'],
conv_type=variant['gnn_kwargs']['conv_type'],
num_conv_layers=variant['gnn_kwargs']['num_layers'],
hidden_activation=variant['gnn_kwargs']['activation'],
)
from gnn_lstm2_net import GNNLSTM2Net
policy_net = GNNLSTM2Net(node_num, gnn, lstm1_ego, lstm1_other,
lstm2_ego, lstm2_other)
from layers import FlattenLayer, SelectLayer
decoder = nn.Sequential(SelectLayer(-2, 0), FlattenLayer(2), nn.ReLU(),
nn.Linear(hidden_dim, action_dim))
from layers import ReshapeLayer
sup_learner = nn.Sequential(
SelectLayer(-2, np.arange(1, node_num)),
nn.ReLU(),
nn.Linear(hidden_dim, label_dim),
)
from sup_softmax_lstm_policy import SupSoftmaxLSTMPolicy
policy = SupSoftmaxLSTMPolicy(
a_0=a_0,
latent_0=latent_0,
obs_dim=obs_dim,
action_dim=action_dim,
lstm_net=policy_net,
decoder=decoder,
sup_learner=sup_learner,
)
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()
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
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,
action_dim=action_dim,
label_dim=label_num,
max_replay_buffer_size=int(1e6),
max_path_length=max_path_length,
recurrent=True,
)
from rlkit.torch.vpg.ppo_sup_vanilla import PPOSupVanillaTrainer
trainer = PPOSupVanillaTrainer(policy=policy,
value_function=vf,
vf_criterion=vf_criterion,
replay_buffer=replay_buffer,
recurrent=True,
**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 simple_sup_lstm import SimpleSupLSTMEnv
expl_env = SimpleSupLSTMEnv(**variant['env_kwargs'])
eval_env = SimpleSupLSTMEnv(**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:
hidden_dim = variant['lstm_kwargs']['hidden_dim']
num_lstm_layers = variant['lstm_kwargs']['num_layers']
node_dim = variant['gnn_kwargs']['node_dim']
node_num = expl_env.node_num
input_node_dim = int(obs_dim / node_num)
a_0 = np.zeros(action_dim)
h1_0 = np.zeros((node_num, hidden_dim * num_lstm_layers))
c1_0 = np.zeros((node_num, hidden_dim * num_lstm_layers))
h2_0 = np.zeros((node_num, hidden_dim * num_lstm_layers))
c2_0 = np.zeros((node_num, hidden_dim * num_lstm_layers))
latent_0 = (h1_0, c1_0, h2_0, c2_0)
from lstm_net import LSTMNet
lstm1_ego = LSTMNet(input_node_dim, action_dim, hidden_dim,
num_lstm_layers)
lstm1_other = LSTMNet(input_node_dim, 0, hidden_dim, num_lstm_layers)
lstm2_ego = LSTMNet(node_dim, 0, hidden_dim, num_lstm_layers)
lstm2_other = LSTMNet(node_dim, 0, hidden_dim, num_lstm_layers)
from graph_builder import TrafficGraphBuilder
gb = TrafficGraphBuilder(
input_dim=hidden_dim,
node_num=node_num,
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=variant['gnn_kwargs']['node_dim'],
conv_type=variant['gnn_kwargs']['conv_type'],
num_conv_layers=variant['gnn_kwargs']['num_layers'],
hidden_activation=variant['gnn_kwargs']['activation'],
)
from gnn_lstm2_net import GNNLSTM2Net
policy_net = GNNLSTM2Net(node_num, gnn, lstm1_ego, lstm1_other,
lstm2_ego, lstm2_other)
from layers import FlattenLayer, SelectLayer
post_net = nn.Sequential(SelectLayer(-2, 0), FlattenLayer(2),
nn.ReLU(), nn.Linear(hidden_dim, action_dim))
from softmax_lstm_policy import SoftmaxLSTMPolicy
policy = SoftmaxLSTMPolicy(
a_0=a_0,
latent_0=latent_0,
obs_dim=obs_dim,
action_dim=action_dim,
lstm_net=policy_net,
post_net=post_net,
)
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,
) # TODO: id is also an input
vf_criterion = nn.MSELoss()
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
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,
recurrent=True,
**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,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
ng = pyg_utils.to_networkx(data)
pos = {}
labels = {}
color_map = []
for node in ng.nodes:
pos[node] = obs_batch[0, node, :2].numpy()
labels[node] = str(node)
color_map.append('C' + str(node))
plt.figure()
networkx.draw(ng, pos, node_color=color_map, labels=labels)
plt.show()
from graph_builder import TrafficGraphBuilder
node_num = env.max_veh_num + 1
gb = TrafficGraphBuilder(
input_dim=4,
node_num=node_num,
ego_init=torch.tensor([0., 1.]),
other_init=torch.tensor([1., 0.]),
)
obs = env.reset()
env.render()
check_graph(obs)
while True:
obs, r, done, info = env.step(1)
env.render()
check_graph(obs)
import json
with open('{}/{}/seed{}/variant.json'.format(pre_dir, args.log_dir,
args.seed)) as f:
variant = json.load(f)
env = make_env(args.exp_name, **variant['env_kwargs'])
o = env.reset()
env.render()
pdb.set_trace()
policy.reset()
if args.plot_graph:
node_num = env.max_veh_num + 1
from graph_builder import TrafficGraphBuilder
gb = TrafficGraphBuilder(
input_dim=4,
node_num=node_num,
ego_init=torch.tensor([0., 1.]),
other_init=torch.tensor([1., 0.]),
)
max_path_length = 200
path_length = 0
done = False
c_r = 0.
while True:
path_length += 1
a, agent_info = eval_policy.get_action(o)
o, r, done, _ = env.step(a)
if 'intentions' in agent_info.keys():
intentions = agent_info['intentions']
else:
import torch
import numpy as np
from graph_builder import TrafficGraphBuilder
from gnn_net import GNNNet
gb = TrafficGraphBuilder(input_dim=4,
ego_init=torch.tensor([0., 1.]),
other_init=torch.tensor([1., 0.]),
edge_index=torch.tensor([[0, 0, 1, 2], [1, 2, 0, 0]]))
obs_batch = torch.tensor([[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2],
[3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5]])
x, edge_index = gb(obs_batch)
print(x)
print(edge_index)
gnn = GNNNet(pre_graph_builder=gb,
node_dim=16,
output_dim=2,
post_mlp_kwargs={'hidden_sizes': [64]},
num_conv_layers=0)
q = gnn(obs_batch)
print(q)