def experiment(variant):
import sys
sys.path.append("./multiagent-particle-envs")
from make_env import make_env
from particle_env_wrapper import ParticleEnv
expl_env = ParticleEnv(
make_env(args.exp_name,
discrete_action_space=False,
world_args=variant['world_args']))
eval_env = ParticleEnv(
make_env(args.exp_name,
discrete_action_space=False,
world_args=variant['world_args']))
num_agent = expl_env.num_agent
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
from rlkit.torch.networks.graph_builders import FullGraphBuilder
graph_builder_1 = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.graph_context_network import GraphContextNet
cg1 = GraphContextNet(graph_builder_1,
obs_dim,
action_dim,
output_activation='lrelu0.2',
**variant['graph_kwargs'])
target_cg1 = copy.deepcopy(cg1)
from rlkit.torch.networks.networks import FlattenMlp
qf1 = FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'] + action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
target_qf1 = copy.deepcopy(qf1)
graph_builder_2 = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
cg2 = GraphContextNet(graph_builder_2,
obs_dim,
action_dim,
output_activation='lrelu0.2',
**variant['graph_kwargs'])
target_cg2 = copy.deepcopy(cg2)
qf2 = FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'] + action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
target_qf2 = copy.deepcopy(qf2)
graph_builder_ca = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.gnn_networks import GNNNet
cgca = GNNNet(
pre_graph_builder=graph_builder_ca,
node_dim=variant['graph_kwargs']['node_dim'],
conv_type='GSage',
num_conv_layers=variant['graph_kwargs']['num_layer'],
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
)
from rlkit.torch.networks.layers import SplitLayer
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
cactor = nn.Sequential(
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=variant['cactor_kwargs']['hidden_dim'],
hidden_sizes=[variant['cactor_kwargs']['hidden_dim']] *
(variant['cactor_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
), nn.LeakyReLU(negative_slope=0.2),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
cactor = TanhGaussianPolicy(module=cactor)
policy_n, expl_policy_n, eval_policy_n = [], [], []
for i in range(num_agent):
policy = nn.Sequential(
FlattenMlp(
input_size=obs_dim,
output_size=variant['policy_kwargs']['hidden_dim'],
hidden_sizes=[variant['policy_kwargs']['hidden_dim']] *
(variant['policy_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
policy = TanhGaussianPolicy(module=policy)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
expl_policy = policy
policy_n.append(policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
from rlkit.samplers.data_collector.ma_path_collector import MAMdpPathCollector
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
from rlkit.data_management.ma_env_replay_buffer import MAEnvReplayBuffer
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
from rlkit.torch.r2g.r2g_gnn3 import R2GGNNTrainer
trainer = R2GGNNTrainer(env=expl_env,
cg1=cg1,
target_cg1=target_cg1,
qf1=qf1,
target_qf1=target_qf1,
cg2=cg2,
target_cg2=target_cg2,
qf2=qf2,
target_qf2=target_qf2,
cgca=cgca,
cactor=cactor,
policy_n=policy_n,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm
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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
# save init params
from rlkit.core import logger
snapshot = algorithm._get_snapshot()
file_name = osp.join(logger._snapshot_dir, 'itr_-1.pkl')
torch.save(snapshot, file_name)
algorithm.train()
def experiment(variant):
from multi_differential_game import MultiDifferentialGame
expl_env = MultiDifferentialGame(**variant['env_kwargs'])
eval_env = MultiDifferentialGame(**variant['env_kwargs'])
num_agent = expl_env.agent_num
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
from rlkit.torch.networks.graph_builders import FullGraphBuilder
graph_builder_1 = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.gnn_networks import GNNNet
gnn1 = GNNNet(
graph_builder_1,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
from rlkit.torch.networks.networks import FlattenMlp
qf1 = nn.Sequential(
gnn1,
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
))
target_qf1 = copy.deepcopy(qf1)
from rlkit.torch.networks.graph_builders import FullGraphBuilder
graph_builder_2 = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.gnn_networks import GNNNet
gnn2 = GNNNet(
graph_builder_2,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
qf2 = nn.Sequential(
gnn2,
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
))
target_qf2 = copy.deepcopy(qf2)
graph_builder_policy = FullGraphBuilder(
input_node_dim=obs_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
shared_gnn = GNNNet(
graph_builder_policy,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
policy_n, eval_policy_n, expl_policy_n = [], [], []
for i in range(num_agent):
from rlkit.torch.networks.layers import SplitLayer
policy = nn.Sequential(
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=variant['policy_kwargs']['hidden_dim'],
hidden_sizes=[variant['policy_kwargs']['hidden_dim']] *
(variant['policy_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
policy = TanhGaussianPolicy(module=policy)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
from rlkit.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
if variant['random_exploration']:
from rlkit.exploration_strategies.epsilon_greedy import EpsilonGreedy
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=EpsilonGreedy(expl_env.action_space,
prob_random_action=1.0),
policy=policy,
)
else:
expl_policy = policy
policy_n.append(policy)
eval_policy_n.append(eval_policy)
expl_policy_n.append(expl_policy)
from rlkit.samplers.data_collector.ma_path_collector import MAMdpPathCollector
eval_path_collector = MAMdpPathCollector(eval_env,
eval_policy_n,
shared_encoder=shared_gnn)
expl_path_collector = MAMdpPathCollector(expl_env,
expl_policy_n,
shared_encoder=shared_gnn)
from rlkit.data_management.ma_env_replay_buffer import MAEnvReplayBuffer
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
from rlkit.torch.masac.masac_gnn import MASACGNNTrainer
trainer = MASACGNNTrainer(env=expl_env,
qf1=qf1,
target_qf1=target_qf1,
qf2=qf2,
target_qf2=target_qf2,
policy_n=policy_n,
shared_gnn=shared_gnn,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm
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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
from cartpole import CartPoleEnv
expl_env = CartPoleEnv(mode=3)
eval_env = CartPoleEnv(mode=3)
num_agent = expl_env.num_agents
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
from rlkit.torch.networks.graph_builders import FullGraphBuilder
graph_builder_obs = FullGraphBuilder(
input_node_dim=obs_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.gnn_networks import GNNNet
obs_gnn_1 = GNNNet(
graph_builder_obs,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
graph_builder_eval = FullGraphBuilder(
input_node_dim=graph_builder_obs.output_node_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
if variant['concat_emb']:
gnn_out_dim = int(obs_dim + variant['graph_kwargs']['node_dim'] *
variant['graph_kwargs']['num_conv_layers'])
else:
gnn_out_dim = variant['graph_kwargs']['node_dim']
from rlkit.torch.networks.networks import FlattenMlp
post_mlp1 = FlattenMlp(
input_size=gnn_out_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
from rlkit.torch.networks.graph_r2g_qnet2 import R2GQNet
qf1 = R2GQNet(
obs_gnn=obs_gnn_1,
pre_graph_builder=graph_builder_eval,
obs_dim=obs_dim,
action_dim=action_dim,
post_mlp=post_mlp1,
normalize_emb=False,
output_activation=None,
concat_emb=variant['concat_emb'],
**variant['graph_kwargs'],
)
target_qf1 = copy.deepcopy(qf1)
obs_gnn_2 = GNNNet(
graph_builder_obs,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
post_mlp2 = FlattenMlp(
input_size=gnn_out_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
qf2 = R2GQNet(
obs_gnn=obs_gnn_2,
pre_graph_builder=graph_builder_eval,
obs_dim=obs_dim,
action_dim=action_dim,
post_mlp=post_mlp2,
normalize_emb=False,
output_activation=None,
concat_emb=variant['concat_emb'],
**variant['graph_kwargs'],
)
target_qf2 = copy.deepcopy(qf2)
graph_builder_ca = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.gnn_networks import GNNNet
cgca = GNNNet(
graph_builder_ca,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
from rlkit.torch.networks.networks import FlattenMlp
from rlkit.torch.networks.layers import SplitLayer
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
cactor = nn.Sequential(
cgca,
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=variant['cactor_kwargs']['hidden_dim'],
hidden_sizes=[variant['cactor_kwargs']['hidden_dim']] *
(variant['cactor_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
), nn.LeakyReLU(negative_slope=0.2),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
cactor = TanhGaussianPolicy(module=cactor)
graph_builder_policy = FullGraphBuilder(
input_node_dim=obs_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
policy_n, expl_policy_n, eval_policy_n = [], [], []
for i in range(num_agent):
policy = nn.Sequential(
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=variant['policy_kwargs']['hidden_dim'],
hidden_sizes=[variant['policy_kwargs']['hidden_dim']] *
(variant['policy_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
policy = TanhGaussianPolicy(module=policy)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
if variant['random_exploration']:
from rlkit.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.epsilon_greedy import EpsilonGreedy
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=EpsilonGreedy(expl_env.action_space,
prob_random_action=1.0),
policy=policy,
)
else:
expl_policy = policy
policy_n.append(policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
from rlkit.samplers.data_collector.ma_path_collector import MAMdpPathCollector
eval_path_collector = MAMdpPathCollector(eval_env,
eval_policy_n,
shared_encoder=obs_gnn_1)
expl_path_collector = MAMdpPathCollector(expl_env,
expl_policy_n,
shared_encoder=obs_gnn_1)
from rlkit.data_management.ma_env_replay_buffer import MAEnvReplayBuffer
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
from rlkit.torch.r2g.r2g_gnn12 import R2GGNNTrainer
trainer = R2GGNNTrainer(env=expl_env,
qf1=qf1,
target_qf1=target_qf1,
qf2=qf2,
target_qf2=target_qf2,
cactor=cactor,
policy_n=policy_n,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm
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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
# save init params
from rlkit.core import logger
snapshot = algorithm._get_snapshot()
file_name = osp.join(logger._snapshot_dir, 'itr_-1.pkl')
torch.save(snapshot, file_name)
algorithm.train()
def experiment(variant):
num_agent = variant['num_agent']
from differential_game import DifferentialGame
expl_env = DifferentialGame(game_name=args.exp_name)
eval_env = DifferentialGame(game_name=args.exp_name)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
from rlkit.torch.networks.graph_builders import FullGraphBuilder
graph_builder_1 = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.graph_context_network import GraphContextNet
cg1 = GraphContextNet(
graph_builder_1,
obs_dim,
action_dim,
use_attention=variant['graph_kwargs']['use_attention'],
num_layer=variant['graph_kwargs']['num_layer'],
node_dim=variant['graph_kwargs']['hidden_dim'],
output_activation='relu',
)
target_cg1 = copy.deepcopy(cg1)
graph_builder_2 = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
cg2 = GraphContextNet(
graph_builder_2,
obs_dim,
action_dim,
use_attention=variant['graph_kwargs']['use_attention'],
num_layer=variant['graph_kwargs']['num_layer'],
node_dim=variant['graph_kwargs']['hidden_dim'],
output_activation='relu',
)
target_cg2 = copy.deepcopy(cg2)
graph_builder_ca = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
cgca = GraphContextNet(
graph_builder_ca,
obs_dim,
action_dim,
use_attention=variant['graph_kwargs']['use_attention'],
num_layer=variant['graph_kwargs']['num_layer'],
node_dim=variant['graph_kwargs']['hidden_dim'],
output_activation='relu',
)
policy_n, expl_policy_n, eval_policy_n = [], [], []
qf1_n, target_qf1_n, qf2_n, target_qf2_n = [], [], [], []
cactor_n = []
for i in range(num_agent):
from rlkit.torch.networks.networks import FlattenMlp
qf1 = FlattenMlp(
input_size=variant['graph_kwargs']['hidden_dim'] + action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
)
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(
input_size=variant['graph_kwargs']['hidden_dim'] + action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
)
target_qf2 = copy.deepcopy(qf2)
from rlkit.torch.networks.layers import SplitLayer
cactor = nn.Sequential(
FlattenMlp(
input_size=variant['graph_kwargs']['hidden_dim'],
output_size=variant['cactor_kwargs']['hidden_dim'],
hidden_sizes=[variant['cactor_kwargs']['hidden_dim']] *
(variant['cactor_kwargs']['num_layer'] - 1),
), nn.ReLU(),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
cactor = TanhGaussianPolicy(module=cactor)
policy = nn.Sequential(
FlattenMlp(
input_size=obs_dim,
output_size=variant['policy_kwargs']['hidden_dim'],
hidden_sizes=[variant['policy_kwargs']['hidden_dim']] *
(variant['policy_kwargs']['num_layer'] - 1),
),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
policy = TanhGaussianPolicy(module=policy)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
expl_policy = policy
policy_n.append(policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
qf1_n.append(qf1)
target_qf1_n.append(target_qf1)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
cactor_n.append(cactor)
from rlkit.samplers.data_collector.ma_path_collector import MAMdpPathCollector
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
from rlkit.data_management.ma_env_replay_buffer import MAEnvReplayBuffer
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
from rlkit.torch.r2g.r2g_gnn4 import R2GGNNTrainer
trainer = R2GGNNTrainer(env=expl_env,
cg1=cg1,
target_cg1=target_cg1,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
cg2=cg2,
target_cg2=target_cg2,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
cgca=cgca,
cactor_n=cactor_n,
policy_n=policy_n,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm
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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
num_agent = variant['num_agent']
from differential_game import DifferentialGame
expl_env = DifferentialGame(game_name=args.exp_name)
eval_env = DifferentialGame(game_name=args.exp_name)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
from rlkit.torch.networks.graph_builders import FullGraphBuilder
graph_builder_1 = FullGraphBuilder(input_node_dim=obs_dim + action_dim,
num_node=num_agent,
contain_self_loop=False)
from rlkit.torch.networks.gnn_networks import GNNNet
cg1 = GNNNet(
graph_builder_1,
node_dim=variant['graph_kwargs']['hidden_dim'],
conv_type=variant['graph_kwargs']['conv_type'],
num_conv_layers=2,
hidden_activation='relu',
output_activation='relu',
)
target_cg1 = copy.deepcopy(cg1)
qf1 = nn.Sequential(
nn.Linear(variant['graph_kwargs']['hidden_dim'] + action_dim,
variant['qf_kwargs']['hidden_dim']), nn.ReLU(),
nn.Linear(variant['qf_kwargs']['hidden_dim'], 1))
target_qf1 = copy.deepcopy(qf1)
graph_builder_2 = FullGraphBuilder(input_node_dim=obs_dim + action_dim,
num_node=num_agent,
contain_self_loop=False)
cg2 = GNNNet(
graph_builder_2,
node_dim=variant['graph_kwargs']['hidden_dim'],
conv_type=variant['graph_kwargs']['conv_type'],
num_conv_layers=2,
hidden_activation='relu',
output_activation='relu',
)
target_cg2 = copy.deepcopy(cg2)
qf2 = nn.Sequential(
nn.Linear(variant['graph_kwargs']['hidden_dim'] + action_dim,
variant['qf_kwargs']['hidden_dim']), nn.ReLU(),
nn.Linear(variant['qf_kwargs']['hidden_dim'], 1))
target_qf2 = copy.deepcopy(qf2)
graph_builder_ca = FullGraphBuilder(input_node_dim=obs_dim + action_dim,
num_node=num_agent,
contain_self_loop=False)
from rlkit.torch.networks.gnn_networks import GNNNet
cgca = GNNNet(
graph_builder_ca,
node_dim=variant['graph_kwargs']['hidden_dim'],
conv_type=variant['graph_kwargs']['conv_type'],
num_conv_layers=2,
hidden_activation='relu',
output_activation='relu',
)
from rlkit.torch.networks.layers import SplitLayer
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
cactor = nn.Sequential(
cgca,
nn.Linear(variant['graph_kwargs']['hidden_dim'],
variant['cactor_kwargs']['hidden_dim']), nn.ReLU(),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
cactor = TanhGaussianPolicy(module=cactor)
policy_n, expl_policy_n, eval_policy_n = [], [], []
for i in range(num_agent):
policy = nn.Sequential(
nn.Linear(obs_dim, variant['policy_kwargs']['hidden_dim']),
nn.ReLU(),
nn.Linear(variant['policy_kwargs']['hidden_dim'],
variant['policy_kwargs']['hidden_dim']), nn.ReLU(),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
policy = TanhGaussianPolicy(module=policy)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
from rlkit.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
if variant['random_exploration']:
from rlkit.exploration_strategies.epsilon_greedy import EpsilonGreedy
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=EpsilonGreedy(expl_env.action_space,
prob_random_action=1.0),
policy=policy,
)
else:
expl_policy = policy
policy_n.append(policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
from rlkit.samplers.data_collector.ma_path_collector import MAMdpPathCollector
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
from rlkit.data_management.ma_env_replay_buffer import MAEnvReplayBuffer
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
from rlkit.torch.r2g.r2g_gnn2 import R2GGNNTrainer
trainer = R2GGNNTrainer(env=expl_env,
cg1=cg1,
target_cg1=target_cg1,
qf1=qf1,
target_qf1=target_qf1,
cg2=cg2,
target_cg2=target_cg2,
qf2=qf2,
target_qf2=target_qf2,
cactor=cactor,
policy_n=policy_n,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm
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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
import sys
sys.path.append("./particle-graph-envs")
from make_env import make_env
expl_env = make_env(args.exp_name,
discrete_action_space=False,
world_args=variant['world_args'])
eval_env = make_env(args.exp_name,
discrete_action_space=False,
world_args=variant['world_args'])
num_agent = expl_env.num_agents
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
from particle_graph import ParticleGraphBuilder
graph_builder_obs = ParticleGraphBuilder(
num_agents=expl_env.scenario.num_agents,
num_landmarks=expl_env.scenario.num_landmarks,
batch_size=variant['algorithm_kwargs']['batch_size'],
append_action=False,
contain_self_loop=False,
)
from rlkit.torch.networks.graph_builders import FullGraphBuilder
graph_builder_eval = FullGraphBuilder(
input_node_dim=graph_builder_obs.output_node_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.networks import FlattenMlp
post_mlp1 = FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
from rlkit.torch.networks.graph_r2g_qnet import R2GQNet
qf1 = R2GQNet(
obs_graph_builder=graph_builder_obs,
eval_graph_builder=graph_builder_eval,
obs_dim=graph_builder_obs.output_node_dim,
action_dim=action_dim,
post_mlp=post_mlp1,
normalize_emb=False,
output_activation=None,
**variant['graph_kwargs'],
)
target_qf1 = copy.deepcopy(qf1)
post_mlp2 = FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
from rlkit.torch.networks.graph_r2g_qnet import R2GQNet
qf2 = R2GQNet(
obs_graph_builder=graph_builder_obs,
eval_graph_builder=graph_builder_eval,
obs_dim=graph_builder_obs.output_node_dim,
action_dim=action_dim,
post_mlp=post_mlp2,
normalize_emb=False,
output_activation=None,
**variant['graph_kwargs'],
)
target_qf2 = copy.deepcopy(qf2)
graph_builder_ca = ParticleGraphBuilder(
num_agents=expl_env.scenario.num_agents,
num_landmarks=expl_env.scenario.num_landmarks,
batch_size=variant['algorithm_kwargs']['batch_size'],
append_action=True,
contain_self_loop=False,
)
from rlkit.torch.networks.gnn_networks import GNNNet
from rlkit.torch.networks.layers import SelectLayer
cgca = nn.Sequential(
GNNNet(
graph_builder_ca,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
),
SelectLayer(dim=1, index=torch.arange(num_agent)),
)
from rlkit.torch.networks.layers import SplitLayer
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
cactor = nn.Sequential(
cgca,
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=variant['cactor_kwargs']['hidden_dim'],
hidden_sizes=[variant['cactor_kwargs']['hidden_dim']] *
(variant['cactor_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
), nn.LeakyReLU(negative_slope=0.2),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
cactor = TanhGaussianPolicy(module=cactor)
policy_n, expl_policy_n, eval_policy_n = [], [], []
for agent in range(num_agent):
graph_builder_policy = ParticleGraphBuilder(
num_agents=expl_env.scenario.num_agents,
num_landmarks=expl_env.scenario.num_landmarks,
batch_size=variant['algorithm_kwargs']['batch_size'],
append_action=False,
contain_self_loop=False,
)
gnn_policy = GNNNet(
graph_builder_policy,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
from rlkit.torch.networks.layers import SplitLayer, FlattenLayer
policy = nn.Sequential(
gnn_policy, SelectLayer(dim=1, index=agent), FlattenLayer(),
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=variant['policy_kwargs']['hidden_dim'],
hidden_sizes=[variant['policy_kwargs']['hidden_dim']] *
(variant['policy_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
policy = TanhGaussianPolicy(module=policy)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
if variant['random_exploration']:
from rlkit.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.epsilon_greedy import EpsilonGreedy
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=EpsilonGreedy(expl_env.action_space,
prob_random_action=1.0),
policy=policy,
)
else:
expl_policy = policy
policy_n.append(policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
from rlkit.samplers.data_collector.ma_path_collector import MAMdpPathCollector
eval_path_collector = MAMdpPathCollector(eval_env,
eval_policy_n,
shared_obs=True)
expl_path_collector = MAMdpPathCollector(expl_env,
expl_policy_n,
shared_obs=True)
from rlkit.data_management.ma_env_replay_buffer import MAEnvReplayBuffer
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent,
shared_obs=True)
from rlkit.torch.r2g.r2g_gnn11 import R2GGNNTrainer
trainer = R2GGNNTrainer(env=expl_env,
qf1=qf1,
target_qf1=target_qf1,
qf2=qf2,
target_qf2=target_qf2,
cactor=cactor,
policy_n=policy_n,
shared_obs=True,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm
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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
# save init params
from rlkit.core import logger
snapshot = algorithm._get_snapshot()
file_name = osp.join(logger._snapshot_dir, 'itr_-1.pkl')
torch.save(snapshot, file_name)
algorithm.train()
def experiment(variant):
import sys
sys.path.append("./multiagent-particle-envs")
from make_env import make_env
from particle_env_wrapper import ParticleEnv
expl_env = ParticleEnv(
make_env(args.exp_name,
discrete_action_space=False,
world_args=variant['world_args']))
eval_env = ParticleEnv(
make_env(args.exp_name,
discrete_action_space=False,
world_args=variant['world_args']))
num_agent = expl_env.num_agent
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
from rlkit.torch.networks.graph_builders import FullGraphBuilder
graph_builder_1 = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.graph_context_network import GraphContextNet
cg1 = GraphContextNet(
graph_builder_1,
obs_dim,
action_dim,
use_attention=variant['graph_kwargs']['use_attention'],
num_layer=variant['graph_kwargs']['num_layer'],
node_dim=variant['graph_kwargs']['hidden_dim'],
output_activation='relu',
)
target_cg1 = copy.deepcopy(cg1)
from rlkit.torch.networks.networks import FlattenMlp
qf1 = FlattenMlp(
input_size=variant['graph_kwargs']['hidden_dim'] + action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
)
target_qf1 = copy.deepcopy(qf1)
graph_builder_2 = FullGraphBuilder(
input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
cg2 = GraphContextNet(
graph_builder_2,
obs_dim,
action_dim,
use_attention=variant['graph_kwargs']['use_attention'],
num_layer=variant['graph_kwargs']['num_layer'],
node_dim=variant['graph_kwargs']['hidden_dim'],
output_activation='relu',
)
target_cg2 = copy.deepcopy(cg2)
qf2 = FlattenMlp(
input_size=variant['graph_kwargs']['hidden_dim'] + action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
)
target_qf2 = copy.deepcopy(qf2)
policy_n, expl_policy_n, eval_policy_n = [], [], []
cactor_n = []
for i in range(num_agent):
from rlkit.torch.networks.layers import SplitLayer
if variant['trainer_kwargs']['dec_cactor']:
input_size = obs_dim + action_dim * (num_agent - 1)
else:
input_size = obs_dim * num_agent + action_dim * (num_agent - 1)
cactor = nn.Sequential(
FlattenMlp(
input_size=input_size,
output_size=variant['cactor_kwargs']['hidden_dim'],
hidden_sizes=[variant['cactor_kwargs']['hidden_dim']] *
(variant['cactor_kwargs']['num_layer'] - 1),
),
SplitLayer(layers=[
nn.Linear(variant['cactor_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['cactor_kwargs']['hidden_dim'], action_dim)
]))
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
cactor = TanhGaussianPolicy(module=cactor)
policy = nn.Sequential(
FlattenMlp(
input_size=obs_dim,
output_size=variant['policy_kwargs']['hidden_dim'],
hidden_sizes=[variant['policy_kwargs']['hidden_dim']] *
(variant['policy_kwargs']['num_layer'] - 1),
),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
policy = TanhGaussianPolicy(module=policy)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
expl_policy = policy
policy_n.append(policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
cactor_n.append(cactor)
from rlkit.samplers.data_collector.ma_path_collector import MAMdpPathCollector
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
from rlkit.data_management.ma_env_replay_buffer import MAEnvReplayBuffer
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
from rlkit.torch.r2g.r2g_gnn3_onlyq import R2GGNNTrainer
trainer = R2GGNNTrainer(env=expl_env,
cg1=cg1,
target_cg1=target_cg1,
qf1=qf1,
target_qf1=target_qf1,
cg2=cg2,
target_cg2=target_cg2,
qf2=qf2,
target_qf2=target_qf2,
cactor_n=cactor_n,
policy_n=policy_n,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm
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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
from multi_differential_game import MultiDifferentialGame
expl_env = MultiDifferentialGame(**variant['env_kwargs'])
eval_env = MultiDifferentialGame(**variant['env_kwargs'])
num_agent = expl_env.agent_num
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
from rlkit.torch.networks.graph_builders import FullGraphBuilder
graph_builder_cg1 = FullGraphBuilder(
input_node_dim=obs_dim+action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.gnn_networks import GNNNet
cg1 = GNNNet(
graph_builder_cg1,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
target_cg1 = copy.deepcopy(cg1)
# graph_builder_qf1 = FullGraphBuilder(
# input_node_dim=action_dim+variant['graph_kwargs']['node_dim'],
# num_node=num_agent,
# batch_size=variant['algorithm_kwargs']['batch_size'],
# contain_self_loop=False)
# qf1 = nn.Sequential(
# GNNNet(
# graph_builder_qf1,
# hidden_activation='lrelu0.2',
# output_activation='lrelu0.2',
# **variant['graph_kwargs'],
# ),
# nn.Linear(variant['graph_kwargs']['node_dim'],1)
# )
# target_qf1 = copy.deepcopy(qf1)
from rlkit.torch.networks.networks import FlattenMlp
qf1 = FlattenMlp(input_size=variant['graph_kwargs']['node_dim']+action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']]*(variant['qf_kwargs']['num_layer']-1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
target_qf1 = copy.deepcopy(qf1)
graph_builder_cg2 = FullGraphBuilder(
input_node_dim=obs_dim+action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.gnn_networks import GNNNet
cg2 = GNNNet(
graph_builder_cg2,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
target_cg2 = copy.deepcopy(cg2)
# graph_builder_qf2 = FullGraphBuilder(
# input_node_dim=action_dim+variant['graph_kwargs']['node_dim'],
# num_node=num_agent,
# batch_size=variant['algorithm_kwargs']['batch_size'],
# contain_self_loop=False)
# qf2 = nn.Sequential(
# GNNNet(
# graph_builder_qf2,
# hidden_activation='lrelu0.2',
# output_activation='lrelu0.2',
# **variant['graph_kwargs'],
# ),
# nn.Linear(variant['graph_kwargs']['node_dim'],1)
# )
# target_qf2 = copy.deepcopy(qf2)
qf2 = FlattenMlp(input_size=variant['graph_kwargs']['node_dim']+action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']]*(variant['qf_kwargs']['num_layer']-1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
target_qf2 = copy.deepcopy(qf2)
graph_builder_ca = FullGraphBuilder(
input_node_dim=obs_dim+action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
cgca = GNNNet(
graph_builder_ca,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
from rlkit.torch.networks.networks import FlattenMlp
from rlkit.torch.networks.layers import SplitLayer
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
cactor = nn.Sequential(
cgca,
FlattenMlp(input_size=variant['graph_kwargs']['node_dim'],
output_size=variant['cactor_kwargs']['hidden_dim'],
hidden_sizes=[variant['cactor_kwargs']['hidden_dim']]*(variant['cactor_kwargs']['num_layer']-1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
),
nn.LeakyReLU(negative_slope=0.2),
SplitLayer(layers=[nn.Linear(variant['policy_kwargs']['hidden_dim'],action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'],action_dim)])
)
cactor = TanhGaussianPolicy(module=cactor)
policy_n, expl_policy_n, eval_policy_n = [], [], []
for i in range(num_agent):
policy = nn.Sequential(
FlattenMlp(input_size=obs_dim,
output_size=variant['policy_kwargs']['hidden_dim'],
hidden_sizes=[variant['policy_kwargs']['hidden_dim']]*(variant['policy_kwargs']['num_layer']-1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
),
SplitLayer(layers=[nn.Linear(variant['policy_kwargs']['hidden_dim'],action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'],action_dim)])
)
policy = TanhGaussianPolicy(module=policy)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
if variant['random_exploration']:
from rlkit.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.epsilon_greedy import EpsilonGreedy
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=EpsilonGreedy(expl_env.action_space, prob_random_action=1.0),
policy=policy,
)
else:
expl_policy = policy
policy_n.append(policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
from rlkit.samplers.data_collector.ma_path_collector import MAMdpPathCollector
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
from rlkit.data_management.ma_env_replay_buffer import MAEnvReplayBuffer
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'], expl_env, num_agent=num_agent)
from rlkit.torch.r2g.r2g_gnn10 import R2GGNNTrainer
trainer = R2GGNNTrainer(
env=expl_env,
cg1=cg1,
target_cg1=target_cg1,
qf1=qf1,
target_qf1=target_qf1,
cg2=cg2,
target_cg2=target_cg2,
qf2=qf2,
target_qf2=target_qf2,
cactor=cactor,
policy_n=policy_n,
**variant['trainer_kwargs']
)
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm
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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs']
)
algorithm.to(ptu.device)
# save init params
from rlkit.core import logger
snapshot = algorithm._get_snapshot()
file_name = osp.join(logger._snapshot_dir, 'itr_-1.pkl')
torch.save(snapshot, file_name)
algorithm.train()
def experiment(variant):
import sys
sys.path.append("./multiagent-particle-envs")
from make_env import make_env
from particle_env_wrapper import ParticleEnv
expl_env = ParticleEnv(
make_env(args.exp_name,
discrete_action_space=False,
world_args=variant['world_args']))
eval_env = ParticleEnv(
make_env(args.exp_name,
discrete_action_space=False,
world_args=variant['world_args']))
num_agent = expl_env.num_agent
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
from simple_spread_graph import SimpleSpreadGraphBuilder
og_builder_1 = SimpleSpreadGraphBuilder(
num_agents=expl_env.scenario.num_agents,
num_landmarks=expl_env.scenario.num_landmarks,
batch_size=variant['algorithm_kwargs']['batch_size'],
append_action=False,
single_observe=False,
contain_self_loop=True,
)
from rlkit.torch.networks.gnn_networks import GNNNet
from rlkit.torch.networks.layers import SelectLayer
og1 = nn.Sequential(
GNNNet(
og_builder_1,
node_dim=variant['graph_kwargs']['node_dim'],
conv_type='GSage',
num_conv_layers=variant['graph_kwargs']['num_layer'],
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
),
SelectLayer(dim=1, index=torch.arange(num_agent)),
)
target_og1 = copy.deepcopy(og1)
from rlkit.torch.networks.graph_builders import FullGraphBuilder
cg_builder_1 = FullGraphBuilder(
input_node_dim=variant['graph_kwargs']['node_dim'] + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
from rlkit.torch.networks.graph_context_network import GraphContextNet
cg1 = GraphContextNet(cg_builder_1,
variant['graph_kwargs']['node_dim'],
action_dim,
output_activation='lrelu0.2',
**variant['graph_kwargs'])
target_cg1 = copy.deepcopy(cg1)
from rlkit.torch.networks.networks import FlattenMlp
qf1 = FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'] + action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
target_qf1 = copy.deepcopy(qf1)
og_builder_2 = SimpleSpreadGraphBuilder(
num_agents=expl_env.scenario.num_agents,
num_landmarks=expl_env.scenario.num_landmarks,
batch_size=variant['algorithm_kwargs']['batch_size'],
append_action=False,
single_observe=False,
contain_self_loop=True,
)
from rlkit.torch.networks.gnn_networks import GNNNet
og2 = nn.Sequential(
GNNNet(
og_builder_2,
node_dim=variant['graph_kwargs']['node_dim'],
conv_type='GSage',
num_conv_layers=variant['graph_kwargs']['num_layer'],
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
),
SelectLayer(dim=1, index=torch.arange(num_agent)),
)
target_og2 = copy.deepcopy(og2)
cg_builder_2 = FullGraphBuilder(
input_node_dim=variant['graph_kwargs']['node_dim'] + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
cg2 = GraphContextNet(cg_builder_2,
variant['graph_kwargs']['node_dim'],
action_dim,
output_activation='lrelu0.2',
**variant['graph_kwargs'])
target_cg2 = copy.deepcopy(cg2)
qf2 = FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'] + action_dim,
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
(variant['qf_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
)
target_qf2 = copy.deepcopy(qf2)
og_builder_ca = SimpleSpreadGraphBuilder(
num_agents=expl_env.scenario.num_agents,
num_landmarks=expl_env.scenario.num_landmarks,
batch_size=variant['algorithm_kwargs']['batch_size'],
append_action=False,
single_observe=False,
contain_self_loop=True,
)
from rlkit.torch.networks.gnn_networks import GNNNet
ogca = nn.Sequential(
GNNNet(
og_builder_ca,
node_dim=variant['graph_kwargs']['node_dim'],
conv_type='GSage',
num_conv_layers=variant['graph_kwargs']['num_layer'],
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
),
SelectLayer(dim=1, index=torch.arange(num_agent)),
)
cg_builder_ca = FullGraphBuilder(
input_node_dim=variant['graph_kwargs']['node_dim'] + action_dim,
num_node=num_agent,
batch_size=variant['algorithm_kwargs']['batch_size'],
contain_self_loop=False)
cgca = GraphContextNet(cg_builder_ca,
variant['graph_kwargs']['node_dim'],
action_dim,
output_activation='lrelu0.2',
**variant['graph_kwargs'])
from rlkit.torch.networks.layers import SplitLayer
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
cactor = nn.Sequential(
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=variant['cactor_kwargs']['hidden_dim'],
hidden_sizes=[variant['cactor_kwargs']['hidden_dim']] *
(variant['cactor_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
), nn.LeakyReLU(negative_slope=0.2),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
cactor = TanhGaussianPolicy(module=cactor)
policy_n, expl_policy_n, eval_policy_n = [], [], []
for i in range(num_agent):
graph_builder_policy = SimpleSpreadGraphBuilder(
num_agents=expl_env.scenario.num_agents,
num_landmarks=expl_env.scenario.num_landmarks,
batch_size=variant['algorithm_kwargs']['batch_size'],
append_action=False,
single_observe=True,
contain_self_loop=True,
)
from rlkit.torch.networks.gnn_networks import GNNNet
gnn_policy = GNNNet(
graph_builder_policy,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
conv_type='GSage',
node_dim=variant['graph_kwargs']['node_dim'],
num_conv_layers=args.glayer,
)
from rlkit.torch.networks.layers import SplitLayer, FlattenLayer
policy = nn.Sequential(
gnn_policy, SelectLayer(dim=1, index=0), FlattenLayer(),
FlattenMlp(
input_size=variant['graph_kwargs']['node_dim'],
output_size=variant['policy_kwargs']['hidden_dim'],
hidden_sizes=[variant['policy_kwargs']['hidden_dim']] *
(variant['policy_kwargs']['num_layer'] - 1),
hidden_activation=nn.LeakyReLU(negative_slope=0.2),
output_activation=nn.LeakyReLU(negative_slope=0.2),
),
SplitLayer(layers=[
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim),
nn.Linear(variant['policy_kwargs']['hidden_dim'], action_dim)
]))
policy = TanhGaussianPolicy(module=policy)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy = MakeDeterministic(policy)
if variant['random_exploration']:
from rlkit.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.epsilon_greedy import EpsilonGreedy
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=EpsilonGreedy(expl_env.action_space,
prob_random_action=1.0),
policy=policy,
)
else:
expl_policy = policy
policy_n.append(policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
from rlkit.samplers.data_collector.ma_path_collector import MAMdpPathCollector
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
from rlkit.data_management.ma_env_replay_buffer import MAEnvReplayBuffer
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
from rlkit.torch.r2g.r2g_gnn8 import R2GGNNTrainer
trainer = R2GGNNTrainer(env=expl_env,
og1=og1,
target_og1=target_og1,
cg1=cg1,
target_cg1=target_cg1,
qf1=qf1,
target_qf1=target_qf1,
og2=og2,
target_og2=target_og2,
cg2=cg2,
target_cg2=target_cg2,
qf2=qf2,
target_qf2=target_qf2,
ogca=ogca,
cgca=cgca,
cactor=cactor,
policy_n=policy_n,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm
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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
# save init params
from rlkit.core import logger
snapshot = algorithm._get_snapshot()
file_name = osp.join(logger._snapshot_dir, 'itr_-1.pkl')
torch.save(snapshot, file_name)
algorithm.train()
boundary=([[-1., -1.], [1., 1.]] if args.boundary else None))
import sys
sys.path.append("./multiagent-particle-envs")
from make_env import make_env
from particle_env_wrapper import ParticleEnv
env = ParticleEnv(
make_env(args.exp_name, discrete_action_space=False,
world_args=world_args))
obs_dim = env.observation_space.low.size
action_dim = env.action_space.low.size
num_agent = env.num_agent
from rlkit.torch.networks.graph_builders import FullGraphBuilder
gb = FullGraphBuilder(input_node_dim=obs_dim + action_dim,
num_node=num_agent,
batch_size=256,
contain_self_loop=False)
obs1 = env.reset()
obs2 = env.reset()
obs_batch = torch.tensor([obs1, obs2])
x, edge_index = gb(obs_batch)
print('obs_batch: ', obs_batch)
print('x: ', x)
print('x reshape', x.reshape(2, -1, obs_dim))
print('edge_index: ', edge_index)
data = Data(x=x, edge_index=edge_index)
ng = pyg_utils.to_networkx(data)
networkx.draw_planar(ng)