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
qf_n, policy_n, target_qf_n, target_policy_n, eval_policy_n, expl_policy_n = \
[], [], [], [], [], []
qf2_n, target_qf2_n = [], []
for i in range(num_agent):
from rlkit.torch.networks import FlattenMlp
qf = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] * 2,
)
target_qf = copy.deepcopy(qf)
from rlkit.torch.policies.deterministic_policies import TanhMlpPolicy
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
hidden_sizes=[variant['policy_kwargs']['hidden_dim']] * 2,
)
target_policy = copy.deepcopy(policy)
eval_policy = 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:
from rlkit.exploration_strategies.ou_strategy import OUStrategy
expl_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=OUStrategy(
action_space=expl_env.action_space),
policy=policy,
)
qf_n.append(qf)
policy_n.append(policy)
target_qf_n.append(target_qf)
target_policy_n.append(target_policy)
eval_policy_n.append(eval_policy)
expl_policy_n.append(expl_policy)
if variant['trainer_kwargs']['double_q']:
qf2 = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] * 2,
)
target_qf2 = copy.deepcopy(qf2)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
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.maddpg.maddpg import MADDPGTrainer
trainer = MADDPGTrainer(qf_n=qf_n,
target_qf_n=target_qf_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_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_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):
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)
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=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)
]))
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)
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.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,
**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("./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)
policy_n, expl_policy_n, eval_policy_n = [], [], []
for i in range(num_agent):
from rlkit.torch.networks.layers import SplitLayer
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)])
)
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)
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.masac.masac_gnn_gcontext import MASACGNNTrainer
trainer = MASACGNNTrainer(
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,
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,
contain_self_loop=False)
from rlkit.torch.networks.graph_context_network import GraphContextNet
cg1 = GraphContextNet(
graph_builder_1,
obs_dim,
action_dim,
node_dim=variant['graph_kwargs']['hidden_dim'],
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 = GraphContextNet(
graph_builder_2,
obs_dim,
action_dim,
node_dim=variant['graph_kwargs']['hidden_dim'],
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)
cgca = GraphContextNet(
graph_builder_ca,
obs_dim,
action_dim,
node_dim=variant['graph_kwargs']['hidden_dim'],
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):
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
policy_n, vf_n = [], []
policy_optimizer_n, vf_optimizer_n = None, None
for i in range(num_agent):
from rlkit.torch.networks.networks import FlattenMlp
from rlkit.torch.networks.layers import SplitLayer
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)])
)
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
policy = TanhGaussianPolicy(module=policy)
vf = FlattenMlp(
input_size=obs_dim,
output_size=1,
hidden_sizes=[variant['vf_kwargs']['hidden_dim']]*variant['vf_kwargs']['num_layer'],
)
policy_n.append(policy)
vf_n.append(vf)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy_n = [MakeDeterministic(policy) for policy in policy_n]
if variant['random_exploration']:
from rlkit.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.epsilon_greedy import EpsilonGreedy
expl_policy_n = [PolicyWrappedWithExplorationStrategy(
exploration_strategy=EpsilonGreedy(expl_env.action_space, prob_random_action=1.0),
policy=policy,
) for i in range(num_agent)]
else:
expl_policy_n = policy_n
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.torch.irl.irl_ppo import IRLPPOTrainer
trainer = IRLPPOTrainer(
env = expl_env,
policy_n=policy_n,
vf_n=vf_n,
policy_optimizer_n=policy_optimizer_n,
vf_optimizer_n=vf_optimizer_n,
**variant['trainer_kwargs']
)
from rlkit.torch.torch_rl_algorithm import TorchOnlineRLAlgorithm
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_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_1 = 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
gnn1 = GNNNet(
graph_builder_1,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
from rlkit.torch.networks.networks import FlattenMlp
from rlkit.torch.networks.layers import SelectLayer
qf1 = nn.Sequential(
gnn1, SelectLayer(dim=1, index=torch.arange(num_agent)),
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)
graph_builder_2 = 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,
)
gnn2 = GNNNet(
graph_builder_2,
hidden_activation='lrelu0.2',
output_activation='lrelu0.2',
**variant['graph_kwargs'],
)
qf2 = nn.Sequential(
gnn2, SelectLayer(dim=1, index=torch.arange(num_agent)),
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)
policy_n, eval_policy_n, expl_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)
]))
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_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.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_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)
algorithm.train()
def experiment(variant):
num_agent = variant['num_agent']
from sequential_differential_game import SequentialDifferentialGame
expl_env = SequentialDifferentialGame(**variant['env_kwargs'])
eval_env = SequentialDifferentialGame(**variant['env_kwargs'])
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
policy_n, eval_policy_n, expl_policy_n, qf1_n, target_qf1_n, qf2_n, target_qf2_n = \
[], [], [], [], [], [], []
for i in range(num_agent):
from rlkit.torch.layers import SplitLayer, ReshapeLayer
weight_head = nn.Linear(variant['policy_kwargs']['hidden_dim'],
variant['policy_kwargs']['m'])
mean_head = nn.Sequential(
nn.Linear(variant['policy_kwargs']['hidden_dim'],
action_dim * variant['policy_kwargs']['m']),
ReshapeLayer(shape=[variant['policy_kwargs']['m'], action_dim]))
logstd_head = nn.Sequential(
nn.Linear(variant['policy_kwargs']['hidden_dim'],
action_dim * variant['policy_kwargs']['m']),
ReshapeLayer(shape=[variant['policy_kwargs']['m'], action_dim]))
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=[weight_head, mean_head, logstd_head]))
from rlkit.torch.policies.mix_tanh_gaussian_policy import MixTanhGaussianPolicy
policy = MixTanhGaussianPolicy(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
from rlkit.torch.networks import FlattenMlp
qf1 = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] * 2,
)
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] * 2,
)
target_qf2 = copy.deepcopy(qf2)
policy_n.append(policy)
eval_policy_n.append(eval_policy)
expl_policy_n.append(expl_policy)
qf1_n.append(qf1)
target_qf1_n.append(target_qf1)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
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.masac.masac import MASACTrainer
trainer = MASACTrainer(env=expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_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 cartpole import CartPoleEnv
from rlkit.envs.ma_wrappers import MAProbDiscreteEnv
expl_env = CartPoleEnv(mode=3)
eval_env = CartPoleEnv(mode=3)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
qf1_n, qf2_n, cactor_n, policy_n, target_qf1_n, target_qf2_n, target_policy_n, expl_policy_n, eval_policy_n = \
[], [], [], [], [], [], [], [], []
for i in range(num_agent):
qf1 = FlattenMlp(input_size=(obs_dim * num_agent +
action_dim * num_agent),
output_size=1,
**variant['qf_kwargs'])
qf2 = FlattenMlp(input_size=(obs_dim * num_agent +
action_dim * num_agent),
output_size=1,
**variant['qf_kwargs'])
cactor = TanhGaussianPolicy(obs_dim=(obs_dim * num_agent + action_dim *
(num_agent - 1)),
action_dim=action_dim,
**variant['cactor_kwargs'])
policy = TanhGaussianPolicy(obs_dim=obs_dim,
action_dim=action_dim,
**variant['policy_kwargs'])
target_qf1 = copy.deepcopy(qf1)
target_qf2 = copy.deepcopy(qf2)
target_policy = copy.deepcopy(policy)
expl_policy = policy
eval_policy = MakeDeterministic(policy)
qf1_n.append(qf1)
qf2_n.append(qf2)
cactor_n.append(cactor)
policy_n.append(policy)
target_qf1_n.append(target_qf1)
target_qf2_n.append(target_qf2)
target_policy_n.append(target_policy)
expl_policy_n.append(expl_policy)
eval_policy_n.append(eval_policy)
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
trainer = PRGTrainer(env=expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
cactor_n=cactor_n,
**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,
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("./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
policy_n, vf_n = [], []
policy_optimizer_n, vf_optimizer_n = None, None
for i in range(num_agent):
from rlkit.torch.networks.networks import FlattenMlp
from rlkit.torch.networks.layers import SplitLayer
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)
]))
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
policy = TanhGaussianPolicy(module=policy, return_raw_action=True)
vf = FlattenMlp(
input_size=obs_dim,
output_size=1,
hidden_sizes=[variant['vf_kwargs']['hidden_dim']] *
variant['vf_kwargs']['num_layer'],
)
policy_n.append(policy)
vf_n.append(vf)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy_n = [MakeDeterministic(policy) for policy in policy_n]
expl_policy_n = policy_n
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,
collect_raw_actions=True)
from rlkit.torch.irl.irl_ppo import IRLPPOTrainer
trainer = IRLPPOTrainer(env=expl_env,
policy_n=policy_n,
vf_n=vf_n,
policy_optimizer_n=policy_optimizer_n,
vf_optimizer_n=vf_optimizer_n,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchOnlineRLAlgorithm
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_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
import gym
import robosumo.envs
from robosumo_env_wrapper import RoboSumoEnv
expl_env = RoboSumoEnv(
gym.make('RoboSumo-{}-vs-{}-v0'.format(args.exp_name, args.exp_name)),
**variant['world_args'])
eval_env = RoboSumoEnv(
gym.make('RoboSumo-{}-vs-{}-v0'.format(args.exp_name, args.exp_name)),
**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
policy_n, qf_n = [], []
policy_optimizer_n, qf_optimizer_n = None, None
for i in range(num_agent):
from rlkit.torch.networks.networks import FlattenMlp
from rlkit.torch.networks.layers import SplitLayer
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)
]))
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
policy = TanhGaussianPolicy(module=policy, return_raw_action=True)
qf = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
variant['qf_kwargs']['num_layer'],
)
policy_n.append(policy)
qf_n.append(qf)
from rlkit.torch.policies.make_deterministic import MakeDeterministic
eval_policy_n = [MakeDeterministic(policy) for policy in policy_n]
expl_policy_n = policy_n
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,
collect_raw_actions=True)
from rlkit.torch.coma.coma import COMATrainer
trainer = COMATrainer(env=expl_env,
policy_n=policy_n,
qf_n=qf_n,
policy_optimizer_n=policy_optimizer_n,
qf_optimizer_n=qf_optimizer_n,
**variant['trainer_kwargs'])
from rlkit.torch.torch_rl_algorithm import TorchOnlineRLAlgorithm
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_generic_ma_path_information,
**variant['algorithm_kwargs'])
algorithm.to(ptu.device)
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
policy_n, qf1_n, target_qf1_n, qf2_n, target_qf2_n = \
[], [], [], [], []
log_alpha_n = None
qf1_optimizer_n, qf2_optimizer_n, policy_optimizer_n, alpha_optimizer_n = \
None, None, None, None
for i in range(num_agent):
from rlkit.torch.networks.networks import FlattenMlp
from rlkit.torch.networks.layers import SplitLayer
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)])
)
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
policy = TanhGaussianPolicy(module=policy)
qf1 = FlattenMlp(
input_size=(obs_dim+action_dim),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']]*variant['qf_kwargs']['num_layer'],
)
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(
input_size=(obs_dim+action_dim),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']]*variant['qf_kwargs']['num_layer'],
)
target_qf2 = copy.deepcopy(qf2)
policy_n.append(policy)
qf1_n.append(qf1)
target_qf1_n.append(target_qf1)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
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.policies.make_deterministic import MakeDeterministic
eval_policy_n = [MakeDeterministic(policy) for policy in policy_n]
expl_policy_n = policy_n
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.torch.irl.irl_sac import IRLSACTrainer
trainer = IRLSACTrainer(
env = expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
log_alpha_n=log_alpha_n,
qf1_optimizer_n=qf1_optimizer_n,
qf2_optimizer_n=qf2_optimizer_n,
policy_optimizer_n=policy_optimizer_n,
alpha_optimizer_n=alpha_optimizer_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("./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
if variant['load_kwargs']['load']:
load_dir = variant['load_kwargs']['load_dir']
load_epoch = variant['load_kwargs']['load_epoch']
load_data = torch.load('{}/itr_{}.pkl'.format(load_dir, load_epoch),
map_location='cpu')
qf1_n = load_data['trainer/qf1_n']
target_qf1_n = load_data['trainer/target_qf1_n']
qf2_n = load_data['trainer/qf2_n']
target_qf2_n = load_data['trainer/target_qf2_n']
cactor_n = load_data['trainer/cactor_n']
policy_n = load_data['trainer/policy_n']
log_alpha_n = load_data['trainer/log_alpha_n']
qf1_optimizer_n = load_data['trainer/qf1_optimizer_n']
qf2_optimizer_n = load_data['trainer/qf2_optimizer_n']
policy_optimizer_n = load_data['trainer/policy_optimizer_n']
cactor_optimizer_n = load_data['trainer/cactor_optimizer_n']
alpha_optimizer_n = load_data['trainer/alpha_optimizer_n']
if args.ce:
log_calpha_n = load_data['trainer/log_calpha_n']
calpha_optimizer_n = load_data['trainer/calpha_optimizer_n']
replay_buffer = load_data['replay_buffer']
else:
qf1_n, qf2_n, cactor_n, policy_n = [], [], [], []
target_qf1_n, target_qf2_n = [], []
log_alpha_n, log_calpha_n = None, None
qf1_optimizer_n, qf2_optimizer_n, policy_optimizer_n, cactor_optimizer_n, alpha_optimizer_n, calpha_optimizer_n = \
None, None, None, None, None, None
for i in range(num_agent):
from rlkit.torch.networks import FlattenMlp
qf1 = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
variant['qf_kwargs']['num_layer'],
)
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
variant['qf_kwargs']['num_layer'],
)
target_qf2 = copy.deepcopy(qf2)
from rlkit.torch.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)
qf1_n.append(qf1)
qf2_n.append(qf2)
cactor_n.append(cactor)
policy_n.append(policy)
target_qf1_n.append(target_qf1)
target_qf2_n.append(target_qf2)
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.policies.make_deterministic import MakeDeterministic
eval_policy_n = [MakeDeterministic(policy) for policy in policy_n]
expl_policy_n = policy_n
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.torch.prg.prg import PRGTrainer
trainer = PRGTrainer(env=expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
cactor_n=cactor_n,
log_alpha_n=log_alpha_n,
log_calpha_n=log_calpha_n,
qf1_optimizer_n=qf1_optimizer_n,
qf2_optimizer_n=qf2_optimizer_n,
policy_optimizer_n=policy_optimizer_n,
cactor_optimizer_n=cactor_optimizer_n,
alpha_optimizer_n=alpha_optimizer_n,
calpha_optimizer_n=calpha_optimizer_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 sequential_differential_game import SequentialDifferentialGame
expl_env = SequentialDifferentialGame(**variant['env_kwargs'])
eval_env = SequentialDifferentialGame(**variant['env_kwargs'])
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_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.graph_context_network import GraphContextNet
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',
)
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']['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)])
)
cactor = TanhGaussianPolicy(module=cactor)
policy_n, expl_policy_n, eval_policy_n = [], [], []
qf1_n, qf2_n, target_qf1_n, target_qf2_n = [], [], [], []
for i in range(num_agent):
qf1 = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']]*variant['qf_kwargs']['num_layer'],
)
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']]*variant['qf_kwargs']['num_layer'],
)
target_qf2 = copy.deepcopy(qf2)
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)
qf2_n.append(qf2)
target_qf1_n.append(target_qf1)
target_qf2_n.append(target_qf2)
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_onlyca import R2GGNNTrainer
trainer = R2GGNNTrainer(
env=expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
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):
num_agent = variant['num_agent']
from rlkit.envs.zmq_env import ZMQEnv
expl_env = ZMQEnv(variant['port'])
eval_env = expl_env
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
qf_n, qf2_n, policy_n, target_qf_n, target_qf2_n, target_policy_n, eval_policy_n, expl_policy_n = \
[], [], [], [], [], [], [], []
for i in range(num_agent):
qf = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
**variant['qf_kwargs']
)
qf2 = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
**variant['qf_kwargs']
)
policy = GumbelSoftmaxMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
target_qf = copy.deepcopy(qf)
target_qf2 = copy.deepcopy(qf2)
target_policy = copy.deepcopy(policy)
eval_policy = ArgmaxDiscretePolicy(policy,use_preactivation=True)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space),
eval_policy,
)
qf_n.append(qf)
qf2_n.append(qf2)
policy_n.append(policy)
target_qf_n.append(target_qf)
target_qf2_n.append(target_qf2)
target_policy_n.append(target_policy)
eval_policy_n.append(eval_policy)
expl_policy_n.append(expl_policy)
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'], expl_env, num_agent=num_agent)
trainer = MADDPGTrainer(
qf_n=qf_n,
target_qf_n=target_qf_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
**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,
log_path_function=get_ma_path_information,
**variant['algorithm_kwargs']
)
algorithm.to(ptu.device)
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=True,
discrete_action_input=True))
eval_env = ParticleEnv(
make_env(args.exp_name,
discrete_action_space=True,
discrete_action_input=True))
num_agent = expl_env.num_agent
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
policy_n, target_policy_n, qf1_n, target_qf1_n, qf2_n, target_qf2_n, eval_policy_n, expl_policy_n = \
[], [], [], [], [], [], [], []
for i in range(num_agent):
policy = SoftmaxMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'])
target_policy = copy.deepcopy(policy)
qf1 = FlattenMlp(input_size=(obs_dim * num_agent + action_dim *
(num_agent - 1)),
output_size=action_dim,
**variant['qf_kwargs'])
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(input_size=(obs_dim * num_agent + action_dim *
(num_agent - 1)),
output_size=action_dim,
**variant['qf_kwargs'])
target_qf2 = copy.deepcopy(qf2)
eval_policy = ArgmaxDiscretePolicy(policy)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space),
eval_policy,
)
policy_n.append(policy)
target_policy_n.append(target_policy)
qf1_n.append(qf1)
target_qf1_n.append(target_qf1)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
eval_policy_n.append(eval_policy)
expl_policy_n.append(expl_policy)
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'],
expl_env,
num_agent=num_agent)
trainer = PRGDiscreteTrainer(env=expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
**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,
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("./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
graph_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=True,
single_observe=False,
contain_self_loop=False,
)
from rlkit.torch.networks.graph_context_network import GraphContextNet
from rlkit.torch.networks.layers import SelectLayer
cg1= nn.Sequential(
GraphContextNet(
graph_builder_1,
graph_builder_1.output_node_dim-action_dim,
action_dim,
output_activation='lrelu0.2',
**variant['graph_kwargs']
),
SelectLayer(dim=1, index=torch.arange(num_agent)),
)
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 = SimpleSpreadGraphBuilder(
num_agents=expl_env.scenario.num_agents,
num_landmarks=expl_env.scenario.num_landmarks,
batch_size=variant['algorithm_kwargs']['batch_size'],
append_action=True,
single_observe=False,
contain_self_loop=False,
)
cg2 = nn.Sequential(
GraphContextNet(
graph_builder_2,
graph_builder_2.output_node_dim-action_dim,
action_dim,
output_activation='lrelu0.2',
**variant['graph_kwargs']
),
SelectLayer(dim=1, index=torch.arange(num_agent)),
)
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 = SimpleSpreadGraphBuilder(
num_agents=expl_env.scenario.num_agents,
num_landmarks=expl_env.scenario.num_landmarks,
batch_size=variant['algorithm_kwargs']['batch_size'],
append_action=True,
single_observe=False,
contain_self_loop=False,
)
from rlkit.torch.networks.gnn_networks import GNNNet
cgca = nn.Sequential(
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',
),
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(
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):
import gym
import robosumo.envs
from robosumo_env_wrapper import RoboSumoEnv
expl_env = RoboSumoEnv(
gym.make('RoboSumo-{}-vs-{}-v0'.format(args.exp_name, args.exp_name)),
**variant['world_args'])
eval_env = RoboSumoEnv(
gym.make('RoboSumo-{}-vs-{}-v0'.format(args.exp_name, args.exp_name)),
**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
qf1_n, qf2_n, cactor_n, policy_n = [], [], [], []
target_qf1_n, target_qf2_n = [], []
log_alpha_n, log_calpha_n = None, None
qf1_optimizer_n, qf2_optimizer_n, policy_optimizer_n, cactor_optimizer_n, alpha_optimizer_n, calpha_optimizer_n = \
None, None, None, None, None, None
for i in range(num_agent):
from rlkit.torch.networks.networks import FlattenMlp
qf1 = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
variant['qf_kwargs']['num_layer'],
)
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
variant['qf_kwargs']['num_layer'],
)
target_qf2 = copy.deepcopy(qf2)
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)
qf1_n.append(qf1)
qf2_n.append(qf2)
cactor_n.append(cactor)
policy_n.append(policy)
target_qf1_n.append(target_qf1)
target_qf2_n.append(target_qf2)
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.policies.make_deterministic import MakeDeterministic
eval_policy_n = [MakeDeterministic(policy) for policy in policy_n]
expl_policy_n = policy_n
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.torch.r2g.r2g import R2GTrainer
trainer = R2GTrainer(env=expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
cactor_n=cactor_n,
log_alpha_n=log_alpha_n,
log_calpha_n=log_calpha_n,
qf1_optimizer_n=qf1_optimizer_n,
qf2_optimizer_n=qf2_optimizer_n,
policy_optimizer_n=policy_optimizer_n,
cactor_optimizer_n=cactor_optimizer_n,
alpha_optimizer_n=alpha_optimizer_n,
calpha_optimizer_n=calpha_optimizer_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 cartpole import CartPoleEnv
expl_env = CartPoleEnv(mode=3)
eval_env = CartPoleEnv(mode=3)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.low.size
qf_n, policy_n, target_qf_n, target_policy_n, exploration_policy_n = \
[], [], [], [], []
qf2_n, target_qf2_n = [], []
for i in range(num_agent):
qf = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
**variant['qf_kwargs']
)
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
target_qf = copy.deepcopy(qf)
target_policy = copy.deepcopy(policy)
exploration_policy = PolicyWrappedWithExplorationStrategy(
exploration_strategy=OUStrategy(action_space=expl_env.action_space),
policy=policy,
)
qf_n.append(qf)
policy_n.append(policy)
target_qf_n.append(target_qf)
target_policy_n.append(target_policy)
exploration_policy_n.append(exploration_policy)
if variant['trainer_kwargs']['double_q']:
qf2 = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
**variant['qf_kwargs']
)
target_qf2 = copy.deepcopy(qf2)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
eval_path_collector = MAMdpPathCollector(eval_env, policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, exploration_policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'], expl_env, num_agent=num_agent)
trainer = MADDPGTrainer(
qf_n=qf_n,
target_qf_n=target_qf_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
qf2_n = qf2_n,
target_qf2_n = target_qf2_n,
**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,
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
policy_n, qf1_n, target_qf1_n, qf2_n, target_qf2_n = \
[], [], [], [], []
log_alpha_n = None
qf1_optimizer_n, qf2_optimizer_n, policy_optimizer_n, alpha_optimizer_n = \
None, None, None, None
for i in range(num_agent):
from rlkit.torch.networks.networks import FlattenMlp
from rlkit.torch.networks.layers import SplitLayer
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)])
)
from rlkit.torch.policies.tanh_gaussian_policy import TanhGaussianPolicy
policy = TanhGaussianPolicy(module=policy)
qf1 = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']]*variant['qf_kwargs']['num_layer'],
)
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']]*variant['qf_kwargs']['num_layer'],
)
target_qf2 = copy.deepcopy(qf2)
policy_n.append(policy)
qf1_n.append(qf1)
target_qf1_n.append(target_qf1)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
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.policies.make_deterministic import MakeDeterministic
eval_policy_n = [MakeDeterministic(policy) for policy in policy_n]
if variant['random_exploration']:
from rlkit.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.epsilon_greedy import EpsilonGreedy
expl_policy_n = [PolicyWrappedWithExplorationStrategy(
exploration_strategy=EpsilonGreedy(expl_env.action_space, prob_random_action=1.0),
policy=policy,
) for i in range(num_agent)]
else:
expl_policy_n = policy_n
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.torch.masac.masac import MASACTrainer
trainer = MASACTrainer(
env = expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
log_alpha_n=log_alpha_n,
qf1_optimizer_n=qf1_optimizer_n,
qf2_optimizer_n=qf2_optimizer_n,
policy_optimizer_n=policy_optimizer_n,
alpha_optimizer_n=alpha_optimizer_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("./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
if variant['load_kwargs']['load']:
load_dir = variant['load_kwargs']['load_dir']
load_epoch = variant['load_kwargs']['load_epoch']
load_data = torch.load('{}/itr_{}.pkl'.format(load_dir, load_epoch),
map_location='cpu')
qf_n = load_data['trainer/qf_n']
target_qf_n = load_data['trainer/target_qf_n']
qf2_n, target_qf2_n = [], []
policy_n = load_data['trainer/policy_n']
target_policy_n = load_data['trainer/target_policy_n']
qf_optimizer_n = load_data['trainer/qf_optimizer_n']
qf2_optimizer_n = None
policy_optimizer_n = load_data['trainer/policy_optimizer_n']
replay_buffer = load_data['replay_buffer']
else:
qf_n, policy_n, target_qf_n, target_policy_n, eval_policy_n, expl_policy_n = \
[], [], [], [], [], []
qf2_n, target_qf2_n = [], []
qf_optimizer_n, qf2_optimizer_n, policy_optimizer_n = None, None, None
for i in range(num_agent):
from rlkit.torch.networks.networks import FlattenMlp
qf = FlattenMlp(
input_size=(obs_dim * num_agent + action_dim * num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']] *
variant['qf_kwargs']['num_layer'],
)
target_qf = copy.deepcopy(qf)
from rlkit.torch.policies.deterministic_policies import TanhMlpPolicy
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
hidden_sizes=[variant['policy_kwargs']['hidden_dim']] *
variant['policy_kwargs']['num_layer'],
)
target_policy = copy.deepcopy(policy)
qf_n.append(qf)
policy_n.append(policy)
target_qf_n.append(target_qf)
target_policy_n.append(target_policy)
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.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.ou_strategy import OUStrategy
expl_policy_n = [
PolicyWrappedWithExplorationStrategy(
exploration_strategy=OUStrategy(
action_space=expl_env.action_space),
policy=policy,
) for policy in policy_n
]
eval_policy_n = policy_n
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.torch.maddpg.maddpg import MADDPGTrainer
trainer = MADDPGTrainer(qf_n=qf_n,
target_qf_n=target_qf_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
qf_optimizer_n=qf_optimizer_n,
qf2_optimizer_n=qf2_optimizer_n,
policy_optimizer_n=policy_optimizer_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 gym
import robosumo.envs
from robosumo_env_wrapper import RoboSumoEnv
expl_env = RoboSumoEnv(gym.make('RoboSumo-{}-vs-{}-v0'.format(args.exp_name,args.exp_name)),**variant['world_args'])
eval_env = RoboSumoEnv(gym.make('RoboSumo-{}-vs-{}-v0'.format(args.exp_name,args.exp_name)),**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
qf_n, policy_n, target_qf_n, target_policy_n, eval_policy_n, expl_policy_n = \
[], [], [], [], [], []
qf2_n, target_qf2_n = [], []
qf_optimizer_n, qf2_optimizer_n, policy_optimizer_n = None, None, None
for i in range(num_agent):
from rlkit.torch.networks.networks import FlattenMlp
qf = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*num_agent),
output_size=1,
hidden_sizes=[variant['qf_kwargs']['hidden_dim']]*variant['qf_kwargs']['num_layer'],
)
target_qf = copy.deepcopy(qf)
from rlkit.torch.policies.deterministic_policies import TanhMlpPolicy
policy = TanhMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
hidden_sizes=[variant['policy_kwargs']['hidden_dim']]*variant['policy_kwargs']['num_layer'],
)
target_policy = copy.deepcopy(policy)
qf_n.append(qf)
policy_n.append(policy)
target_qf_n.append(target_qf)
target_policy_n.append(target_policy)
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.exploration_strategies.base import PolicyWrappedWithExplorationStrategy
from rlkit.exploration_strategies.ou_strategy import OUStrategy
expl_policy_n = [PolicyWrappedWithExplorationStrategy(
exploration_strategy=OUStrategy(action_space=expl_env.action_space),
policy=policy,
) for policy in policy_n]
eval_policy_n = policy_n
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.torch.maddpg.maddpg import MADDPGTrainer
trainer = MADDPGTrainer(
qf_n=qf_n,
target_qf_n=target_qf_n,
policy_n=policy_n,
target_policy_n=target_policy_n,
qf2_n = qf2_n,
target_qf2_n = target_qf2_n,
qf_optimizer_n=qf_optimizer_n,
qf2_optimizer_n=qf2_optimizer_n,
policy_optimizer_n=policy_optimizer_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("./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)
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)
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,
output_activation='lrelu0.2',
**variant['graph_kwargs'])
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']['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)
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)
from rlkit.torch.networks.layers import SplitLayer
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),
), 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
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),
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)
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):
from matrix_game import MatrixGame
expl_env = MatrixGame(game_name=args.exp_name)
eval_env = MatrixGame(game_name=args.exp_name)
num_agent = eval_env.agent_num
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
policy_n, qf1_n, target_qf1_n, qf2_n, target_qf2_n, eval_policy_n, expl_policy_n = \
[], [], [], [], [], [], []
for i in range(num_agent):
policy = SoftmaxMlpPolicy(
input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs']
)
qf1 = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*(num_agent-1)),
output_size=action_dim,
**variant['qf_kwargs']
)
target_qf1 = copy.deepcopy(qf1)
qf2 = FlattenMlp(
input_size=(obs_dim*num_agent+action_dim*(num_agent-1)),
output_size=action_dim,
**variant['qf_kwargs']
)
target_qf2 = copy.deepcopy(qf2)
eval_policy = ArgmaxDiscretePolicy(policy)
expl_policy = PolicyWrappedWithExplorationStrategy(
EpsilonGreedy(expl_env.action_space),
eval_policy,
)
policy_n.append(policy)
qf1_n.append(qf1)
target_qf1_n.append(target_qf1)
qf2_n.append(qf2)
target_qf2_n.append(target_qf2)
eval_policy_n.append(eval_policy)
expl_policy_n.append(expl_policy)
eval_path_collector = MAMdpPathCollector(eval_env, eval_policy_n)
expl_path_collector = MAMdpPathCollector(expl_env, expl_policy_n)
replay_buffer = MAEnvReplayBuffer(variant['replay_buffer_size'], expl_env, num_agent=num_agent)
trainer = MASACDiscreteTrainer(
env = expl_env,
qf1_n=qf1_n,
target_qf1_n=target_qf1_n,
qf2_n=qf2_n,
target_qf2_n=target_qf2_n,
policy_n=policy_n,
**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,
log_path_function=get_generic_ma_path_information,
**variant['algorithm_kwargs']
)
algorithm.to(ptu.device)
algorithm.train()
