def experiment(variant):
num_agent = variant['num_agent']
from cartpole import CartPoleEnv
expl_env = CartPoleEnv(mode=4)
eval_env = CartPoleEnv(mode=4)
obs_dim = eval_env.observation_space.low.size
action_dim = eval_env.action_space.n
qf_n, policy_n, target_qf_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'])
policy = GumbelSoftmaxMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'])
target_qf = copy.deepcopy(qf)
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)
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)
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,
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):
num_agent = variant['num_agent']
from cartpole import CartPoleEnv
from rlkit.envs.ma_wrappers import MAProbDiscreteEnv
expl_env = MAProbDiscreteEnv(CartPoleEnv(mode=4))
eval_env = MAProbDiscreteEnv(CartPoleEnv(mode=4))
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 = \
[], [], [], [], []
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 = SoftmaxMlpPolicy(input_size=obs_dim,
output_size=action_dim,
**variant['policy_kwargs'])
target_qf = copy.deepcopy(qf)
target_policy = copy.deepcopy(policy)
exploration_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)
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,
**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
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
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 = [
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 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
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):
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()