def train(env_id, num_timesteps, seed, num_cpu, batch, lr):
from rl.common import set_global_seeds
from rl.common.vec_env.vec_normalize import MAVecNormalize
from rl.common.ma_wrappers import MAWrapper
from sandbox.mppo import ppo2
from sandbox.mppo.policies import MlpPolicy
import gym
import tensorflow as tf
from rl.common.vec_env.subproc_vec_env import SubprocVecEnv
ncpu = 1
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=ncpu,
inter_op_parallelism_threads=ncpu)
tf.Session(config=config).__enter__()
def _make_env():
env = gym.make(env_id)
env = MAWrapper(env)
env = bench.Monitor(env, logger.get_dir())
return env
env = SubprocVecEnv([_make_env for _ in range(num_cpu)], is_multi_agent=True)
env = MAVecNormalize(env)
set_global_seeds(seed)
policy = MlpPolicy
ppo2.learn(policy=policy, env=env, nsteps=batch // num_cpu, nminibatches=32,
lam=0.95, gamma=0.99, noptepochs=10, log_interval=1,
ent_coef=0.0,
lr=lr,
cliprange=0.2,
total_timesteps=num_timesteps)
def train(env_id, num_timesteps, seed, num_cpu, batch, lr):
from rl.common import set_global_seeds
from rl.common.vec_env.vec_normalize import MAVecNormalize
from rl.common.ma_wrappers import MAWrapper
from sandbox.mppo import ppo2
from sandbox.mppo.policies import MlpPolicy
import gym
import tensorflow as tf
from rl.common.vec_env.subproc_vec_env import SubprocVecEnv
ncpu = 1
config = tf.ConfigProto(allow_soft_placement=True,
intra_op_parallelism_threads=ncpu,
inter_op_parallelism_threads=ncpu)
tf.Session(config=config).__enter__()
def _make_env(rank):
env = gym.make('RoboSumo-Ant-vs-Ant-v0')
env = bench.Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
return env
env = SubprocVecEnv([lambda: _make_env(i) for i in range(num_cpu)], is_multi_agent=True)
env = MAVecNormalize(env)
set_global_seeds(seed)
policy = MlpPolicy
expert = MADataSet('/atlas/u/tsong/Projects/imitation/ant-vs-ant.pkl')
ppo2.learn(policy=policy, env=env, nsteps=batch // num_cpu, nminibatches=160,
lam=0.95, gamma=0.99, noptepochs=10, log_interval=1,
ent_coef=0.0,
lr=lr,
cliprange=0.2,
total_timesteps=num_timesteps, expert=expert, clone_iters=1000)