111 lines
2.8 KiB
Python
111 lines
2.8 KiB
Python
"""The PPO training configuration file for minitaur environments."""
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
import functools
|
|
from agents import ppo
|
|
from agents.scripts import networks
|
|
from pybullet_envs.bullet import minitaur_gym_env
|
|
from pybullet_envs.bullet import minitaur_env_randomizer
|
|
import pybullet_envs.bullet.minitaur_gym_env as minitaur_gym_env
|
|
import pybullet_envs
|
|
|
|
|
|
# pylint: disable=unused-variable
|
|
def default():
|
|
"""The default configurations."""
|
|
# General
|
|
algorithm = ppo.PPOAlgorithm
|
|
num_agents = 25
|
|
eval_episodes = 25
|
|
use_gpu = False
|
|
# Network
|
|
network = networks.ForwardGaussianPolicy
|
|
weight_summaries = dict(
|
|
all=r'.*', policy=r'.*/policy/.*', value=r'.*/value/.*')
|
|
policy_layers = 200, 100
|
|
value_layers = 200, 100
|
|
init_mean_factor = 0.2
|
|
init_logstd = -1
|
|
network_config = dict()
|
|
# Optimization
|
|
update_every = 25
|
|
policy_optimizer = 'AdamOptimizer'
|
|
value_optimizer = 'AdamOptimizer'
|
|
update_epochs_policy = 25
|
|
update_epochs_value = 25
|
|
value_lr = 1e-3
|
|
policy_lr = 1e-4
|
|
# Losses
|
|
discount = 0.99
|
|
kl_target = 1e-2
|
|
kl_cutoff_factor = 2
|
|
kl_cutoff_coef = 1000
|
|
kl_init_penalty = 1
|
|
return locals()
|
|
|
|
|
|
def pybullet_pendulum():
|
|
locals().update(default())
|
|
env = 'InvertedPendulumBulletEnv-v0'
|
|
max_length = 200
|
|
steps = 5e7 # 50M
|
|
return locals()
|
|
|
|
def pybullet_doublependulum():
|
|
locals().update(default())
|
|
env = 'InvertedDoublePendulumBulletEnv-v0'
|
|
max_length = 1000
|
|
steps = 5e7 # 50M
|
|
return locals()
|
|
|
|
def pybullet_pendulumswingup():
|
|
locals().update(default())
|
|
env = 'InvertedPendulumSwingupBulletEnv-v0'
|
|
max_length = 1000
|
|
steps = 5e7 # 50M
|
|
return locals()
|
|
|
|
def pybullet_cheetah():
|
|
"""Configuration for MuJoCo's half cheetah task."""
|
|
locals().update(default())
|
|
# Environment
|
|
env = 'HalfCheetahBulletEnv-v0'
|
|
max_length = 1000
|
|
steps = 1e8 # 100M
|
|
return locals()
|
|
|
|
def pybullet_ant():
|
|
locals().update(default())
|
|
env = 'AntBulletEnv-v0'
|
|
max_length = 1000
|
|
steps = 5e7 # 50M
|
|
return locals()
|
|
|
|
def pybullet_racecar():
|
|
"""Configuration for Bullet MIT Racecar task."""
|
|
locals().update(default())
|
|
# Environment
|
|
env = 'RacecarBulletEnv-v0' #functools.partial(racecarGymEnv.RacecarGymEnv, isDiscrete=False, renders=True)
|
|
max_length = 10
|
|
steps = 1e7 # 10M
|
|
return locals()
|
|
|
|
|
|
def pybullet_minitaur():
|
|
"""Configuration specific to minitaur_gym_env.MinitaurBulletEnv class."""
|
|
locals().update(default())
|
|
randomizer = (minitaur_env_randomizer.MinitaurEnvRandomizer())
|
|
env = functools.partial(
|
|
minitaur_gym_env.MinitaurBulletEnv,
|
|
accurate_motor_model_enabled=True,
|
|
motor_overheat_protection=True,
|
|
pd_control_enabled=True,
|
|
env_randomizer=randomizer,
|
|
render=False)
|
|
max_length = 1000
|
|
steps = 3e7 # 30M
|
|
return locals()
|
|
|
|
|