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run_humanoid.py
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run_humanoid.py
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#!/usr/bin/env python3
import os
from baselines.common.cmd_util import make_mujoco_env, mujoco_arg_parser
from baselines.common import tf_util as U
from baselines import logger
import gym
def train(num_timesteps, seed, model_path=None):
env_id = 'Humanoid-v2'
from baselines.ppo1 import mlp_policy, pposgd_simple
U.make_session(num_cpu=1).__enter__()
def policy_fn(name, ob_space, ac_space):
return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=64, num_hid_layers=2)
env = make_mujoco_env(env_id, seed)
# parameters below were the best found in a simple random search
# these are good enough to make humanoid walk, but whether those are
# an absolute best or not is not certain
env = RewScale(env, 0.1)
logger.log("NOTE: reward will be scaled by a factor of 10 in logged stats. Check the monitor for unscaled reward.")
pi = pposgd_simple.learn(env, policy_fn,
max_timesteps=num_timesteps,
timesteps_per_actorbatch=2048,
clip_param=0.1, entcoeff=0.0,
optim_epochs=10,
optim_stepsize=1e-4,
optim_batchsize=64,
gamma=0.99,
lam=0.95,
schedule='constant',
)
env.close()
if model_path:
U.save_state(model_path)
return pi
class RewScale(gym.RewardWrapper):
def __init__(self, env, scale):
gym.RewardWrapper.__init__(self, env)
self.scale = scale
def reward(self, r):
return r * self.scale
def main():
logger.configure()
parser = mujoco_arg_parser()
parser.add_argument('--model-path', default=os.path.join(logger.get_dir(), 'humanoid_policy'))
parser.set_defaults(num_timesteps=int(5e7))
args = parser.parse_args()
if not args.play:
# train the model
train(num_timesteps=args.num_timesteps, seed=args.seed, model_path=args.model_path)
else:
# construct the model object, load pre-trained model and render
pi = train(num_timesteps=1, seed=args.seed)
U.load_state(args.model_path)
env = make_mujoco_env('Humanoid-v2', seed=0)
ob = env.reset()
while True:
action = pi.act(stochastic=False, ob=ob)[0]
ob, _, done, _ = env.step(action)
env.render()
if done:
ob = env.reset()
if __name__ == '__main__':
main()