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eager_main_ofePaper_ppo.py
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eager_main_ofePaper_ppo.py
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import argparse
import logging
import os
import shutil
import sys
import time
import gin
import gym
import numpy as np
import tensorflow as tf
import datetime, pytz
import src.util.gin_utils as gin_utils
from src.aux.dummy_extractor import DummyFeatureExtractor
from src.aux.munk_extractor import MunkNet
from src.aux.network_ofePaper import OFENet
from src.policy import DDPG
from src.policy import PPO
from src.policy import SAC
from src.policy import TD3
from src.util import misc
from src.util import replay
from src.util.misc import get_target_dim, make_ofe_name, get_default_steps
misc.set_gpu_device_growth()
dir_of_env = {'HalfCheetah-v2': 'hc',
'Hopper-v2': 'hopper',
'Walker2d-v2': 'walker',
'Ant-v2': 'ant',
'Swimmer-v2': 'swim',
'Humanoid-v2': 'human',
"InvertedDoublePendulum-v2": 'IDPen'}
def evaluate_policy(env, policy, eval_episodes=10):
avg_reward = 0.
episode_length = []
for _ in range(eval_episodes):
state = env.reset()
cur_length = 0
done = False
while not done:
action = policy.select_action(np.array(state))
state, reward, done, _ = env.step(action)
avg_reward += reward
cur_length += 1
episode_length.append(cur_length)
avg_reward /= eval_episodes
avg_length = np.average(episode_length)
return avg_reward, avg_length
def make_exp_name(args):
if args.gin is not None:
extractor_name = gin.query_parameter("feature_extractor.name")
if extractor_name == "OFE":
ofe_unit = gin.query_parameter("OFENet.total_units")
ofe_layer = gin.query_parameter("OFENet.num_layers")
ofe_act = gin.query_parameter("OFENet.activation")
ofe_block = gin.query_parameter("OFENet.block")
ofe_act = str(ofe_act).split(".")[-1]
ofe_name = make_ofe_name(ofe_layer, ofe_unit, ofe_act, ofe_block)
elif extractor_name == "Munk":
munk_size = gin.query_parameter("MunkNet.internal_states")
ofe_name = "Munk_{}".format(munk_size)
else:
raise ValueError("invalid extractor name {}".format(extractor_name))
ofe_name = 'ofePaper_' + ofe_name
else:
ofe_name = "raw"
env_name = args.env.split("-")[0]
exp_name = "{}_{}_{}".format(env_name, args.policy, ofe_name)
if args.name is not None:
exp_name = exp_name + "_" + args.name
exp_name += "_up{}".format(str(args.update_every))
return exp_name
def make_policy(policy, env_name, extractor, units=256):
env = gym.make(env_name)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
max_action = float(env.action_space.high[0])
n_units = [units, units]
if policy == "SAC":
scale_reward = SAC.get_default_scale_reward(env_name)
policy = SAC.SAC(state_dim, action_dim, max_action,
feature_extractor=extractor,
scale_reward=scale_reward,
actor_units=n_units, q_units=n_units, v_units=n_units)
print("We use SAC algorithm!")
elif policy == "DDPG":
policy = DDPG.DDPG(state_dim, action_dim, max_action, feature_extractor=extractor)
print("We use DDPG algorithm!")
elif policy == "PPO":
policy = PPO.PPO(state_dim, action_dim, max_action, feature_extractor=extractor)
print("We use PPO algorithm!")
elif policy == "TD3":
policy = TD3.TD3(state_dim, action_dim, max_action, layer_units=(400, 300), feature_extractor=extractor)
print("We use TD3 algorithm!")
elif policy == "TD3small":
policy = TD3.TD3(state_dim, action_dim, max_action, layer_units=(256, 256), feature_extractor=extractor)
else:
raise ValueError("invalid policy {}".format(policy))
return policy
def make_output_dir(dir_root, exp_name, env_name, seed, ignore_errors):
seed_name = "seed{}".format(seed)
dir_log = os.path.join(dir_root, "log_{}".format(dir_of_env[env_name]), exp_name, seed_name)
for cur_dir in [dir_log]:
if os.path.exists(cur_dir):
if ignore_errors:
shutil.rmtree(cur_dir, ignore_errors=True)
else:
raise ValueError("output directory {} exists".format(cur_dir))
os.makedirs(cur_dir)
return dir_log
@gin.configurable
def feature_extractor(env_name, dim_state, dim_action, normalizer='batch', name=None, skip_action_branch=False):
logger = logging.getLogger(name="main")
logger.info("Use Extractor {}".format(name))
if name == "OFE":
target_dim = get_target_dim(env_name)
extractor = OFENet(dim_state=dim_state, dim_action=dim_action,
dim_output=target_dim, normalizer=normalizer, skip_action_branch=skip_action_branch)
# print network parameters of OFENet
print("OFENet's network structure:\n")
tvars = extractor.trainable_variables
for var in tvars:
print(" name = %s, shape = %s" % (var.name, var.shape))
elif name == "Munk":
extractor = MunkNet(dim_state=dim_state, dim_action=dim_action)
else:
extractor = DummyFeatureExtractor(dim_state=dim_state, dim_action=dim_action)
return extractor
def main(args):
logger = logging.Logger(name="main")
handler = logging.StreamHandler()
handler.setLevel(logging.INFO)
handler.setFormatter(logging.Formatter(fmt='%(asctime)s [%(levelname)s] (%(filename)s:%(lineno)s) %(message)s',
datefmt="%m/%d %I:%M:%S"))
logger.addHandler(handler)
logger.setLevel(logging.INFO)
# CONSTANTS
if args.gin is not None:
gin.parse_config_file(args.gin)
max_steps = args.steps
summary_freq = 1000
eval_freq = 5000
random_collect = 4000
if eval_freq % summary_freq != 0:
logger.error("eval_freq must be divisible by summary_freq.")
sys.exit(-1)
seed = args.seed
max_steps = args.steps = get_default_steps(args.env)
epochs = max_steps // args.steps_per_epoch + 1
total_steps = args.steps_per_epoch*epochs
steps_per_epoch = args.steps_per_epoch
save_freq = args.save_freq
env_name = args.env
policy_name = args.policy
batch_size = args.batch_size
dir_root = args.dir_root
exp_name = make_exp_name(args)
logger.info("Start Experiment {}".format(exp_name))
dir_log = make_output_dir(dir_root=dir_root, exp_name=exp_name, env_name=args.env, seed=seed,
ignore_errors=args.force)
eval_env = gym.make(env_name)
eval_env.seed(seed + 1000)
# Set seeds
env = gym.make(env_name)
env.seed(seed)
# tf.set_random_seed(args.seed)
tf.random.set_seed(seed)
np.random.seed(seed)
dim_state = env.observation_space.shape[0]
dim_action = env.action_space.shape[0]
extractor = feature_extractor(env_name, dim_state, dim_action, args.normalizer)
# Makes a summary writer before graph construction
writer = tf.summary.create_file_writer(dir_log)
writer.set_as_default()
# Initialize policy
policy = make_policy(policy=policy_name, env_name=env_name, extractor=extractor, units=args.sac_units)
replay_buffer = replay.PPOBuffer(obs_dim=dim_state, act_dim=dim_action, size=steps_per_epoch, gamma=args.discount, lam=args.lam)
gin_utils.write_gin_to_summary(dir_log, global_step=0)
total_timesteps = np.array(0, dtype=np.int32)
episode_timesteps = 0
episode_return = 0
state = env.reset()
logger.info("collecting random {} transitions".format(random_collect))
print("Initialization: I am collecting samples randomly!")
for i in range(random_collect):
action = env.action_space.sample()
next_state, reward, done, _ = env.step(action)
episode_return += reward
episode_timesteps += 1
total_timesteps += 1
done_flag = done
if episode_timesteps == env._max_episode_steps:
done_flag = False
replay_buffer.add(obs=state, act=action, obs2=next_state, rew=reward, done=done_flag, val=0, logp=0)
state = next_state
if done:
state = env.reset()
episode_timesteps = 0
episode_return = 0
# pretraining the extractor
print("Pretrain: I am pretraining the extractor!")
for i in range(random_collect):
sample_states, sample_actions, sample_next_states, sample_rewards, sample_dones = replay_buffer.sample(
batch_size=batch_size)
extractor.train(sample_states, sample_actions, sample_next_states, sample_rewards, sample_dones)
state = np.array(state, dtype=np.float32)
prev_calc_time = time.time()
prev_calc_step = random_collect
replay_buffer.get()
print("Train: I am starting to train myself!")
# should_summary = lambda: tf.equal(total_timesteps % summary_freq, 0)
# with tf.summary.record_if(should_summary):
for cur_steps in range(total_steps):
action, logp, v_t = policy.get_action_and_val(state)
# Step the env
next_state, reward, done, _ = env.step(action)
episode_timesteps += 1
episode_return += reward
total_timesteps += 1
tf.summary.experimental.set_step(total_timesteps)
done_flag = done
# done is valid, when an episode is not finished by max_step.
if episode_timesteps == env._max_episode_steps:
done_flag = False
replay_buffer.add(obs=state, act=action, obs2=next_state, rew=reward, done=done_flag, val=v_t, logp=logp)
state = next_state
if done or (episode_timesteps == env._max_episode_steps) or (cur_steps + 1) % steps_per_epoch == 0:
# if trajectory didn't reach terminal state, bootstrap value target
last_val = 0 if done else policy.get_val(state)
replay_buffer.finish_path(last_val)
state = env.reset()
logger.info("Time {} : Sample Steps {} Reward {}".format(int(total_timesteps), episode_timesteps,
episode_return))
with tf.summary.record_if(True):
tf.summary.scalar(name="performance/exploration_steps", data=episode_timesteps,
description="Exploration Episode Length")
tf.summary.scalar(name="performance/exploration_return", data=episode_return,
description="Exploration Episode Return")
episode_timesteps = 0
episode_return = 0
if args.gin is not None and cur_steps % args.update_every == 0:
for _ in range(args.update_every):
sample_states, sample_actions, sample_next_states, sample_rewards, sample_dones = replay_buffer.sample(
batch_size=batch_size)
extractor.train(sample_states, sample_actions, sample_next_states, sample_rewards, sample_dones)
if (cur_steps + 1) % steps_per_epoch == 0:
policy.train(replay_buffer)
if cur_steps % eval_freq == 0:
duration = time.time() - prev_calc_time
duration_steps = cur_steps - prev_calc_step
throughput = duration_steps / float(duration)
logger.info("Throughput {:.2f} ({:.2f} secs)".format(throughput, duration))
cur_evaluate, average_length = evaluate_policy(eval_env, policy)
logger.info("Evaluate Time {} : Average Reward {}".format(int(total_timesteps), cur_evaluate))
with tf.summary.record_if(True):
tf.summary.scalar(name="performance/evaluate_return", data=cur_evaluate,
description="Evaluate for test dataset")
tf.summary.scalar(name="performance/evaluate_steps", data=average_length,
description="Step length during evaluation")
tf.summary.scalar(name="throughput", data=throughput, description="Throughput. Steps per Second.")
prev_calc_time = time.time()
prev_calc_step = cur_steps
# store model
if args.save_model == True and cur_steps % save_freq == 0:
model_save_dir = os.path.join(dir_log, 'model')
policy.save(model_save_dir)
if args.gin is not None:
extractor.save_weights(os.path.join(model_save_dir,'extractor_model'))
print('Models have been saved.')
tf.summary.flush()
if __name__ == "__main__":
logging.basicConfig(datefmt="%d/%Y %I:%M:%S", level=logging.INFO,
format='%(asctime)s [%(levelname)s] (%(filename)s:%(lineno)s) %(message)s'
)
parser = argparse.ArgumentParser()
parser.add_argument("--policy", default="DDPG")
parser.add_argument("--env", default="HalfCheetah-v2")
parser.add_argument("--seed", default=0, type=int)
parser.add_argument('--cpu', type=int, default=4)
parser.add_argument("--steps", default=1000000, type=int)
parser.add_argument("--sac-units", default=256, type=int)
parser.add_argument("--batch_size", default=256, type=int)
parser.add_argument("--gin", default=None)
parser.add_argument("--name", default=None, type=str)
parser.add_argument("--force", default=False, action="store_true",
help="remove existed directory")
parser.add_argument("--dir-root", default="output", type=str)
parser.add_argument("--save_model", default=False, action="store_true")
parser.add_argument("--save_freq", default=100000, type=int)
parser.add_argument("--steps_per_epoch", default=4000, type=int)
parser.add_argument("--lam", default=0.97, type=float)
parser.add_argument("--discount", default=0.99, type=float)
parser.add_argument("--update_every", default=1, type=int)
parser.add_argument("--normalizer", default='layer', type=str, choices=['layer', 'batch'])
args = parser.parse_args()
main(args)