def get_venv(args, env_id, num_env, seed, hps):
env_type = args.env
if env_type == 'pass':
env = make_multi_pass_env(env_id, env_type, num_env, seed, args)
elif env_type == 'threepass':
env = make_m_three_pass_env(env_id, env_type, num_env, seed, args)
elif env_type == 'island':
env = make_m_island_env(env_id, env_type, num_env, seed, args)
elif env_type == 'x_island':
env = make_m_x_island_env(env_id, env_type, num_env, seed, args)
elif env_type == 'pushball':
env = make_m_pushball_env(env_id, env_type, num_env, seed, args)
venv = VecFrameStack(env, hps.pop('frame_stack'))
return venv
def train(*, env_id, num_env, hps, num_timesteps, seed, use_reward, ep_path,
dmlab):
venv = VecFrameStack(
DummyVecEnv([
lambda: CollectGymDataset(Minecraft('MineRLTreechop-v0', 'treechop'
),
ep_path,
atari=False)
]), hps.pop('frame_stack'))
venv.score_multiple = 1
venv.record_obs = True if env_id == 'SolarisNoFrameskip-v4' else False
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop('gamma')
policy = {'rnn': CnnGruPolicy, 'cnn': CnnPolicy}[hps.pop('policy')]
print('Running train ==========================================')
agent = PpoAgent(
scope='ppo',
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope='pol',
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop(
'update_ob_stats_independently_per_gpu'),
proportion_of_exp_used_for_predictor_update=hps.pop(
'proportion_of_exp_used_for_predictor_update'),
dynamics_bonus=hps.pop("dynamics_bonus")),
gamma=gamma,
gamma_ext=hps.pop('gamma_ext'),
lam=hps.pop('lam'),
nepochs=hps.pop('nepochs'),
nminibatches=hps.pop('nminibatches'),
lr=hps.pop('lr'),
cliprange=0.1,
nsteps=128,
ent_coef=0.001,
max_grad_norm=hps.pop('max_grad_norm'),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop('update_ob_stats_every_step'),
int_coeff=hps.pop('int_coeff'),
ext_coeff=hps.pop('ext_coeff'))
agent.start_interaction([venv])
if hps.pop('update_ob_stats_from_random_agent'):
agent.collect_random_statistics(num_timesteps=128 * 50)
assert len(hps) == 0, "Unused hyperparameters: %s" % list(hps.keys())
counter = 0
while True:
info = agent.step()
if info['update']:
logger.logkvs(info['update'])
logger.dumpkvs()
counter += 1
if agent.I.stats['tcount'] > num_timesteps:
break
agent.stop_interaction()
def train(*, env_id, num_env, hps, num_timesteps, seed):
venv = VecFrameStack(
make_atari_env(env_id, num_env, seed, wrapper_kwargs=dict(),
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop('max_episode_steps')),
hps.pop('frame_stack'))
# venv.score_multiple = {'Mario': 500,
# 'MontezumaRevengeNoFrameskip-v4': 100,
# 'GravitarNoFrameskip-v4': 250,
# 'PrivateEyeNoFrameskip-v4': 500,
# 'SolarisNoFrameskip-v4': None,
# 'VentureNoFrameskip-v4': 200,
# 'PitfallNoFrameskip-v4': 100,
# }[env_id]
venv.score_multiple = 1
venv.record_obs = True
# venv.record_obs = True if env_id == 'SolarisNoFrameskip-v4' else False
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop('gamma')
policy = {'rnn': CnnGruPolicy,
'cnn': CnnPolicy}[hps.pop('policy')]
agent = PpoAgent(
scope='ppo',
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope='pol',
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop('update_ob_stats_independently_per_gpu'),
proportion_of_exp_used_for_predictor_update=hps.pop('proportion_of_exp_used_for_predictor_update'),
dynamics_bonus = hps.pop("dynamics_bonus")
),
gamma=gamma,
gamma_ext=hps.pop('gamma_ext'),
lam=hps.pop('lam'),
nepochs=hps.pop('nepochs'),
nminibatches=hps.pop('nminibatches'),
lr=hps.pop('lr'),
cliprange=0.1,
nsteps=128,
ent_coef=0.001,
max_grad_norm=hps.pop('max_grad_norm'),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop('update_ob_stats_every_step'),
int_coeff=hps.pop('int_coeff'),
ext_coeff=hps.pop('ext_coeff'),
)
agent.start_interaction([venv])
if hps.pop('update_ob_stats_from_random_agent'):
agent.collect_random_statistics(num_timesteps=128*50)
assert len(hps) == 0, "Unused hyperparameters: %s" % list(hps.keys())
counter = 0
while True:
info = agent.step()
if info['update']:
logger.logkvs(info['update'])
logger.dumpkvs()
counter += 1
if agent.I.stats['tcount'] > num_timesteps:
break
agent.stop_interaction()
return agent
def train(*, env_id, num_env, hps, num_timesteps, seed):
venv = VecFrameStack(
make_atari_env(env_id,
num_env,
seed,
wrapper_kwargs=dict(),
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop('max_episode_steps')),
hps.pop('frame_stack'))
venv.score_multiple = 1
venv.record_obs = False
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop('gamma')
policy = {'rnn': CnnGruPolicy, 'cnn': CnnPolicy}[hps.pop('policy')]
agent = PpoAgent(
scope='ppo',
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope='pol',
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop(
'update_ob_stats_independently_per_gpu'),
proportion_of_exp_used_for_predictor_update=hps.pop(
'proportion_of_exp_used_for_predictor_update'),
exploration_type=hps.pop("exploration_type"),
beta=hps.pop("beta"),
),
gamma=gamma,
gamma_ext=hps.pop('gamma_ext'),
lam=hps.pop('lam'),
nepochs=hps.pop('nepochs'),
nminibatches=hps.pop('nminibatches'),
lr=hps.pop('lr'),
cliprange=0.1,
nsteps=128,
ent_coef=0.001,
max_grad_norm=hps.pop('max_grad_norm'),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop('update_ob_stats_every_step'),
int_coeff=hps.pop('int_coeff'),
ext_coeff=hps.pop('ext_coeff'),
noise_type=hps.pop('noise_type'),
noise_p=hps.pop('noise_p'),
use_sched=hps.pop('use_sched'),
num_env=num_env,
exp_name=hps.pop('exp_name'),
)
agent.start_interaction([venv])
if hps.pop('update_ob_stats_from_random_agent'):
agent.collect_random_statistics(num_timesteps=128 * 50)
assert len(hps) == 0, "Unused hyperparameters: %s" % list(hps.keys())
counter = 0
while True:
info = agent.step()
n_updates = 0
if info['update']:
logger.logkvs(info['update'])
logger.dumpkvs()
if NSML:
n_updates = int(info['update']['n_updates'])
nsml_dict = {
k: np.float64(v)
for k, v in info['update'].items()
if isinstance(v, Number)
}
nsml.report(step=n_updates, **nsml_dict)
counter += 1
#if n_updates >= 40*1000: # 40K updates
# break
if agent.I.stats['tcount'] > num_timesteps:
break
agent.stop_interaction()
def train(*, env_id, num_env, hps, num_timesteps, seed):
if "NoFrameskip" in env_id:
env_factory = make_atari_env
else:
env_factory = make_non_atari_env
venv = VecFrameStack(
env_factory(
env_id,
num_env,
seed,
wrapper_kwargs=dict(),
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop("max_episode_steps"),
),
hps.pop("frame_stack"),
)
# venv.score_multiple = {'Mario': 500,
# 'MontezumaRevengeNoFrameskip-v4': 100,
# 'GravitarNoFrameskip-v4': 250,
# 'PrivateEyeNoFrameskip-v4': 500,
# 'SolarisNoFrameskip-v4': None,
# 'VentureNoFrameskip-v4': 200,
# 'PitfallNoFrameskip-v4': 100,
# }[env_id]
venv.score_multiple = 1
venv.record_obs = True if env_id == "SolarisNoFrameskip-v4" else False
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop("gamma")
policy = {
"rnn": CnnGruPolicy,
"cnn": CnnPolicy,
'ffnn': GruPolicy
}[hps.pop("policy")]
agent = PpoAgent(
scope="ppo",
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope="pol",
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop(
"update_ob_stats_independently_per_gpu"),
proportion_of_exp_used_for_predictor_update=hps.pop(
"proportion_of_exp_used_for_predictor_update"),
dynamics_bonus=hps.pop("dynamics_bonus"),
meta_rl=hps['meta_rl']),
gamma=gamma,
gamma_ext=hps.pop("gamma_ext"),
lam=hps.pop("lam"),
nepochs=hps.pop("nepochs"),
nminibatches=hps.pop("nminibatches"),
lr=hps.pop("lr"),
cliprange=0.1,
nsteps=128,
ent_coef=0.001,
max_grad_norm=hps.pop("max_grad_norm"),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop("update_ob_stats_every_step"),
int_coeff=hps.pop("int_coeff"),
ext_coeff=hps.pop("ext_coeff"),
meta_rl=hps.pop('meta_rl'))
agent.start_interaction([venv])
if hps.pop("update_ob_stats_from_random_agent"):
agent.collect_random_statistics(num_timesteps=128 * 50)
assert len(hps) == 0, f"Unused hyperparameters: {list(hps.keys())}"
counter = 0
while True:
info = agent.step()
if info["update"]:
logger.logkvs(info["update"])
logger.dumpkvs()
counter += 1
if agent.I.stats["tcount"] > num_timesteps:
break
agent.stop_interaction()
def train(*, env_id, num_env, hps, num_timesteps, seed):
venv = VecFrameStack(
make_atari_env(env_id,
num_env,
seed,
wrapper_kwargs=dict(),
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop('max_episode_steps')),
hps.pop('frame_stack'))
# Size of states when stored in the memory.
only_train_r = hps.pop('only_train_r')
online_r_training = hps.pop('online_train_r') or only_train_r
r_network_trainer = None
save_path = hps.pop('save_path')
r_network_weights_path = hps.pop('r_path')
'''
ec_type = 'none' # hps.pop('ec_type')
venv = CuriosityEnvWrapperFrameStack(
make_atari_env(env_id, num_env, seed, wrapper_kwargs=dict(),
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop('max_episode_steps')),
vec_episodic_memory = None,
observation_embedding_fn = None,
exploration_reward = ec_type,
exploration_reward_min_step = 0,
nstack = hps.pop('frame_stack'),
only_train_r = only_train_r
)
'''
# venv.score_multiple = {'Mario': 500,
# 'MontezumaRevengeNoFrameskip-v4': 100,
# 'GravitarNoFrameskip-v4': 250,
# 'PrivateEyeNoFrameskip-v4': 500,
# 'SolarisNoFrameskip-v4': None,
# 'VentureNoFrameskip-v4': 200,
# 'PitfallNoFrameskip-v4': 100,
# }[env_id]
venv.score_multiple = 1
venv.record_obs = True if env_id == 'SolarisNoFrameskip-v4' else False
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop('gamma')
log_interval = hps.pop('log_interval')
nminibatches = hps.pop('nminibatches')
play = hps.pop('play')
if play:
nsteps = 1
rnd_type = hps.pop('rnd_type')
div_type = hps.pop('div_type')
num_agents = hps.pop('num_agents')
load_ram = hps.pop('load_ram')
debug = hps.pop('debug')
rnd_mask_prob = hps.pop('rnd_mask_prob')
rnd_mask_type = hps.pop('rnd_mask_type')
indep_rnd = hps.pop('indep_rnd')
logger.info("indep_rnd:", indep_rnd)
indep_policy = hps.pop('indep_policy')
sd_type = hps.pop('sd_type')
from_scratch = hps.pop('from_scratch')
use_kl = hps.pop('use_kl')
save_interval = 100
policy = {'rnn': CnnGruPolicy, 'cnn': CnnPolicy}[hps.pop('policy')]
agent = PpoAgent(
scope='ppo',
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope='pol',
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop(
'update_ob_stats_independently_per_gpu'),
proportion_of_exp_used_for_predictor_update=hps.pop(
'proportion_of_exp_used_for_predictor_update'),
dynamics_bonus=hps.pop("dynamics_bonus"),
num_agents=num_agents,
rnd_type=rnd_type,
div_type=div_type,
indep_rnd=indep_rnd,
indep_policy=indep_policy,
sd_type=sd_type,
rnd_mask_prob=rnd_mask_prob),
gamma=gamma,
gamma_ext=hps.pop('gamma_ext'),
gamma_div=hps.pop('gamma_div'),
lam=hps.pop('lam'),
nepochs=hps.pop('nepochs'),
nminibatches=nminibatches,
lr=hps.pop('lr'),
cliprange=0.1,
nsteps=5 if debug else 128,
ent_coef=0.001,
max_grad_norm=hps.pop('max_grad_norm'),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop('update_ob_stats_every_step'),
int_coeff=hps.pop('int_coeff'),
ext_coeff=hps.pop('ext_coeff'),
log_interval=log_interval,
only_train_r=only_train_r,
rnd_type=rnd_type,
reset=hps.pop('reset'),
dynamics_sample=hps.pop('dynamics_sample'),
save_path=save_path,
num_agents=num_agents,
div_type=div_type,
load_ram=load_ram,
debug=debug,
rnd_mask_prob=rnd_mask_prob,
rnd_mask_type=rnd_mask_type,
sd_type=sd_type,
from_scratch=from_scratch,
use_kl=use_kl,
indep_rnd=indep_rnd)
load_path = hps.pop('load_path')
base_load_path = hps.pop('base_load_path')
agent.start_interaction([venv])
if load_path is not None:
if play:
agent.load(load_path)
else:
#agent.load(load_path)
#agent.load_help_info(0, load_path)
#agent.load_help_info(1, load_path)
#load diversity agent
#base_agent_idx = 1
#logger.info("load base agents weights from {} agent {}".format(base_load_path, str(base_agent_idx)))
#agent.load_agent(base_agent_idx, base_load_path)
#agent.clone_baseline_agent(base_agent_idx)
#agent.load_help_info(0, dagent_load_path)
#agent.clone_agent(0)
#load main agen1
src_agent_idx = 1
logger.info("load main agent weights from {} agent {}".format(
load_path, str(src_agent_idx)))
agent.load_agent(src_agent_idx, load_path)
if indep_rnd == False:
rnd_agent_idx = 1
else:
rnd_agent_idx = src_agent_idx
#rnd_agent_idx = 0
logger.info("load rnd weights from {} agent {}".format(
load_path, str(rnd_agent_idx)))
agent.load_rnd(rnd_agent_idx, load_path)
agent.clone_agent(rnd_agent_idx,
rnd=True,
policy=False,
help_info=False)
logger.info("load help info from {} agent {}".format(
load_path, str(src_agent_idx)))
agent.load_help_info(src_agent_idx, load_path)
agent.clone_agent(src_agent_idx,
rnd=False,
policy=True,
help_info=True)
#logger.info("load main agent weights from {} agent {}".format(load_path, str(2)))
#load_path = '/data/xupeifrom7700_1000/seed1_log0.5_clip-0.5~0.5_3agent_hasint4_2divrew_-1~1/models'
#agent.load_agent(1, load_path)
#agent.clone_baseline_agent()
#if sd_type =='sd':
# agent.load_sd("save_dir/models_sd_trained")
#agent.initialize_discriminator()
update_ob_stats_from_random_agent = hps.pop(
'update_ob_stats_from_random_agent')
if play == False:
if load_path is not None:
pass #agent.collect_statistics_from_model()
else:
if update_ob_stats_from_random_agent and rnd_type == 'rnd':
agent.collect_random_statistics(num_timesteps=128 *
5 if debug else 128 * 50)
assert len(hps) == 0, "Unused hyperparameters: %s" % list(hps.keys())
#agent.collect_rnd_info(128*50)
'''
if sd_type=='sd':
agent.train_sd(max_nepoch=300, max_neps=5)
path = '{}_sd_trained'.format(save_path)
logger.log("save model:",path)
agent.save(path)
return
#agent.update_diverse_agent(max_nepoch=1000)
#path = '{}_divupdated'.format(save_path)
#logger.log("save model:",path)
#agent.save(path)
'''
counter = 0
while True:
info = agent.step()
n_updates = agent.I.stats["n_updates"]
if info['update']:
logger.logkvs(info['update'])
logger.dumpkvs()
counter += 1
if info['update'] and save_path is not None and (
n_updates % save_interval == 0 or n_updates == 1):
path = '{}_{}'.format(save_path, str(n_updates))
logger.log("save model:", path)
agent.save(path)
agent.save_help_info(save_path, n_updates)
if agent.I.stats['tcount'] > num_timesteps:
path = '{}_{}'.format(save_path, str(n_updates))
logger.log("save model:", path)
agent.save(path)
agent.save_help_info(save_path, n_updates)
break
agent.stop_interaction()
else:
'''
check_point_rews_list_path ='{}_rewslist'.format(load_path)
check_point_rnd_path ='{}_rnd'.format(load_path)
oracle_rnd = oracle.OracleExplorationRewardForAllEpisodes()
oracle_rnd.load(check_point_rnd_path)
#print(oracle_rnd._collected_positions_writer)
#print(oracle_rnd._collected_positions_reader)
rews_list = load_rews_list(check_point_rews_list_path)
print(rews_list)
'''
istate = agent.stochpol.initial_state(1)
#ph_mean, ph_std = agent.stochpol.get_ph_mean_std()
last_obs, prevrews, ec_rews, news, infos, ram_states, _ = agent.env_get(
0)
agent.I.step_count += 1
flag = False
show_cam = True
last_xr = 0
restore = None
'''
#path = 'ram_state_500_7room'
#path='ram_state_400_6room'
#path='ram_state_6700'
path='ram_state_7700_10room'
f = open(path,'rb')
restore = pickle.load(f)
f.close()
last_obs[0] = agent.I.venvs[0].restore_full_state_by_idx(restore,0)
print(last_obs.shape)
#path = 'ram_state_400_monitor_rews_6room'
#path = 'ram_state_500_monitor_rews_7room'
#path='ram_state_6700_monitor_rews'
path='ram_state_7700_monitor_rews_10room'
f = open(path,'rb')
monitor_rews = pickle.load(f)
f.close()
agent.I.venvs[0].set_cur_monitor_rewards_by_idx(monitor_rews,0)
'''
agent_idx = np.asarray([0])
sample_agent_prob = np.asarray([0.5])
ph_mean = agent.stochpol.ob_rms_list[0].mean
ph_std = agent.stochpol.ob_rms_list[0].var**0.5
buf_ph_mean = np.zeros(
([1, 1] + list(agent.stochpol.ob_space.shape[:2]) + [1]),
np.float32)
buf_ph_std = np.zeros(
([1, 1] + list(agent.stochpol.ob_space.shape[:2]) + [1]),
np.float32)
buf_ph_mean[0, 0] = ph_mean
buf_ph_std[0, 0] = ph_std
vpreds_ext_list = []
ep_rews = np.zeros((1))
divexp_flag = False
step_count = 0
stage_prob = True
last_rew_ob = np.full_like(last_obs, 128)
clusters = Clusters(1.0)
#path = '{}_sd_rms'.format(load_path)
#agent.I.sd_rms.load(path)
while True:
dict_obs = agent.stochpol.ensure_observation_is_dict(last_obs)
#acs= np.random.randint(low=0, high=15, size=(1))
acs, vpreds_int, vpreds_ext, nlps, istate, ent = agent.stochpol.call(
dict_obs, news, istate, agent_idx[:, None])
step_acs = acs
t = ''
#if show_cam==True:
t = input("input:")
if t != '':
t = int(t)
if t <= 17:
step_acs = [t]
agent.env_step(0, step_acs)
obs, prevrews, ec_rews, news, infos, ram_states, monitor_rews = agent.env_get(
0)
if news[0] and restore is not None:
obs[0] = agent.I.venvs[0].restore_full_state_by_idx(restore, 0)
agent.I.venvs[0].set_cur_monitor_rewards_by_idx(
monitor_rews, 0)
ep_rews = ep_rews + prevrews
print(ep_rews)
last_rew_ob[prevrews > 0] = obs[prevrews > 0]
room = infos[0]['position'][2]
vpreds_ext_list.append([vpreds_ext, room])
#print(monitor_rews[0])
#print(len(monitor_rews[0]))
#print(infos[0]['open_door_type'])
stack_obs = np.concatenate([last_obs[:, None], obs[:, None]], 1)
fd = {}
fd[agent.stochpol.ph_ob[None]] = stack_obs
fd.update({
agent.stochpol.sep_ph_mean: buf_ph_mean,
agent.stochpol.sep_ph_std: buf_ph_std
})
fd[agent.stochpol.ph_agent_idx] = agent_idx[:, None]
fd[agent.stochpol.sample_agent_prob] = sample_agent_prob[:, None]
fd[agent.stochpol.last_rew_ob] = last_rew_ob[:, None]
fd[agent.stochpol.game_score] = ep_rews[:, None]
fd[agent.stochpol.sd_ph_mean] = agent.I.sd_rms.mean
fd[agent.stochpol.sd_ph_std] = agent.I.sd_rms.var**0.5
div_prob = 0
all_div_prob = tf_util.get_session().run(
[agent.stochpol.all_div_prob], fd)
'''
if prevrews[0] > 0:
clusters.update(rnd_em,room)
num_clusters = len(clusters._cluster_list)
for i in range(num_clusters):
print("{} {}".format(str(i),list(clusters._room_set[i])))
'''
print("vpreds_int: ", vpreds_int, "vpreds_ext:", vpreds_ext,
"ent:", ent, "all_div_prob:", all_div_prob, "room:", room,
"step_count:", step_count)
#aaaa = np.asarray(vpreds_ext_list)
#print(aaaa[-100:])
'''
if step_acs[0]==0:
ram_state = ram_states[0]
path='ram_state_7700_10room'
f = open(path,'wb')
pickle.dump(ram_state,f)
f.close()
path='ram_state_7700_monitor_rews_10room'
f = open(path,'wb')
pickle.dump(monitor_rews[0],f)
f.close()
'''
'''
if restore is None:
restore = ram_states[0]
if np.random.rand() < 0.1:
print("restore")
obs = agent.I.venvs[0].restore_full_state_by_idx(restore,0)
prevrews = None
ec_rews = None
news= True
infos = {}
ram_states = ram_states[0]
#restore = ram_states[0]
'''
img = agent.I.venvs[0].render()
last_obs = obs
step_count = step_count + 1
time.sleep(0.04)
def load_test(*, env_id, num_env, hps, num_timesteps, seed, fname):
venv = VecFrameStack(
make_atari_env(env_id,
num_env,
seed,
wrapper_kwargs=dict(),
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop('max_episode_steps')),
hps.pop('frame_stack'))
# venv.score_multiple = {'Mario': 500,
# 'MontezumaRevengeNoFrameskip-v4': 100,
# 'GravitarNoFrameskip-v4': 250,
# 'PrivateEyeNoFrameskip-v4': 500,
# 'SolarisNoFrameskip-v4': None,
# 'VentureNoFrameskip-v4': 200,
# 'PitfallNoFrameskip-v4': 100,
# }[env_id]
venv.score_multiple = 1
venv.record_obs = True
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop('gamma')
policy = {'rnn': CnnGruPolicy, 'cnn': CnnPolicy}[hps.pop('policy')]
agent = PpoAgent(
scope='ppo',
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope='pol',
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop(
'update_ob_stats_independently_per_gpu'),
proportion_of_exp_used_for_predictor_update=hps.pop(
'proportion_of_exp_used_for_predictor_update'),
dynamics_bonus=hps.pop("dynamics_bonus")),
gamma=gamma,
gamma_ext=hps.pop('gamma_ext'),
lam=hps.pop('lam'),
nepochs=hps.pop('nepochs'),
nminibatches=hps.pop('nminibatches'),
lr=hps.pop('lr'),
cliprange=0.1,
nsteps=128,
ent_coef=0.001,
max_grad_norm=hps.pop('max_grad_norm'),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop('update_ob_stats_every_step'),
int_coeff=hps.pop('int_coeff'),
ext_coeff=hps.pop('ext_coeff'),
obs_save_flag=True)
tf_util.load_state("saved_states/save1")
agent.start_interaction([venv])
counter = 0
while True:
info = agent.step()
if agent.I.stats['epcount'] > 1:
with open("obs_acs.pickle", 'wb') as f1:
pickle.dump(agent.obs_rec, f1)
break
def train(*,
env_id,
num_env,
hps,
num_timesteps,
seed,
test=False,
e_greedy=0,
**kwargs):
# import pdb; pdb.set_trace()
print('_________________________________________________________')
pprint.pprint(f'hyperparams: {hps}', width=1)
pprint.pprint(f'additional hyperparams: {kwargs}', width=1)
print('_________________________________________________________')
# TODO: this is just for debugging
# tmp_env = make_atari_env(env_id, num_env, seed, wrapper_kwargs=dict(clip_rewards=kwargs['clip_rewards']))
venv = VecFrameStack(
make_atari_env(
env_id,
num_env,
seed,
wrapper_kwargs=dict(clip_rewards=kwargs['clip_rewards']),
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop('max_episode_steps'),
action_space=kwargs['action_space']), hps.pop('frame_stack'))
# venv.score_multiple = {'Mario': 500,
# 'MontezumaRevengeNoFrameskip-v4': 100,
# 'GravitarNoFrameskip-v4': 250,
# 'PrivateEyeNoFrameskip-v4': 500,
# 'SolarisNoFrameskip-v4': None,
# 'VentureNoFrameskip-v4': 200,
# 'PitfallNoFrameskip-v4': 100,
# }[env_id]
venv.score_multiple = 1 # TODO: understand what is score multiple
venv.record_obs = True if env_id == 'SolarisNoFrameskip-v4' else False
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop('gamma')
policy = {'rnn': CnnGruPolicy, 'cnn': CnnPolicy}[hps.pop('policy')]
agent = PpoAgent(
scope='ppo',
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope='pol',
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop(
'update_ob_stats_independently_per_gpu'),
proportion_of_exp_used_for_predictor_update=hps.pop(
'proportion_of_exp_used_for_predictor_update'),
dynamics_bonus=hps.pop("dynamics_bonus")),
gamma=gamma,
gamma_ext=hps.pop('gamma_ext'),
lam=hps.pop('lam'),
nepochs=hps.pop('nepochs'),
nminibatches=hps.pop('nminibatches'),
lr=hps.pop('lr'),
cliprange=0.1,
nsteps=128,
ent_coef=0.001,
max_grad_norm=hps.pop('max_grad_norm'),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop('update_ob_stats_every_step'),
int_coeff=hps.pop('int_coeff'),
ext_coeff=hps.pop('ext_coeff'),
)
saver = tf.train.Saver()
if test:
agent.restore_model(saver,
kwargs['load_dir'],
kwargs['exp_name'],
mtype=kwargs['load_mtype'])
agent.start_interaction([venv])
import time
st = time.time()
if hps.pop('update_ob_stats_from_random_agent'):
if os.path.exists('./data/ob_rms.pkl'):
with open('./data/ob_rms.pkl', 'rb') as handle:
agent.stochpol.ob_rms.mean, agent.stochpol.ob_rms.var, agent.stochpol.ob_rms.count = pickle.load(
handle)
else:
ob_rms = agent.collect_random_statistics(
num_timesteps=128 * 50) # original number128*50
with open('./data/ob_rms.pkl', 'wb') as handle:
pickle.dump([ob_rms.mean, ob_rms.var, ob_rms.count],
handle,
protocol=2)
assert len(hps) == 0, "Unused hyperparameters: %s" % list(hps.keys())
print(f'Time duration {time.time() - st}')
if not test:
counter = 1
while True:
info = agent.step()
if info['update']:
# import pdb; pdb.set_trace()
logger.logkvs(info['update'])
logger.dumpkvs()
if agent.I.stats['tcount'] // 1e7 == counter:
agent.save_model(saver,
kwargs['save_dir'],
kwargs['exp_name'],
mtype=str(counter * 1e7))
counter += 1
if agent.I.stats['tcount'] > num_timesteps:
break
if test:
for pkls in range(0, 10):
print('collecting the', pkls, 'th pickle', ' ' * 20)
all_rollout = agent.evaluate(env_id=env_id,
seed=seed,
episodes=1000,
save_image=kwargs['save_image'],
e_greedy=e_greedy)
with open('./data/newworld21_' + str(pkls).zfill(1) + '.pkl',
'wb') as handle:
pickle.dump(all_rollout, handle)
agent.stop_interaction()
if not test:
agent.save_model(saver, kwargs['save_dir'], kwargs['exp_name'])
def train(*, env_id, num_env, hps, num_timesteps, seed):
experiment = os.environ.get('EXPERIMENT_LVL')
if experiment == 'ego':
# for the ego experiment we needed a higher intrinsic coefficient
hps['int_coeff'] = 3.0
hyperparams = copy(hps)
hyperparams.update({'seed': seed})
logger.info("Hyperparameters:")
logger.info(hyperparams)
venv = VecFrameStack(
make_atari_env(env_id,
num_env,
seed,
wrapper_kwargs={},
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop('max_episode_steps')),
hps.pop('frame_stack'))
venv.score_multiple = {
'Mario': 500,
'MontezumaRevengeNoFrameskip-v4': 1,
'GravitarNoFrameskip-v4': 250,
'PrivateEyeNoFrameskip-v4': 500,
'SolarisNoFrameskip-v4': None,
'VentureNoFrameskip-v4': 200,
'PitfallNoFrameskip-v4': 100,
}[env_id]
venv.record_obs = True if env_id == 'SolarisNoFrameskip-v4' else False
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop('gamma')
policy = {'rnn': CnnGruPolicy, 'cnn': CnnPolicy}[hps.pop('policy')]
agent = PpoAgent(
scope='ppo',
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope='pol',
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop(
'update_ob_stats_independently_per_gpu'),
proportion_of_exp_used_for_predictor_update=hps.pop(
'proportion_of_exp_used_for_predictor_update'),
dynamics_bonus=hps.pop("dynamics_bonus")),
gamma=gamma,
gamma_ext=hps.pop('gamma_ext'),
lam=hps.pop('lam'),
nepochs=hps.pop('nepochs'),
nminibatches=hps.pop('nminibatches'),
lr=hps.pop('lr'),
cliprange=0.1,
nsteps=128,
ent_coef=0.001,
max_grad_norm=hps.pop('max_grad_norm'),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop('update_ob_stats_every_step'),
int_coeff=hps.pop('int_coeff'),
ext_coeff=hps.pop('ext_coeff'),
restore_model_path=hps.pop('restore_model_path'))
agent.start_interaction([venv])
if hps.pop('update_ob_stats_from_random_agent'):
agent.collect_random_statistics(num_timesteps=128 * 50)
save_model = hps.pop('save_model')
assert len(hps) == 0, "Unused hyperparameters: %s" % list(hps.keys())
#profiler = cProfile.Profile()
#profiler.enable()
#tracemalloc.start()
#prev_snap = tracemalloc.take_snapshot()
counter = 0
while True:
info = agent.step()
if info['update']:
logger.logkvs(info['update'])
logger.dumpkvs()
counter += 1
# if (counter % 10) == 0:
# snapshot = tracemalloc.take_snapshot()
# top_stats = snapshot.compare_to(prev_snap, 'lineno')
# for stat in top_stats[:10]:
# print(stat)
# prev_snap = snapshot
# profiler.dump_stats("profile_rnd")
if (counter % 100) == 0 and save_model:
agent.save_model(agent.I.step_count)
if agent.I.stats['tcount'] > num_timesteps:
break
agent.stop_interaction()