def configure(folder: str,
format_strs: Optional[Sequence[str]] = None) -> None:
"""Configure Stable Baselines logger to be `accumulate_means()`-compatible.
After this function is called, `stable_baselines.logger.{configure,reset}()`
are replaced with stubs that raise RuntimeError.
Args:
folder: Argument from `stable_baselines.logger.configure`.
format_strs: An list of output format strings. For details on available
output formats see `stable_baselines.logger.make_output_format`.
"""
# Replace `stable_baselines.logger` methods with erroring stubs to
# prevent unexpected logging state from mixed logging configuration.
sb_logger.configure = _sb_logger_configure_replacement
sb_logger.reset = _sb_logger_reset_replacement
if format_strs is None:
format_strs = ["stdout", "log", "csv"]
output_formats = _build_output_formats(folder, format_strs)
default_logger = sb_logger.Logger(folder, output_formats)
hier_logger = _HierarchicalLogger(default_logger, format_strs)
sb_logger.Logger.CURRENT = hier_logger
sb_logger.log("Logging to %s" % folder)
assert is_configured()
def do_ppo(args, start_theta, parent_this_run_dir, full_space_save_dir):
"""
Runs the test
"""
logger.log(f"#######CMA and then PPO TRAIN: {args}")
this_conti_ppo_run_dir = get_ppo_part(parent_this_run_dir)
log_dir = get_log_dir(this_conti_ppo_run_dir)
conti_ppo_save_dir = get_save_dir(this_conti_ppo_run_dir)
logger.configure(log_dir)
full_param_traj_dir_path = get_full_params_dir(this_conti_ppo_run_dir)
if os.path.exists(full_param_traj_dir_path):
import shutil
shutil.rmtree(full_param_traj_dir_path)
os.makedirs(full_param_traj_dir_path)
if os.path.exists(conti_ppo_save_dir):
import shutil
shutil.rmtree(conti_ppo_save_dir)
os.makedirs(conti_ppo_save_dir)
def make_env():
env_out = gym.make(args.env)
env_out.env.disableViewer = True
env_out.env.visualize = False
env_out = bench.Monitor(env_out, logger.get_dir(), allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
if args.normalize:
env = VecNormalize(env)
model = PPO2.load(f"{full_space_save_dir}/ppo2")
model.set_from_flat(start_theta)
if args.normalize:
env.load_running_average(full_space_save_dir)
model.set_env(env)
run_info = {"run_num": args.run_num,
"env_id": args.env,
"full_param_traj_dir_path": full_param_traj_dir_path}
# model = PPO2(policy=policy, env=env, n_steps=args.n_steps, nminibatches=args.nminibatches, lam=0.95, gamma=0.99,
# noptepochs=10,
# ent_coef=0.0, learning_rate=3e-4, cliprange=0.2, optimizer=args.optimizer)
model.tell_run_info(run_info)
episode_returns = model.learn(total_timesteps=args.ppo_num_timesteps)
model.save(f"{conti_ppo_save_dir}/ppo2")
env.save_running_average(conti_ppo_save_dir)
return episode_returns, full_param_traj_dir_path
def train(args):
total_timesteps = int(args.num_timesteps)
seed = args.seed
# get params
alg_kwargs, policy_kwargs = get_params(args)
env = build_env(args)
policy = get_policy(args)
# if args.use_typeVector:
# model = policy(CnnVectorPolicy, env, verbose=1, policy_kwargs=policy_kwargs, **alg_kwargs)
# else:
# model = policy(CnnPolicy, env, verbose=1, policy_kwargs=policy_kwargs, **alg_kwargs)
model = policy(CnnPolicy,
env,
verbose=1,
policy_kwargs=policy_kwargs,
**alg_kwargs)
model.learn(
total_timesteps=total_timesteps,
log_interval=args.log_interval,
# save_interval=args.save_interval
)
logger.log('Trained Over.')
return model, env
def main():
# requires n_comp_to_use, pc1_chunk_size
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
cma_args, cma_unknown_args = common_arg_parser.parse_known_args()
this_run_dir = get_dir_path_for_this_run(cma_args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir)
save_dir = get_save_dir( this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
result = do_pca(cma_args.n_components, cma_args.n_comp_to_use, traj_params_dir_name, intermediate_data_dir,
proj=False,
origin="mean_param", use_IPCA=cma_args.use_IPCA, chunk_size=cma_args.chunk_size, reuse=True)
logger.debug("after pca")
final_pcs = result["first_n_pcs"]
all_param_iterator = get_allinone_concat_df(dir_name=traj_params_dir_name, use_IPCA=True, chunk_size=cma_args.pc1_chunk_size)
plane_angles_vs_final_plane_along_the_way = []
ipca = IncrementalPCA(n_components=cma_args.n_comp_to_use) # for sparse PCA to speed up
for chunk in all_param_iterator:
logger.log(f"currently at {all_param_iterator._currow}")
ipca.partial_fit(chunk.values)
first_n_pcs = ipca.components_[:cma_args.n_comp_to_use]
assert final_pcs.shape[0] == first_n_pcs.shape[0]
plane_angle = cal_angle_between_nd_planes(first_n_pcs, final_pcs)
plane_angles_vs_final_plane_along_the_way.append(plane_angle)
plot_dir = get_plot_dir(cma_args)
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
plane_angles_vs_final_plane_plot_dir = get_plane_angles_vs_final_plane_along_the_way_plot_dir(plot_dir, cma_args.n_comp_to_use)
if not os.path.exists(plane_angles_vs_final_plane_plot_dir):
os.makedirs(plane_angles_vs_final_plane_plot_dir)
angles_plot_name = f"plane_angles_vs_final_plane_plot_dir "
plot_2d(plane_angles_vs_final_plane_plot_dir, angles_plot_name, np.arange(len(plane_angles_vs_final_plane_along_the_way)), plane_angles_vs_final_plane_along_the_way, "num of chunks", "angle with diff in degrees", False)
def do_cma(cma_args, first_n_pcs, orgin_param, save_dir, starting_coord, var):
tic = time.time()
#TODO better starting locations, record how many samples,
logger.log(f"CMAES STARTING :{starting_coord}")
es = cma.CMAEvolutionStrategy(starting_coord, var)
total_num_of_evals = 0
total_num_timesteps = 0
mean_rets = []
min_rets = []
max_rets = []
eval_returns = None
optimization_path = []
while total_num_timesteps < cma_args.cma_num_timesteps and not es.stop():
solutions = es.ask()
optimization_path.extend(solutions)
thetas = [
np.matmul(coord, first_n_pcs) + orgin_param for coord in solutions
]
logger.log(
f"current time steps num: {total_num_timesteps} total time steps: {cma_args.cma_num_timesteps}"
)
eval_returns = Parallel(n_jobs=cma_args.cores_to_use) \
(delayed(eval_return)(cma_args, save_dir, theta, cma_args.eval_num_timesteps, i) for
(i, theta) in enumerate(thetas))
mean_rets.append(np.mean(eval_returns))
min_rets.append(np.min(eval_returns))
max_rets.append(np.max(eval_returns))
total_num_of_evals += len(eval_returns)
total_num_timesteps += cma_args.eval_num_timesteps * len(eval_returns)
logger.log(f"current eval returns: {str(eval_returns)}")
logger.log(f"total timesteps so far: {total_num_timesteps}")
negative_eval_returns = [-r for r in eval_returns]
es.tell(solutions, negative_eval_returns)
es.logger.add() # write data to disc to be plotted
es.disp()
toc = time.time()
logger.log(
f"####################################CMA took {toc-tic} seconds")
es_logger = es.logger
if not hasattr(es_logger, 'xmean'):
es_logger.load()
n_comp_used = first_n_pcs.shape[0]
optimization_path_mean = np.vstack(
(starting_coord, es_logger.xmean[:, 5:5 + n_comp_used]))
return mean_rets, min_rets, max_rets, np.array(
optimization_path), np.array(optimization_path_mean)
def main():
# Parse command line args
parser = arg_parser()
parser.add_argument("-ns", "--num-timesteps", type=str, default="1e6")
parser.add_argument("-hw", "--use-hardware", action="store_true")
parser.add_argument("-ld", "--logdir", type=str, default="logs")
parser.add_argument("-l", "--load", type=str, default=None)
parser.add_argument("-s", "--save", action="store_true")
parser.add_argument("-si", "--save-interval", type=float, default=5e4)
parser.add_argument("-p", "--play", action="store_true")
parser.add_argument("-sd", "--seed", type=int, default=-1)
parser.add_argument(
"-o", "--output-formats", nargs="*", default=["stdout", "log", "csv"]
)
args = parser.parse_args()
# Set default seed
if args.seed == -1:
seed = np.random.randint(1, 1000)
print("Seed is", seed)
else:
seed = args.seed
device_type = "hardware" if args.use_hardware else "simulator"
logdir = "{}/{}/{}/{}/seed-{}".format(
args.logdir, device_type, "QubeSwingupEnv", args.num_timesteps, str(seed)
)
logger.configure(logdir, args.output_formats)
# Round save interval to a multiple of 2048
save_interval = int(np.ceil(args.save_interval / 2048)) if args.save else 0
# Run training script (+ loading/saving)
model, env = train(
QubeSwingupEnv,
num_timesteps=int(float(args.num_timesteps)),
hardware=args.use_hardware,
logdir=logdir,
save=args.save,
save_interval=save_interval,
load=args.load,
seed=seed,
)
if args.play:
logger.log("Running trained model")
obs = np.zeros((env.num_envs,) + env.observation_space.shape)
obs[:] = env.reset()
while True:
actions = model.step(obs)[0]
obs[:] = env.step(actions)[0]
if not args.use_hardware:
env.render()
env.close()
def main():
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
args, cma_unknown_args = common_arg_parser.parse_known_args()
this_run_dir = get_dir_path_for_this_run(args)
plot_dir_alg = get_plot_dir(args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir,
params_scope="pi")
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
if not os.path.exists(plot_dir_alg):
os.makedirs(plot_dir_alg)
final_file = get_full_param_traj_file_path(traj_params_dir_name,
"pi_final")
final_params = pd.read_csv(final_file, header=None).values[0]
def make_env():
env_out = gym.make(args.env)
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
if args.normalize:
env = VecNormalize(env)
model = PPO2.load(f"{save_dir}/ppo2") # this also loads V function
model.set_pi_from_flat(final_params)
if args.normalize:
env.load_running_average(save_dir)
obz_tensor = model.act_model.fake_input_tensor
some_neuron = model.act_model.policy_neurons[2][-1]
grads = tf.gradients(tf.math.negative(some_neuron), obz_tensor)
grads = list(zip(grads, obz_tensor))
trainer = tf.train.AdamOptimizer(learning_rate=0.01, epsilon=1e-5)
train_op = trainer.apply_gradients(grads)
for i in range(10000):
obz, _ = model.sess.run([obz_tensor, train_op])
def main():
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
cma_args, cma_unknown_args = common_arg_parser.parse_known_args()
run_nums = cma_args.run_nums_to_check
run_nums = [int(run_num) for run_num in run_nums.split(":")]
final_params_list = []
start_params_list = []
for run_num in run_nums:
cma_args.run_num = run_num
if os.path.exists(get_dir_path_for_this_run(cma_args)):
this_run_dir = get_dir_path_for_this_run(cma_args)
plot_dir_alg = get_plot_dir(cma_args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(
this_run_dir, params_scope="pi")
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
if not os.path.exists(plot_dir_alg):
os.makedirs(plot_dir_alg)
start_file = get_full_param_traj_file_path(traj_params_dir_name,
"pi_start")
start_params = pd.read_csv(start_file, header=None).values[0]
final_file = get_full_param_traj_file_path(traj_params_dir_name,
"pi_final")
final_params = pd.read_csv(final_file, header=None).values[0]
final_params_list.append(final_params)
start_params_list.append(start_params)
cma_args.run_num += 1
final_params_distances = []
for i in range(len(final_params_list)):
for j in range(i + 1, len(final_params_list)):
final_params_distances.append(
LA.norm(final_params_list[i] - final_params_list[j], ord=2))
plot_dir = get_plot_dir(cma_args)
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
np.savetxt(f"{plot_dir}/final_params_distances.txt",
final_params_distances,
delimiter=",")
def do_eval_returns(plot_args,
intermediate_data_dir,
two_pcs_eval,
origin_param,
xcoordinates_to_eval,
ycoordinates_to_eval,
save_dir,
pca_center="final_param",
reuse=True):
eval_string = f"xnum_{np.min(xcoordinates_to_eval)}:{np.max(xcoordinates_to_eval)}:{plot_args.xnum}_" \
f"ynum_{np.min(ycoordinates_to_eval)}:{np.max(ycoordinates_to_eval)}:{plot_args.ynum}"
if not reuse or not os.path.exists(
get_eval_returns_filename(intermediate_dir=intermediate_data_dir,
eval_string=eval_string,
n_comp=2,
pca_center=pca_center)):
from stable_baselines.ppo2.run_mujoco import eval_return
thetas_to_eval = [
origin_param + x * two_pcs_eval[0] + y * two_pcs_eval[1]
for y in ycoordinates_to_eval for x in xcoordinates_to_eval
]
tic = time.time()
eval_returns = Parallel(n_jobs=plot_args.cores_to_use, max_nbytes='100M')\
(delayed(eval_return)(plot_args, save_dir, theta, plot_args.eval_num_timesteps, i) for (i, theta) in enumerate(thetas_to_eval))
toc = time.time()
logger.log(
f"####################################1st version took {toc-tic} seconds"
)
np.savetxt(get_eval_returns_filename(
intermediate_dir=intermediate_data_dir,
eval_string=eval_string,
n_comp=2,
pca_center=pca_center),
eval_returns,
delimiter=',')
else:
eval_returns = np.loadtxt(get_eval_returns_filename(
intermediate_dir=intermediate_data_dir,
eval_string=eval_string,
n_comp=2,
pca_center=pca_center),
delimiter=',')
return eval_returns
def main():
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
cma_args, cma_unknown_args = common_arg_parser.parse_known_args()
this_run_dir = get_dir_path_for_this_run(cma_args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
from stable_baselines.low_dim_analysis.common import do_pca, plot_2d
origin = "mean_param"
result = do_pca(cma_args.n_components,
cma_args.n_comp_to_use,
traj_params_dir_name,
intermediate_data_dir,
proj=False,
origin=origin,
use_IPCA=cma_args.use_IPCA,
chunk_size=cma_args.chunk_size)
final_params = result["final_concat_params"]
all_pcs = result["pcs_components"]
logger.log("grab start params")
start_file = get_full_param_traj_file_path(traj_params_dir_name, "start")
start_params = pd.read_csv(start_file, header=None).values[0]
angles = []
for pc in all_pcs:
angles.append(cal_angle(pc, final_params - start_params))
plot_dir = get_plot_dir(cma_args)
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
angles_plot_name = f"angles with final - start start n_comp:{all_pcs.shape[0]} dim space of mean pca plane, "
plot_2d(plot_dir, angles_plot_name, np.arange(all_pcs.shape[0]), angles,
"num of pcs", "angle with diff", False)
def log_info(self):
"""
log the information of the dataset
"""
logger.log("Total trajectorues: %d" % self.num_traj)
logger.log("Total transitions: %d" % self.num_transition)
logger.log("Average returns: %f" % self.avg_ret)
logger.log("Std for returns: %f" % self.std_ret)
def log_info(self):
"""
Log the information of the dataset.
"""
logger.log("Total trajectories: {}".format(self.num_traj))
logger.log("Total transitions: {}".format(self.num_transition))
logger.log("Average returns: {}".format(self.avg_ret))
logger.log("Std for returns: {}".format(self.std_ret))
def plot_cma_returns(plot_dir_alg, name, mean_rets, min_rets, max_rets, show):
X = np.arange(len(mean_rets))
fig, ax = plt.subplots()
plt.xlabel('num of eval')
plt.ylabel('mean returns with min and max filled')
ax.plot(X, mean_rets)
ax.fill_between(X, min_rets, max_rets, alpha=0.5)
file_path = f"{plot_dir_alg}/{name}.pdf"
if os.path.isfile(file_path):
os.remove(file_path)
logger.log(f"saving cma plot to {file_path}")
fig.savefig(file_path, dpi=300, bbox_inches='tight', format='pdf')
if show: plt.show()
def main():
"""
Runs the test
"""
args = mujoco_arg_parser().parse_args()
logger.configure()
model, env = train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
if args.play:
logger.log("Running trained model")
obs = np.zeros((env.num_envs,) + env.observation_space.shape)
obs[:] = env.reset()
while True:
actions = model.step(obs)[0]
obs[:] = env.step(actions)[0]
env.render('human')
def plot_2d(plot_dir_alg, name, X, Y, xlabel, ylabel, show):
fig, ax = plt.subplots()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
ax.plot(X, Y)
file_path = f"{plot_dir_alg}/{name}.pdf"
if os.path.isfile(file_path):
os.remove(file_path)
if not os.path.exists(plot_dir_alg):
os.makedirs(plot_dir_alg)
logger.log(f"####saving to {file_path}")
fig.savefig(file_path, dpi=300, bbox_inches='tight', format='pdf')
if show: plt.show()
def train(args):
total_timesteps = int(args.num_timesteps)
seed = args.seed
# get params
alg_kwargs = get_params(args)
env = build_env(args)
model = PPO2(CnnPolicy, env, verbose=1, **alg_kwargs)
model.learn(total_timesteps=total_timesteps,
log_interval=args.log_interval,
save_interval=args.save_interval)
logger.log('Trained Over.')
return model, env
def plot_2d_2(plot_dir_alg, name, X, grad_vs_v, pc1_vs_V, xlabel, ylabel, show):
fig, ax = plt.subplots()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
ax.plot(X, grad_vs_v)
ax.plot(X, pc1_vs_V)
plt.legend(['in so far grad_vs_v', 'in so far pc1_vs_V'], loc='upper left')
file_path = f"{plot_dir_alg}/{name}.pdf"
if os.path.isfile(file_path):
os.remove(file_path)
logger.log(f"####saving to {file_path}")
fig.savefig(file_path, dpi=300,
bbox_inches='tight', format='pdf')
if show: plt.show()
def main():
"""
Runs the test
"""
args = mujoco_arg_parser().parse_args()
logger.configure()
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed)
env = make_mujoco_env(args.env, args.seed)
model = PPO1(MlpPolicy,
env,
timesteps_per_actorbatch=2048,
clip_param=0.2,
entcoeff=0.0,
optim_epochs=10,
optim_stepsize=3e-4,
optim_batchsize=64,
gamma=0.99,
lam=0.95,
schedule='linear')
model.learn(total_timesteps=args.num_timesteps)
model.save("ppo1")
# env.close()
del model # remove to demonstrate saving and loading
# env = make_mujoco_env(args.env, args.seed)
model = PPO1.load("ppo1")
logger.log("~!!!!!!!!")
episode_rew = 0
obs = env.reset()
while True:
action, _states = model.predict(obs)
ob, reward, done, info = env.step(action)
episode_rew += reward
env.render()
if done:
print(f'episode_rew={episode_rew}')
episode_rew = 0
obs = env.reset()
def __init__(self, env_fns, spaces=None):
"""
If you don't specify observation_space, we'll have to create a dummy
environment to get it.
"""
if spaces:
observation_space, action_space = spaces
else:
logger.log('Creating dummy env object to get spaces')
with logger.scoped_configure(format_strs=[]):
dummy = env_fns[0]()
observation_space, action_space = dummy.observation_space, dummy.action_space
dummy.close()
del dummy
VecEnv.__init__(self, len(env_fns), observation_space, action_space)
obs_spaces = observation_space.spaces if isinstance(
self.observation_space,
gym.spaces.Tuple) else (self.observation_space, )
self.obs_bufs = [
tuple(
Array(_NP_TO_CT[s.dtype.type], int(np.prod(s.shape)))
for s in obs_spaces) for _ in env_fns
]
self.obs_shapes = [s.shape for s in obs_spaces]
self.obs_dtypes = [s.dtype for s in obs_spaces]
self.parent_pipes = []
self.procs = []
for i, (env_fn, obs_buf) in enumerate(zip(env_fns, self.obs_bufs)):
wrapped_fn = CloudpickleWrapper(env_fn)
parent_pipe, child_pipe = Pipe()
proc = Process(target=_subproc_worker,
args=(child_pipe, parent_pipe, wrapped_fn, obs_buf,
self.obs_shapes, i))
proc.daemon = True
self.procs.append(proc)
self.parent_pipes.append(parent_pipe)
proc.start()
child_pipe.close()
self.waiting_step = False
def main(origin="final_param"):
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
cma_args, cma_unknown_args = common_arg_parser.parse_known_args()
cma_args = {
"alg": 'ppo2',
"env": "DartHopper-v1",
"num_timesteps": 5000,
"normalize": True,
"n_steps": 2048,
"nminibatches": 32,
"run_num": 0
}
this_run_dir = get_dir_path_for_this_run(cma_args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
from stable_baselines.low_dim_analysis.common import do_pca
result = do_pca(cma_args.n_components,
cma_args.n_comp_to_use,
traj_params_dir_name,
intermediate_data_dir,
proj=True,
origin=origin,
use_IPCA=cma_args.use_IPCA,
chunk_size=cma_args.chunk_size)
def test(model_path, env, args):
policy = get_policy(args)
model = policy.load(model_path)
test_episode = args.test_episode
num_env = args.num_env
take_nums = args.take_nums
avg_reward = 0
logger.log('Begin testing, total ' + str(test_episode * num_env) +
' episodes...')
for i_episode in range(test_episode):
obs = env.reset()
for _ in range(take_nums):
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
avg_reward += np.sum(rewards)
avg_reward /= (test_episode * num_env)
logger.log('Average reward: ' + str(avg_reward))
def main():
# requires n_comp_to_use, pc1_chunk_size
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
cma_args, cma_unknown_args = common_arg_parser.parse_known_args()
this_run_dir = get_dir_path_for_this_run(cma_args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir)
save_dir = get_save_dir( this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
logger.log("grab final params")
final_file = get_full_param_traj_file_path(traj_params_dir_name, "final")
final_params = pd.read_csv(final_file, header=None).values[0]
logger.log("grab start params")
start_file = get_full_param_traj_file_path(traj_params_dir_name, "start")
start_params = pd.read_csv(start_file, header=None).values[0]
V = final_params - start_params
pcs_components = np.loadtxt(
get_pcs_filename(intermediate_dir=intermediate_data_dir, n_comp=cma_args.num_comp_to_load), delimiter=',')
angle = cal_angle(V, pcs_components[0])
logger.log(f"@@@@@@@@@@@@ {angle}")
def test(model, env, args):
logger.log("Test...")
n_episode = args.test_episode
num_env = args.num_env
state = model.initial_state if hasattr(model, 'initial_state') else None
dones = np.zeros((1, ))
total_rewards = 0
for i_episode in range(n_episode):
obs = env.reset()
for i in range(100):
if state is not None:
actions, _, state, _ = model.step(obs, S=state, M=dones)
else:
actions, _, _, _ = model.step(obs)
obs, rew, done, info = env.step(actions)
for r in rew:
total_rewards += np.sum(r)
done = done[0]
if done:
break
avg_reward = total_rewards / (n_episode * num_env)
if args.log:
logger.log("Path: ", args.save_dir)
logger.log("Test ", n_episode, " episodes, average reward is: ",
avg_reward)
logger.log("Test over.")
else:
print("Test ", n_episode, " episodes, average reward is: ", avg_reward)
print("Test over.")
def learn(self,
total_timesteps,
callback=None,
log_interval=100,
tb_log_name="PPO1",
reset_num_timesteps=True):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
callback = self._init_callback(callback)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn()
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the PPO1 model must be " \
"an instance of common.policies.ActorCriticPolicy."
with self.sess.as_default():
self.adam.sync()
callback.on_training_start(locals(), globals())
# Prepare for rollouts
seg_gen = traj_segment_generator(self.policy_pi,
self.env,
self.timesteps_per_actorbatch,
callback=callback)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
t_start = time.time()
# rolling buffer for episode lengths
len_buffer = deque(maxlen=100)
# rolling buffer for episode rewards
reward_buffer = deque(maxlen=100)
while True:
if timesteps_so_far >= total_timesteps:
break
if self.schedule == 'constant':
cur_lrmult = 1.0
elif self.schedule == 'linear':
cur_lrmult = max(
1.0 - float(timesteps_so_far) / total_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************" %
iters_so_far)
seg = seg_gen.__next__()
# Stop training early (triggered by the callback)
if not seg.get('continue_training', True): # pytype: disable=attribute-error
break
add_vtarg_and_adv(seg, self.gamma, self.lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
observations, actions = seg["observations"], seg["actions"]
atarg, tdlamret = seg["adv"], seg["tdlamret"]
# true_rew is the reward without discount
if writer is not None:
total_episode_reward_logger(
self.episode_reward, seg["true_rewards"].reshape(
(self.n_envs, -1)), seg["dones"].reshape(
(self.n_envs, -1)), writer,
self.num_timesteps)
# predicted value function before udpate
vpredbefore = seg["vpred"]
# standardized advantage function estimate
atarg = (atarg - atarg.mean()) / atarg.std()
dataset = Dataset(dict(ob=observations,
ac=actions,
atarg=atarg,
vtarg=tdlamret),
shuffle=not self.policy.recurrent)
optim_batchsize = self.optim_batchsize or observations.shape[
0]
# set old parameter values to new parameter values
self.assign_old_eq_new(sess=self.sess)
logger.log("Optimizing...")
logger.log(fmt_row(13, self.loss_names))
# Here we do a bunch of optimization epochs over the data
for k in range(self.optim_epochs):
# list of tuples, each of which gives the loss for a minibatch
losses = []
for i, batch in enumerate(
dataset.iterate_once(optim_batchsize)):
steps = (
self.num_timesteps + k * optim_batchsize +
int(i *
(optim_batchsize / len(dataset.data_map))))
if writer is not None:
# run loss backprop with summary, but once every 10 runs save the metadata
# (memory, compute time, ...)
if self.full_tensorboard_log and (1 +
k) % 10 == 0:
run_options = tf.compat.v1.RunOptions(
trace_level=tf.compat.v1.RunOptions.
FULL_TRACE)
run_metadata = tf.compat.v1.RunMetadata()
summary, grad, *newlosses = self.lossandgrad(
batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess,
options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(
run_metadata, 'step%d' % steps)
else:
summary, grad, *newlosses = self.lossandgrad(
batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
writer.add_summary(summary, steps)
else:
_, grad, *newlosses = self.lossandgrad(
batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
self.adam.update(grad,
self.optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in dataset.iterate_once(optim_batchsize):
newlosses = self.compute_losses(batch["ob"],
batch["ob"],
batch["ac"],
batch["atarg"],
batch["vtarg"],
cur_lrmult,
sess=self.sess)
losses.append(newlosses)
mean_losses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, mean_losses))
for (loss_val, name) in zipsame(mean_losses,
self.loss_names):
logger.record_tabular("loss_" + name, loss_val)
logger.record_tabular(
"ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
# local values
lrlocal = (seg["ep_lens"], seg["ep_rets"])
# list of tuples
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal)
lens, rews = map(flatten_lists, zip(*listoflrpairs))
len_buffer.extend(lens)
reward_buffer.extend(rews)
if len(len_buffer) > 0:
logger.record_tabular("EpLenMean", np.mean(len_buffer))
logger.record_tabular("EpRewMean",
np.mean(reward_buffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
current_it_timesteps = MPI.COMM_WORLD.allreduce(
seg["total_timestep"])
timesteps_so_far += current_it_timesteps
self.num_timesteps += current_it_timesteps
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", self.num_timesteps)
logger.record_tabular("TimeElapsed", time.time() - t_start)
if self.verbose >= 1 and MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
callback.on_training_end()
return self
def learn(self, total_timesteps, callback=None, seed=None, log_interval=100, tb_log_name="DDPG", \
reset_num_timesteps=True, replay_wrapper=None):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
if replay_wrapper is not None:
self.replay_buffer = replay_wrapper(self.replay_buffer)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn(seed)
# a list for tensorboard logging, to prevent logging with the same step number, if it already occured
self.tb_seen_steps = []
rank = MPI.COMM_WORLD.Get_rank()
# we assume symmetric actions.
assert np.all(
np.abs(self.env.action_space.low) ==
self.env.action_space.high)
if self.verbose >= 2:
logger.log('Using agent with the following configuration:')
logger.log(str(self.__dict__.items()))
eval_episode_rewards_history = deque(maxlen=100)
episode_rewards_history = deque(maxlen=100)
self.episode_reward = np.zeros((1, ))
episode_successes = []
with self.sess.as_default(), self.graph.as_default():
# Prepare everything.
self._reset()
obs = self.env.reset()
eval_obs = None
if self.eval_env is not None:
eval_obs = self.eval_env.reset()
episode_reward = 0.
episode_step = 0
episodes = 0
step = 0
total_steps = 0
start_time = time.time()
epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actor_losses = []
epoch_critic_losses = []
epoch_adaptive_distances = []
eval_episode_rewards = []
eval_qs = []
epoch_actions = []
epoch_qs = []
epoch_episodes = 0
epoch = 0
while True:
for _ in range(log_interval):
# Perform rollouts.
for _ in range(self.nb_rollout_steps):
if total_steps >= total_timesteps:
return self
# Predict next action.
action, q_value = self._policy(obs,
apply_noise=True,
compute_q=True)
assert action.shape == self.env.action_space.shape
# Execute next action.
if rank == 0 and self.render:
self.env.render()
# Randomly sample actions from a uniform distribution
# with a probabilty self.random_exploration (used in HER + DDPG)
if np.random.rand() < self.random_exploration:
rescaled_action = action = self.action_space.sample(
)
else:
rescaled_action = action * np.abs(
self.action_space.low)
rescaled_action = np.where(action)[0][0]
new_obs, reward, done, info = self.env.step(
rescaled_action)
if writer is not None:
ep_rew = np.array([reward]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
self.episode_reward = total_episode_reward_logger(
self.episode_reward, ep_rew, ep_done,
writer, self.num_timesteps)
step += 1
total_steps += 1
self.num_timesteps += 1
if rank == 0 and self.render:
self.env.render()
episode_reward += reward
episode_step += 1
# Book-keeping.
epoch_actions.append(action)
epoch_qs.append(q_value)
self._store_transition(obs, action, reward,
new_obs, done)
obs = new_obs
if callback is not None:
# Only stop training if return value is False, not when it is None.
# This is for backwards compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
return self
if done:
# Episode done.
epoch_episode_rewards.append(episode_reward)
episode_rewards_history.append(episode_reward)
epoch_episode_steps.append(episode_step)
episode_reward = 0.
episode_step = 0
epoch_episodes += 1
episodes += 1
maybe_is_success = info.get('is_success')
if maybe_is_success is not None:
episode_successes.append(
float(maybe_is_success))
self._reset()
if not isinstance(self.env, VecEnv):
obs = self.env.reset()
# Train.
epoch_actor_losses = []
epoch_critic_losses = []
epoch_adaptive_distances = []
for t_train in range(self.nb_train_steps):
# Not enough samples in the replay buffer
if not self.replay_buffer.can_sample(
self.batch_size):
break
# Adapt param noise, if necessary.
if len(self.replay_buffer) >= self.batch_size and \
t_train % self.param_noise_adaption_interval == 0:
distance = self._adapt_param_noise()
epoch_adaptive_distances.append(distance)
# weird equation to deal with the fact the nb_train_steps will be different
# to nb_rollout_steps
step = (int(t_train * (self.nb_rollout_steps /
self.nb_train_steps)) +
self.num_timesteps - self.nb_rollout_steps)
critic_loss, actor_loss = self._train_step(
step, writer, log=t_train == 0)
epoch_critic_losses.append(critic_loss)
epoch_actor_losses.append(actor_loss)
self._update_target_net()
# Evaluate.
eval_episode_rewards = []
eval_qs = []
if self.eval_env is not None:
eval_episode_reward = 0.
for _ in range(self.nb_eval_steps):
if total_steps >= total_timesteps:
return self
eval_action, eval_q = self._policy(
eval_obs,
apply_noise=False,
compute_q=True)
eval_obs, eval_r, eval_done, _ = self.eval_env.step(
eval_action *
np.abs(self.action_space.low))
if self.render_eval:
self.eval_env.render()
eval_episode_reward += eval_r
eval_qs.append(eval_q)
if eval_done:
if not isinstance(self.env, VecEnv):
eval_obs = self.eval_env.reset()
eval_episode_rewards.append(
eval_episode_reward)
eval_episode_rewards_history.append(
eval_episode_reward)
eval_episode_reward = 0.
mpi_size = MPI.COMM_WORLD.Get_size()
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
stats = self._get_stats()
combined_stats = stats.copy()
combined_stats['rollout/return'] = np.mean(
epoch_episode_rewards)
combined_stats['rollout/return_history'] = np.mean(
episode_rewards_history)
combined_stats['rollout/episode_steps'] = np.mean(
epoch_episode_steps)
combined_stats['rollout/actions_mean'] = np.mean(
epoch_actions)
combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
combined_stats['train/loss_actor'] = np.mean(
epoch_actor_losses)
combined_stats['train/loss_critic'] = np.mean(
epoch_critic_losses)
if len(epoch_adaptive_distances) != 0:
combined_stats['train/param_noise_distance'] = np.mean(
epoch_adaptive_distances)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(
step) / float(duration)
combined_stats['total/episodes'] = episodes
combined_stats['rollout/episodes'] = epoch_episodes
combined_stats['rollout/actions_std'] = np.std(
epoch_actions)
# Evaluation statistics.
if self.eval_env is not None:
combined_stats['eval/return'] = np.mean(
eval_episode_rewards)
combined_stats['eval/return_history'] = np.mean(
eval_episode_rewards_history)
combined_stats['eval/Q'] = np.mean(eval_qs)
combined_stats['eval/episodes'] = len(
eval_episode_rewards)
def as_scalar(scalar):
"""
check and return the input if it is a scalar, otherwise raise ValueError
:param scalar: (Any) the object to check
:return: (Number) the scalar if x is a scalar
"""
if isinstance(scalar, np.ndarray):
assert scalar.size == 1
return scalar[0]
elif np.isscalar(scalar):
return scalar
else:
raise ValueError('expected scalar, got %s' %
scalar)
combined_stats_sums = MPI.COMM_WORLD.allreduce(
np.array(
[as_scalar(x) for x in combined_stats.values()]))
combined_stats = {
k: v / mpi_size
for (k, v) in zip(combined_stats.keys(),
combined_stats_sums)
}
# Total statistics.
combined_stats['total/epochs'] = epoch + 1
combined_stats['total/steps'] = step
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
if len(episode_successes) > 0:
logger.logkv("success rate",
np.mean(episode_successes[-100:]))
logger.dump_tabular()
logger.info('')
logdir = logger.get_dir()
if rank == 0 and logdir:
if hasattr(self.env, 'get_state'):
with open(os.path.join(logdir, 'env_state.pkl'),
'wb') as file_handler:
pickle.dump(self.env.get_state(), file_handler)
if self.eval_env and hasattr(self.eval_env,
'get_state'):
with open(
os.path.join(logdir, 'eval_env_state.pkl'),
'wb') as file_handler:
pickle.dump(self.eval_env.get_state(),
file_handler)
def main():
# requires n_comp_to_use, pc1_chunk_size
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
cma_args, cma_unknown_args = common_arg_parser.parse_known_args()
this_run_dir = get_dir_path_for_this_run(cma_args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
logger.log("grab final params")
final_file = get_full_param_traj_file_path(traj_params_dir_name, "final")
final_params = pd.read_csv(final_file, header=None).values[0]
all_param_iterator = get_allinone_concat_df(
dir_name=traj_params_dir_name,
use_IPCA=True,
chunk_size=cma_args.pc1_chunk_size)
all_grads_iterator = get_allinone_concat_df(
dir_name=traj_params_dir_name,
use_IPCA=True,
chunk_size=cma_args.pc1_chunk_size,
index="grads")
angles_with_pc1_along_the_way = []
grad_vs_final_min_current_param = []
ipca = IncrementalPCA(1) # for sparse PCA to speed up
for chunk in all_param_iterator:
logger.log(f"currently at {all_param_iterator._currow}")
target_direction = final_params - chunk.values[-1]
ipca.partial_fit(chunk.values)
angle_with_pc1 = cal_angle(target_direction, ipca.components_[0])
angles_with_pc1_along_the_way.append(angle_with_pc1)
grads = all_grads_iterator.__next__().values
for i, grad in enumerate(grads):
grad_angle = cal_angle(grad, final_params - chunk.values[i])
grad_vs_final_min_current_param.append(grad_angle)
plot_dir = get_plot_dir(cma_args)
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
angles_plot_name = f"final - current VS so far pc1" \
f"cma_args.pc1_chunk_size: {cma_args.pc1_chunk_size}"
plot_2d(plot_dir, angles_plot_name,
np.arange(len(angles_with_pc1_along_the_way)),
angles_with_pc1_along_the_way, "num of chunks",
"angle with diff in degrees", False)
grad_vs_current_plot_name = f"##final - current param VS current grad" \
f"cma_args.pc1_chunk_size: {cma_args.pc1_chunk_size}"
plot_2d(plot_dir, grad_vs_current_plot_name,
np.arange(len(grad_vs_final_min_current_param)),
grad_vs_final_min_current_param, "num of chunks",
"angle with diff in degrees", False)
def learn(self,
total_timesteps,
callback=None,
seed=None,
log_interval=100,
tb_log_name="TRPO",
reset_num_timesteps=True):
new_tb_log = self._init_num_timesteps(reset_num_timesteps)
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name, new_tb_log) \
as writer:
self._setup_learn(seed)
with self.sess.as_default():
seg_gen = traj_segment_generator(
self.policy_pi,
self.env,
self.timesteps_per_batch,
reward_giver=self.reward_giver,
gail=self.using_gail)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
t_start = time.time()
len_buffer = deque(
maxlen=40) # rolling buffer for episode lengths
reward_buffer = deque(
maxlen=40) # rolling buffer for episode rewards
self.episode_reward = np.zeros((self.n_envs, ))
true_reward_buffer = None
if self.using_gail:
true_reward_buffer = deque(maxlen=40)
# Initialize dataloader
batchsize = self.timesteps_per_batch // self.d_step
self.expert_dataset.init_dataloader(batchsize)
# Stats not used for now
# TODO: replace with normal tb logging
# g_loss_stats = Stats(loss_names)
# d_loss_stats = Stats(reward_giver.loss_name)
# ep_stats = Stats(["True_rewards", "Rewards", "Episode_length"])
while True:
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
if total_timesteps and timesteps_so_far >= total_timesteps:
break
logger.log("********** Iteration %i ************" %
iters_so_far)
def fisher_vector_product(vec):
return self.allmean(
self.compute_fvp(
vec, *fvpargs,
sess=self.sess)) + self.cg_damping * vec
# ------------------ Update G ------------------
logger.log("Optimizing Policy...")
# g_step = 1 when not using GAIL
mean_losses = None
vpredbefore = None
tdlamret = None
observation = None
action = None
seg = None
for k in range(self.g_step):
with self.timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, self.gamma, self.lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
observation, action, atarg, tdlamret = seg["ob"], seg[
"ac"], seg["adv"], seg["tdlamret"]
vpredbefore = seg[
"vpred"] # predicted value function before update
atarg = (atarg - atarg.mean()) / atarg.std(
) # standardized advantage function estimate
# true_rew is the reward without discount
if writer is not None:
self.episode_reward = total_episode_reward_logger(
self.episode_reward, seg["true_rew"].reshape(
(self.n_envs, -1)), seg["dones"].reshape(
(self.n_envs, -1)), writer,
self.num_timesteps)
args = seg["ob"], seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
self.assign_old_eq_new(sess=self.sess)
with self.timed("computegrad"):
steps = self.num_timesteps + (k + 1) * (
seg["total_timestep"] / self.g_step)
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata(
) if self.full_tensorboard_log else None
# run loss backprop with summary, and save the metadata (memory, compute time, ...)
if writer is not None:
summary, grad, *lossbefore = self.compute_lossandgrad(
*args,
tdlamret,
sess=self.sess,
options=run_options,
run_metadata=run_metadata)
if self.full_tensorboard_log:
writer.add_run_metadata(
run_metadata, 'step%d' % steps)
writer.add_summary(summary, steps)
else:
_, grad, *lossbefore = self.compute_lossandgrad(
*args,
tdlamret,
sess=self.sess,
options=run_options,
run_metadata=run_metadata)
lossbefore = self.allmean(np.array(lossbefore))
grad = self.allmean(grad)
if np.allclose(grad, 0):
logger.log("Got zero gradient. not updating")
else:
with self.timed("conjugate_gradient"):
stepdir = conjugate_gradient(
fisher_vector_product,
grad,
cg_iters=self.cg_iters,
verbose=self.rank == 0
and self.verbose >= 1)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(
fisher_vector_product(stepdir))
# abs(shs) to avoid taking square root of negative values
lagrange_multiplier = np.sqrt(
abs(shs) / self.max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lagrange_multiplier
expectedimprove = grad.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = self.get_flat()
thnew = None
for _ in range(10):
thnew = thbefore + fullstep * stepsize
self.set_from_flat(thnew)
mean_losses = surr, kl_loss, *_ = self.allmean(
np.array(
self.compute_losses(*args,
sess=self.sess)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(mean_losses).all():
logger.log(
"Got non-finite value of losses -- bad!"
)
elif kl_loss > self.max_kl * 1.5:
logger.log(
"violated KL constraint. shrinking step."
)
elif improve < 0:
logger.log(
"surrogate didn't improve. shrinking step."
)
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
self.set_from_flat(thbefore)
if self.nworkers > 1 and iters_so_far % 20 == 0:
# list of tuples
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(), self.vfadam.getflat().sum()))
assert all(
np.allclose(ps, paramsums[0])
for ps in paramsums[1:])
with self.timed("vf"):
for _ in range(self.vf_iters):
# NOTE: for recurrent policies, use shuffle=False?
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=128,
shuffle=True):
grad = self.allmean(
self.compute_vflossandgrad(
mbob, mbob, mbret, sess=self.sess))
self.vfadam.update(grad, self.vf_stepsize)
for (loss_name, loss_val) in zip(self.loss_names,
mean_losses):
logger.record_tabular(loss_name, loss_val)
logger.record_tabular(
"explained_variance_tdlam_before",
explained_variance(vpredbefore, tdlamret))
if self.using_gail:
# ------------------ Update D ------------------
logger.log("Optimizing Discriminator...")
logger.log(fmt_row(13, self.reward_giver.loss_name))
assert len(observation) == self.timesteps_per_batch
batch_size = self.timesteps_per_batch // self.d_step
# NOTE: uses only the last g step for observation
d_losses = [
] # list of tuples, each of which gives the loss for a minibatch
# NOTE: for recurrent policies, use shuffle=False?
for ob_batch, ac_batch in dataset.iterbatches(
(observation, action),
include_final_partial_batch=False,
batch_size=batch_size,
shuffle=True):
ob_expert, ac_expert = self.expert_dataset.get_next_batch(
)
# update running mean/std for reward_giver
if self.reward_giver.normalize:
self.reward_giver.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
# Reshape actions if needed when using discrete actions
if isinstance(self.action_space,
gym.spaces.Discrete):
if len(ac_batch.shape) == 2:
ac_batch = ac_batch[:, 0]
if len(ac_expert.shape) == 2:
ac_expert = ac_expert[:, 0]
*newlosses, grad = self.reward_giver.lossandgrad(
ob_batch, ac_batch, ob_expert, ac_expert)
self.d_adam.update(self.allmean(grad),
self.d_stepsize)
d_losses.append(newlosses)
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
# lr: lengths and rewards
lr_local = (seg["ep_lens"], seg["ep_rets"],
seg["ep_true_rets"]) # local values
list_lr_pairs = MPI.COMM_WORLD.allgather(
lr_local) # list of tuples
lens, rews, true_rets = map(flatten_lists,
zip(*list_lr_pairs))
true_reward_buffer.extend(true_rets)
else:
# lr: lengths and rewards
lr_local = (seg["ep_lens"], seg["ep_rets"]
) # local values
list_lr_pairs = MPI.COMM_WORLD.allgather(
lr_local) # list of tuples
lens, rews = map(flatten_lists, zip(*list_lr_pairs))
len_buffer.extend(lens)
reward_buffer.extend(rews)
if len(len_buffer) > 0:
logger.record_tabular("EpLenMean", np.mean(len_buffer))
logger.record_tabular("EpRewMean",
np.mean(reward_buffer))
if self.using_gail:
logger.record_tabular("EpTrueRewMean",
np.mean(true_reward_buffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
current_it_timesteps = MPI.COMM_WORLD.allreduce(
seg["total_timestep"])
timesteps_so_far += current_it_timesteps
self.num_timesteps += current_it_timesteps
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", self.num_timesteps)
logger.record_tabular("TimeElapsed", time.time() - t_start)
if self.verbose >= 1 and self.rank == 0:
logger.dump_tabular()
return self
def main():
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
cma_args, cma_unknown_args = common_arg_parser.parse_known_args()
origin_name = "final_param"
this_run_dir = get_dir_path_for_this_run(cma_args)
plot_dir_alg = get_plot_dir(cma_args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir,
params_scope="pi")
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
if not os.path.exists(plot_dir_alg):
os.makedirs(plot_dir_alg)
start_file = get_full_param_traj_file_path(traj_params_dir_name,
"pi_start")
start_params = pd.read_csv(start_file, header=None).values[0]
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
pca_indexes = cma_args.other_pca_index
pca_indexes = [int(pca_index) for pca_index in pca_indexes.split(":")]
n_comp_to_project_on = pca_indexes
result = do_pca(n_components=cma_args.n_components,
traj_params_dir_name=traj_params_dir_name,
intermediate_data_dir=intermediate_data_dir,
use_IPCA=cma_args.use_IPCA,
chunk_size=cma_args.chunk_size,
reuse=True)
logger.debug("after pca")
if origin_name == "final_param":
origin_param = result["final_params"]
elif origin_name == "start_param":
origin_param = start_params
else:
origin_param = result["mean_param"]
proj_coords = project(result["pcs_components"],
pcs_slice=n_comp_to_project_on,
origin_name=origin_name,
origin_param=origin_param,
IPCA_chunk_size=cma_args.chunk_size,
traj_params_dir_name=traj_params_dir_name,
intermediate_data_dir=intermediate_data_dir,
n_components=cma_args.n_components,
reuse=True)
'''
==========================================================================================
eval all xy coords
==========================================================================================
'''
other_pcs_plot_dir = get_other_pcs_plane_plot_dir(plot_dir_alg,
pca_indexes)
if not os.path.exists(other_pcs_plot_dir):
os.makedirs(other_pcs_plot_dir)
plot_3d_trajectory_path_only(
other_pcs_plot_dir,
f"{pca_indexes}_final_origin_3d_path_plot",
proj_coords,
explained_ratio=result["explained_variance_ratio"][pca_indexes])
def main():
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
cma_args, cma_unknown_args = common_arg_parser.parse_known_args()
# origin = "final_param"
origin = cma_args.origin
this_run_dir = get_dir_path_for_this_run(cma_args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
cma_run_num, cma_intermediate_data_dir = generate_run_dir(
get_cma_returns_dirname,
intermediate_dir=intermediate_data_dir,
n_comp=cma_args.n_comp_to_use)
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
proj_or_not = (cma_args.n_comp_to_use == 2)
result = do_pca(cma_args.n_components,
cma_args.n_comp_to_use,
traj_params_dir_name,
intermediate_data_dir,
proj=proj_or_not,
origin=origin,
use_IPCA=cma_args.use_IPCA,
chunk_size=cma_args.chunk_size,
reuse=False)
'''
==========================================================================================
eval all xy coords
==========================================================================================
'''
from stable_baselines.low_dim_analysis.common import plot_contour_trajectory, gen_subspace_coords,do_eval_returns\
, do_proj_on_first_n
if origin == "final_param":
origin_param = result["final_concat_params"]
else:
origin_param = result["mean_param"]
final_param = result["final_concat_params"]
last_proj_coord = do_proj_on_first_n(final_param, result["first_n_pcs"],
origin_param)
starting_coord = last_proj_coord
logger.log(f"CMA STASRTING CORRD: {starting_coord}")
# starting_coord = (1/2*np.max(xcoordinates_to_eval), 1/2*np.max(ycoordinates_to_eval)) # use mean
assert result["first_n_pcs"].shape[0] == cma_args.n_comp_to_use
mean_rets, min_rets, max_rets, opt_path, opt_path_mean = do_cma(
cma_args, result["first_n_pcs"], origin_param, save_dir,
starting_coord, cma_args.cma_var)
dump_rows_write_csv(cma_intermediate_data_dir, opt_path_mean,
"opt_mean_path")
plot_dir = get_plot_dir(cma_args)
cma_plot_dir = get_cma_plot_dir(plot_dir, cma_args.n_comp_to_use,
cma_run_num, origin)
if not os.path.exists(cma_plot_dir):
os.makedirs(cma_plot_dir)
ret_plot_name = f"cma return on {cma_args.n_comp_to_use} dim space of real pca plane, " \
f"explained {np.sum(result['explained_variance_ratio'][:cma_args.n_comp_to_use])}"
plot_cma_returns(cma_plot_dir,
ret_plot_name,
mean_rets,
min_rets,
max_rets,
show=False)
if cma_args.n_comp_to_use == 2:
proj_coords = result["proj_coords"]
assert proj_coords.shape[1] == 2
xcoordinates_to_eval, ycoordinates_to_eval = gen_subspace_coords(
cma_args,
np.vstack((proj_coords, opt_path_mean)).T)
eval_returns = do_eval_returns(cma_args,
intermediate_data_dir,
result["first_n_pcs"],
origin_param,
xcoordinates_to_eval,
ycoordinates_to_eval,
save_dir,
pca_center=origin,
reuse=False)
plot_contour_trajectory(cma_plot_dir,
f"{origin}_origin_eval_return_contour_plot",
xcoordinates_to_eval,
ycoordinates_to_eval,
eval_returns,
proj_coords[:, 0],
proj_coords[:, 1],
result["explained_variance_ratio"][:2],
num_levels=25,
show=False,
sub_alg_path=opt_path_mean)
opt_mean_path_in_old_basis = [
mean_projected_param.dot(result["first_n_pcs"]) + result["mean_param"]
for mean_projected_param in opt_path_mean
]
distance_to_final = [
LA.norm(opt_mean - final_param, ord=2)
for opt_mean in opt_mean_path_in_old_basis
]
distance_to_final_plot_name = f"distance_to_final over generations "
plot_2d(cma_plot_dir, distance_to_final_plot_name,
np.arange(len(distance_to_final)), distance_to_final,
"num generation", "distance_to_final", False)
def main():
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
cma_args, cma_unknown_args = common_arg_parser.parse_known_args()
origin = "mean_param"
this_run_dir = get_dir_path_for_this_run(cma_args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
cma_run_num, cma_intermediate_data_dir = generate_run_dir(
get_cma_returns_dirname,
intermediate_dir=intermediate_data_dir,
n_comp=cma_args.n_comp_to_use)
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
logger.log("grab final params")
final_file = get_full_param_traj_file_path(traj_params_dir_name, "final")
final_param = pd.read_csv(final_file, header=None).values[0]
final_pca = IncrementalPCA(n_components=2) # for sparse PCA to speed up
theta_file = get_full_param_traj_file_path(traj_params_dir_name, 0)
concat_df = pd.read_csv(theta_file, header=None, chunksize=10000)
tic = time.time()
for chunk in concat_df:
logger.log(f"currnet at : {concat_df._currow}")
if chunk.shape[0] < 2:
logger.log(f"last column too few: {chunk.shape[0]}")
continue
final_pca.partial_fit(chunk.values)
toc = time.time()
logger.log(
'\nElapsed time computing the chunked PCA {:.2f} s\n'.format(toc -
tic))
logger.log(final_pca.explained_variance_ratio_)
pcs_components = final_pca.components_
first_2_pcs = pcs_components[:2]
mean_param = final_pca.mean_
origin_param = mean_param
theta_file = get_full_param_traj_file_path(traj_params_dir_name, 0)
concat_df = pd.read_csv(theta_file, header=None, chunksize=10000)
proj_coords = do_proj_on_first_n_IPCA(concat_df, first_2_pcs, origin_param)
'''
==========================================================================================
eval all xy coords
==========================================================================================
'''
from stable_baselines.low_dim_analysis.common import plot_contour_trajectory, gen_subspace_coords,do_eval_returns, \
get_allinone_concat_df, do_proj_on_first_n
from stable_baselines.ppo2.run_mujoco import eval_return
last_proj_coord = do_proj_on_first_n(final_param, first_2_pcs,
origin_param)
starting_coord = last_proj_coord
tic = time.time()
#TODO better starting locations, record how many samples,
logger.log(f"CMAES STARTING :{starting_coord}")
es = cma.CMAEvolutionStrategy(starting_coord, 5)
total_num_of_evals = 0
total_num_timesteps = 0
mean_rets = []
min_rets = []
max_rets = []
eval_returns = None
optimization_path = []
while total_num_timesteps < cma_args.cma_num_timesteps and not es.stop():
solutions = es.ask()
optimization_path.extend(solutions)
thetas = [
np.matmul(coord, first_2_pcs) + origin_param for coord in solutions
]
logger.log(
f"current time steps num: {total_num_timesteps} total time steps: {cma_args.cma_num_timesteps}"
)
eval_returns = Parallel(n_jobs=cma_args.cores_to_use) \
(delayed(eval_return)(cma_args, save_dir, theta, cma_args.eval_num_timesteps, i) for
(i, theta) in enumerate(thetas))
mean_rets.append(np.mean(eval_returns))
min_rets.append(np.min(eval_returns))
max_rets.append(np.max(eval_returns))
total_num_of_evals += len(eval_returns)
total_num_timesteps += cma_args.eval_num_timesteps * len(eval_returns)
logger.log(f"current eval returns: {str(eval_returns)}")
logger.log(f"total timesteps so far: {total_num_timesteps}")
negative_eval_returns = [-r for r in eval_returns]
es.tell(solutions, negative_eval_returns)
es.logger.add() # write data to disc to be plotted
es.disp()
toc = time.time()
logger.log(
f"####################################CMA took {toc-tic} seconds")
es_logger = es.logger
if not hasattr(es_logger, 'xmean'):
es_logger.load()
n_comp_used = first_2_pcs.shape[0]
optimization_path_mean = np.vstack(
(starting_coord, es_logger.xmean[:, 5:5 + n_comp_used]))
dump_rows_write_csv(cma_intermediate_data_dir, optimization_path_mean,
"opt_mean_path")
plot_dir = get_plot_dir(cma_args)
cma_plot_dir = get_cma_plot_dir(plot_dir,
cma_args.n_comp_to_use,
cma_run_num,
origin=origin)
if not os.path.exists(cma_plot_dir):
os.makedirs(cma_plot_dir)
ret_plot_name = f"cma return on {cma_args.n_comp_to_use} dim space of real pca plane, " \
f"explained {np.sum(final_pca.explained_variance_ratio_[:2])}"
plot_cma_returns(cma_plot_dir,
ret_plot_name,
mean_rets,
min_rets,
max_rets,
show=False)
assert proj_coords.shape[1] == 2
xcoordinates_to_eval, ycoordinates_to_eval = gen_subspace_coords(
cma_args,
np.vstack((proj_coords, optimization_path_mean)).T)
from stable_baselines.ppo2.run_mujoco import eval_return
thetas_to_eval = [
origin_param + x * first_2_pcs[0] + y * first_2_pcs[1]
for y in ycoordinates_to_eval for x in xcoordinates_to_eval
]
tic = time.time()
eval_returns = Parallel(n_jobs=-1, max_nbytes='100M') \
(delayed(eval_return)(cma_args, save_dir, theta, cma_args.eval_num_timesteps, i) for (i, theta) in
enumerate(thetas_to_eval))
toc = time.time()
logger.log(
f"####################################1st version took {toc-tic} seconds"
)
plot_contour_trajectory(
cma_plot_dir,
f"cma redo___{origin}_origin_eval_return_contour_plot",
xcoordinates_to_eval,
ycoordinates_to_eval,
eval_returns,
proj_coords[:, 0],
proj_coords[:, 1],
final_pca.explained_variance_ratio_,
num_levels=25,
show=False,
sub_alg_path=optimization_path_mean.T)
opt_mean_path_in_old_basis = [
mean_projected_param.dot(first_2_pcs) + mean_param
for mean_projected_param in optimization_path_mean
]
distance_to_final = [
LA.norm(opt_mean - final_param, ord=2)
for opt_mean in opt_mean_path_in_old_basis
]
distance_to_final_plot_name = f"cma redo distance_to_final over generations "
plot_2d(cma_plot_dir, distance_to_final_plot_name,
np.arange(len(distance_to_final)), distance_to_final,
"num generation", "distance_to_final", False)