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 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 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 train(args):
"""
Runs the test
"""
args, argv = mujoco_arg_parser().parse_known_args(args)
logger.log(f"#######TRAIN: {args}")
args.alg = "ppo2"
this_run_dir = get_dir_path_for_this_run(args)
if os.path.exists(this_run_dir):
import shutil
shutil.rmtree(this_run_dir)
os.makedirs(this_run_dir)
log_dir = get_log_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
logger.configure(log_dir)
def make_env():
env_out = gym.make(args.env)
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])
env.envs[0].env.env.disableViewer = True
set_global_seeds(args.seed)
env.envs[0].env.env.seed(args.seed)
if args.normalize:
env = VecNormalize(env)
policy = MlpPolicy
# extra run info I added for my purposes
full_param_traj_dir_path = get_full_params_dir(this_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(save_dir):
import shutil
shutil.rmtree(save_dir)
os.makedirs(save_dir)
run_info = {
"run_num": args.run_num,
"env_id": args.env,
"full_param_traj_dir_path": full_param_traj_dir_path,
"state_samples_to_collect": args.state_samples_to_collect
}
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,
seed=args.seed)
model.tell_run_info(run_info)
model.learn(total_timesteps=args.num_timesteps)
model.save(f"{save_dir}/ppo2")
if args.normalize:
env.save_running_average(save_dir)
def visualize_augment_experiment(augment_num_timesteps,
top_num_to_include_slice,
augment_seed,
augment_run_num,
network_size,
policy_env,
policy_num_timesteps,
policy_run_num,
policy_seed,
eval_seed,
eval_run_num,
learning_rate,
additional_note,
result_dir,
lagrangian_inds_to_include=None):
args = AttributeDict()
args.normalize = True
args.num_timesteps = augment_num_timesteps
args.run_num = augment_run_num
args.alg = "ppo2"
args.seed = augment_seed
logger.log(f"#######TRAIN: {args}")
# non_linear_global_dict
timestamp = get_time_stamp('%Y_%m_%d_%H_%M_%S')
experiment_label = f"learning_rate_{learning_rate}timestamp_{timestamp}_augment_num_timesteps{augment_num_timesteps}" \
f"_top_num_to_include{top_num_to_include_slice.start}_{top_num_to_include_slice.stop}" \
f"_augment_seed{augment_seed}_augment_run_num{augment_run_num}_network_size{network_size}" \
f"_policy_num_timesteps{policy_num_timesteps}_policy_run_num{policy_run_num}_policy_seed{policy_seed}" \
f"_eval_seed{eval_seed}_eval_run_num{eval_run_num}_additional_note_{additional_note}"
if policy_env == "DartWalker2d-v1":
entry_point = 'gym.envs.dart:DartWalker2dEnv_aug_input'
elif policy_env == "DartHopper-v1":
entry_point = 'gym.envs.dart:DartHopperEnv_aug_input'
elif policy_env == "DartHalfCheetah-v1":
entry_point = 'gym.envs.dart:DartHalfCheetahEnv_aug_input'
elif policy_env == "DartSnake7Link-v1":
entry_point = 'gym.envs.dart:DartSnake7LinkEnv_aug_input'
else:
raise NotImplemented()
this_run_dir = get_experiment_path_for_this_run(
entry_point,
args.num_timesteps,
args.run_num,
args.seed,
learning_rate=learning_rate,
top_num_to_include=top_num_to_include_slice,
result_dir=result_dir,
network_size=network_size)
full_param_traj_dir_path = get_full_params_dir(this_run_dir)
log_dir = get_log_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
create_dir_remove(this_run_dir)
create_dir_remove(full_param_traj_dir_path)
create_dir_remove(save_dir)
create_dir_remove(log_dir)
logger.configure(log_dir)
# note this is only linear
if lagrangian_inds_to_include is None:
linear_top_vars_list = read_linear_top_var(policy_env,
policy_num_timesteps,
policy_run_num, policy_seed,
eval_seed, eval_run_num,
additional_note)
# keys_to_include = ["COM", "M", "Coriolis", "total_contact_forces_contact_bodynode",
# "com_jacobian", "contact_bodynode_jacobian"]
keys_to_include = ["COM", "M", "Coriolis", "com_jacobian"]
# lagrangian_inds_to_include = linear_top_vars_list[top_num_to_include_slice]
lagrangian_inds_to_include = get_wanted_lagrangians(
keys_to_include, linear_top_vars_list, top_num_to_include_slice)
with open(f"{log_dir}/lagrangian_inds_to_include.json", 'w') as fp:
json.dump(lagrangian_inds_to_include, fp)
args.env = f'{experiment_label}_{entry_point}-v1'
register(id=args.env,
entry_point=entry_point,
max_episode_steps=1000,
kwargs={"lagrangian_inds_to_include": lagrangian_inds_to_include})
def make_env():
env_out = gym.make(args.env)
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])
walker_env = env.envs[0].env.env
walker_env.disableViewer = True
if args.normalize:
env = VecNormalize(env)
policy = MlpPolicy
# extra run info I added for my purposes
run_info = {
"run_num": args.run_num,
"env_id": args.env,
"full_param_traj_dir_path": full_param_traj_dir_path
}
layers = [network_size, network_size]
set_global_seeds(args.seed)
walker_env.seed(args.seed)
policy_kwargs = {"net_arch": [dict(vf=layers, pi=layers)]}
model = PPO2(policy=policy,
env=env,
n_steps=4096,
nminibatches=64,
lam=0.95,
gamma=0.99,
noptepochs=10,
ent_coef=0.0,
learning_rate=learning_rate,
cliprange=0.2,
optimizer='adam',
policy_kwargs=policy_kwargs,
seed=args.seed)
model.tell_run_info(run_info)
model.learn(total_timesteps=args.num_timesteps, seed=args.seed)
model.save(f"{save_dir}/ppo2")
if args.normalize:
env.save_running_average(save_dir)
return log_dir
def visualize_policy_and_collect_COM(
augment_num_timesteps, top_num_to_include_slice, augment_seed,
augment_run_num, network_size, policy_env, policy_num_timesteps,
policy_run_num, policy_seed, eval_seed, eval_run_num, learning_rate,
additional_note, metric_param):
result_dir = get_result_dir(policy_env, policy_num_timesteps,
policy_run_num, policy_seed, eval_seed,
eval_run_num, additional_note, metric_param)
args = AttributeDict()
args.normalize = True
args.num_timesteps = augment_num_timesteps
args.run_num = augment_run_num
args.alg = "ppo2"
args.seed = augment_seed
logger.log(f"#######VISUALIZE: {args}")
# non_linear_global_dict
linear_global_dict, non_linear_global_dict, lagrangian_values, input_values, layers_values, all_weights = read_all_data(
policy_env,
policy_num_timesteps,
policy_run_num,
policy_seed,
eval_seed,
eval_run_num,
additional_note=additional_note)
timestamp = get_time_stamp('%Y_%m_%d_%H_%M_%S')
experiment_label = f"learning_rate_{learning_rate}timestamp_{timestamp}_augment_num_timesteps{augment_num_timesteps}" \
f"_top_num_to_include{top_num_to_include_slice.start}_{top_num_to_include_slice.stop}" \
f"_augment_seed{augment_seed}_augment_run_num{augment_run_num}_network_size{network_size}" \
f"_policy_num_timesteps{policy_num_timesteps}_policy_run_num{policy_run_num}_policy_seed{policy_seed}" \
f"_eval_seed{eval_seed}_eval_run_num{eval_run_num}_additional_note_{additional_note}"
entry_point = 'gym.envs.dart:DartWalker2dEnv_aug_input'
this_run_dir = get_experiment_path_for_this_run(
entry_point,
args.num_timesteps,
args.run_num,
args.seed,
learning_rate=learning_rate,
top_num_to_include=top_num_to_include_slice,
result_dir=result_dir,
network_size=network_size,
metric_param=metric_param)
traj_params_dir_name = get_full_params_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
aug_plot_dir = get_aug_plot_dir(this_run_dir) + "_vis"
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]
args.env = f'{experiment_label}_{entry_point}-v1'
register(id=args.env,
entry_point=entry_point,
max_episode_steps=1000,
kwargs={
'linear_global_dict': linear_global_dict,
'non_linear_global_dict': non_linear_global_dict,
'top_to_include_slice': top_num_to_include_slice,
'aug_plot_dir': aug_plot_dir,
"lagrangian_values": lagrangian_values,
"layers_values": layers_values
})
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])
walker_env = env.envs[0].env.env
walker_env.disableViewer = False
if args.normalize:
env = VecNormalize(env)
set_global_seeds(args.seed)
walker_env.seed(args.seed)
model = PPO2.load(f"{save_dir}/ppo2", seed=augment_seed)
model.set_pi_from_flat(final_params)
if args.normalize:
env.load_running_average(save_dir)
sk = env.venv.envs[0].env.env.robot_skeleton
lagrangian_values = {}
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
env = VecVideoRecorder(env,
aug_plot_dir,
record_video_trigger=lambda x: x == 0,
video_length=3000,
name_prefix="vis_this_policy")
lagrangian_values["M"] = [sk.M.reshape((-1, 1))]
lagrangian_values["COM"] = [sk.C.reshape((-1, 1))]
lagrangian_values["Coriolis"] = [sk.c.reshape((-1, 1))]
lagrangian_values["q"] = [sk.q.reshape((-1, 1))]
lagrangian_values["dq"] = [sk.dq.reshape((-1, 1))]
contact_values = {}
neuron_values = model.give_neuron_values(obs)
raw_layer_values_list = [[neuron_value.reshape((-1, 1))]
for neuron_value in neuron_values]
env.render()
ep_infos = []
steps_to_first_done = 0
first_done = False
# epi_rew = 0
for _ in range(3000):
actions = model.step(obs)[0]
# yield neuron_values
obs, rew, done, infos = env.step(actions)
# epi_rew+= rew[0]
if done and not first_done:
first_done = True
if not first_done:
steps_to_first_done += 1
neuron_values = model.give_neuron_values(obs)
for i, layer in enumerate(neuron_values):
raw_layer_values_list[i].append(layer.reshape((-1, 1)))
# fill_contacts_jac_dict(infos[0]["contacts"], contact_dict=contact_values, neuron_values=neuron_values)
lagrangian_values["M"].append(sk.M.reshape((-1, 1)))
lagrangian_values["q"].append(sk.q.reshape((-1, 1)))
lagrangian_values["dq"].append(sk.dq.reshape((-1, 1)))
lagrangian_values["COM"].append(sk.C.reshape((-1, 1)))
lagrangian_values["Coriolis"].append(sk.c.reshape((-1, 1)))
# env.render()
# time.sleep(1)
for info in infos:
maybe_ep_info = info.get('episode')
if maybe_ep_info is not None:
ep_infos.append(maybe_ep_info)
env.render()
done = done.any()
if done:
episode_rew = safe_mean([ep_info['r'] for ep_info in ep_infos])
print(f'episode_rew={episode_rew}')
# print(f'episode_rew={epi_rew}')
# epi_rew = 0
obs = env.reset()
#Hstack into a big matrix
lagrangian_values["M"] = np.hstack(lagrangian_values["M"])
lagrangian_values["COM"] = np.hstack(lagrangian_values["COM"])
lagrangian_values["Coriolis"] = np.hstack(lagrangian_values["Coriolis"])
lagrangian_values["q"] = np.hstack(lagrangian_values["q"])
lagrangian_values["dq"] = np.hstack(lagrangian_values["dq"])
# for contact_body_name, l in contact_values.items():
# body_contact_dict = contact_values[contact_body_name]
# for name, l in body_contact_dict.items():
# body_contact_dict[name] = np.hstack(body_contact_dict[name])
input_values = np.hstack(raw_layer_values_list[0])
layers_values = [
np.hstack(layer_list) for layer_list in raw_layer_values_list
][1:-2] # drop variance and inputs
for i, com in enumerate(lagrangian_values["COM"]):
plt.figure()
plt.plot(np.arange(len(com)), com)
plt.xlabel("time")
plt.ylabel(f"COM{i}")
plt.savefig(f"{aug_plot_dir}/COM{i}.jpg")
plt.close()
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_and_then_ppo_run_dir,
intermediate_dir=intermediate_data_dir,
n_comp=cma_args.n_comp_to_use,
cma_steps=cma_args.cma_num_timesteps
)
# cma_intermediate_data_dir = get_cma_and_then_ppo_run_dir(intermediate_dir = intermediate_data_dir,
# n_comp = cma_args.n_comp_to_use,
# cma_steps = cma_args.cma_num_timesteps, run_num=0)
best_theta_file_name = "best theta from cma"
# if not os.path.exists(f"{cma_intermediate_data_dir}/{best_theta_file_name}.csv") or \
# not os.path.exists(f"{cma_intermediate_data_dir}/opt_mean_path.csv"):
'''
==========================================================================================
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=True)
logger.debug("after pca")
'''
==========================================================================================
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"]
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]
starting_coord = do_proj_on_first_n(start_params, result["first_n_pcs"], origin_param)
# starting_coord = np.random.rand(1, cma_args.n_comp_to_use)
# 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, best_theta = do_cma(cma_args, result["first_n_pcs"],
origin_param, save_dir, starting_coord, cma_args.cma_var)
np.savetxt(f"{cma_intermediate_data_dir}/opt_mean_path.csv", opt_path_mean, delimiter=',')
np.savetxt(f"{cma_intermediate_data_dir}/{best_theta_file_name}.csv", best_theta, delimiter=',')
episode_returns, conti_ppo_full_param_traj_dir_path = do_ppo(args=cma_args, start_theta=best_theta, parent_this_run_dir=cma_intermediate_data_dir, full_space_save_dir=save_dir)
np.savetxt(f"{cma_intermediate_data_dir}/ppo part returns.csv", episode_returns, delimiter=',')
plot_dir = get_plot_dir(cma_args)
cma_and_then_ppo_plot_dir = get_cma_and_then_ppo_plot_dir(plot_dir, cma_args.n_comp_to_use,
cma_run_num, cma_num_steps=cma_args.cma_num_timesteps,
ppo_num_steps=cma_args.ppo_num_timesteps,
origin=origin)
if not os.path.exists(cma_and_then_ppo_plot_dir):
os.makedirs(cma_and_then_ppo_plot_dir)
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_and_then_ppo_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)
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_and_then_ppo_plot_dir, ret_plot_name, mean_rets, min_rets, max_rets, show=False)
final_ppo_ep_name = f"final episodes returns after CMA"
plot_2d(cma_and_then_ppo_plot_dir, final_ppo_ep_name, np.arange(len(episode_returns)),
episode_returns, "num episode", "episode returns", 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_and_then_ppo_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)
skip_rows = lambda x: x%2 == 0
conti_ppo_params = get_allinone_concat_df(conti_ppo_full_param_traj_dir_path, index=0, skip_rows=skip_rows).values
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 - result["final_concat_params"], ord=2) for opt_mean in np.vstack((opt_mean_path_in_old_basis, conti_ppo_params))]
distance_to_final_plot_name = f"distance_to_final over generations "
plot_2d(cma_and_then_ppo_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 = "final_param"
origin_name = 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,
params_scope="pi")
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
pca_indexes = cma_args.other_pca_index
pca_indexes = [int(pca_index) for pca_index in pca_indexes.split(":")]
cma_run_num, cma_intermediate_data_dir = generate_run_dir(
get_cma_and_then_ppo_run_dir,
intermediate_dir=intermediate_data_dir,
pca_indexes=pca_indexes,
cma_steps=cma_args.cma_num_timesteps)
best_theta_file_name = "best theta from cma"
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]
# if not os.path.exists(f"{cma_intermediate_data_dir}/{best_theta_file_name}.csv") or \
# not os.path.exists(f"{cma_intermediate_data_dir}/opt_mean_path.csv"):
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
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")
'''
==========================================================================================
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
logger.log("grab start params")
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]
if origin_name == "final_param":
origin_param = result["final_concat_params"]
elif origin_name == "start_param":
origin_param = start_params
else:
origin_param = result["mean_param"]
pcs = result["pcs_components"]
pcs_to_use = pcs[pca_indexes]
starting_coord = do_proj_on_first_n(start_params, pcs_to_use, origin_param)
plot_dir = get_plot_dir(cma_args)
cma_and_then_ppo_plot_dir = get_cma_and_then_ppo_plot_dir(
plot_dir,
pca_indexes,
cma_run_num,
cma_num_steps=cma_args.cma_num_timesteps,
ppo_num_steps=cma_args.ppo_num_timesteps,
origin=origin_name)
# starting_coord = (1/2*np.max(xcoordinates_to_eval), 1/2*np.max(ycoordinates_to_eval)) # use mean
mean_rets, min_rets, max_rets, opt_path, opt_path_mean, best_pi_theta = do_cma(
cma_args, pcs_to_use, origin_param, save_dir, starting_coord,
cma_args.cma_var)
# np.savetxt(f"{cma_intermediate_data_dir}/opt_mean_path.csv", opt_path_mean, delimiter=',')
np.savetxt(f"{cma_intermediate_data_dir}/{best_theta_file_name}.csv",
best_pi_theta,
delimiter=',')
ret_plot_name = f"cma return on {pca_indexes} dim space of real pca plane, " \
f"explained {np.sum(result['explained_variance_ratio'][pca_indexes])}"
plot_cma_returns(cma_and_then_ppo_plot_dir,
ret_plot_name,
mean_rets,
min_rets,
max_rets,
show=False)
vf_final_file = get_full_param_traj_file_path(traj_params_dir_name,
"vf_final")
vf_final_params = pd.read_csv(vf_final_file, header=None).values[0]
episode_returns, conti_ppo_full_param_traj_dir_path = do_ppo(
args=cma_args,
start_pi_theta=best_pi_theta,
parent_this_run_dir=cma_intermediate_data_dir,
full_space_save_dir=save_dir,
vf_final_params=vf_final_params)
# dump_row_write_csv(cma_intermediate_data_dir, episode_returns, "ppo part returns")
np.savetxt(f"{cma_intermediate_data_dir}/ppo part returns.csv",
episode_returns,
delimiter=",")
if not os.path.exists(cma_and_then_ppo_plot_dir):
os.makedirs(cma_and_then_ppo_plot_dir)
conti_ppo_params = get_allinone_concat_df(
conti_ppo_full_param_traj_dir_path).values
if len(pca_indexes) <= 2:
pcs_to_plot_contour = pca_indexes
if len(pcs_to_plot_contour) == 1:
if pcs_to_plot_contour[0] + 1 < cma_args.n_components:
pcs_to_plot_contour.append(pcs_to_plot_contour[0] + 1)
else:
pcs_to_plot_contour.append(pcs_to_plot_contour[0] - 1)
proj_coords = project(result["pcs_components"],
pcs_slice=pcs_to_plot_contour,
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=False)
assert proj_coords.shape[1] == 2
if len(pca_indexes) == 1:
opt_path_mean_2d = np.hstack(
(opt_path_mean, np.zeros((1, len(opt_path_mean))).T))
else:
opt_path_mean_2d = opt_path_mean
xcoordinates_to_eval, ycoordinates_to_eval = gen_subspace_coords(
cma_args,
np.vstack((proj_coords, opt_path_mean_2d)).T)
projected_after_ppo_params = do_proj_on_first_n(
conti_ppo_params, pcs[pcs_to_plot_contour], origin_param)
full_path = np.vstack((opt_path_mean_2d, projected_after_ppo_params))
eval_returns = do_eval_returns(cma_args,
intermediate_data_dir,
pcs[pcs_to_plot_contour],
origin_param,
xcoordinates_to_eval,
ycoordinates_to_eval,
save_dir,
pca_center=origin_name,
reuse=False)
plot_contour_trajectory(
cma_and_then_ppo_plot_dir,
f"{origin_name}_origin_eval_return_contour_plot",
xcoordinates_to_eval,
ycoordinates_to_eval,
eval_returns,
proj_coords[:, 0],
proj_coords[:, 1],
result["explained_variance_ratio"][pcs_to_plot_contour],
num_levels=25,
show=False,
sub_alg_path=full_path)
final_ppo_ep_name = f"final episodes returns CMA PPO"
plot_2d(cma_and_then_ppo_plot_dir, final_ppo_ep_name,
np.arange(len(episode_returns)), episode_returns, "num episode",
"episode returns", False)
opt_mean_path_in_old_basis = [
mean_projected_param.dot(pcs_to_use) + result["mean_param"]
for mean_projected_param in opt_path_mean
]
distance_to_final = [
LA.norm(opt_mean - result["final_params"], ord=2)
for opt_mean in opt_mean_path_in_old_basis
]
distance_to_final_plot_name = f"distance_to_final over generations of CMA "
plot_2d(cma_and_then_ppo_plot_dir, distance_to_final_plot_name,
np.arange(len(distance_to_final)), distance_to_final,
"num generation", "distance_to_final", False)
distance_to_final_ppo = [
LA.norm(opt_mean - result["final_params"], ord=2)
for opt_mean in conti_ppo_params
]
distance_to_final_plot_name = f"distance_to_final over generations of PPO"
plot_2d(cma_and_then_ppo_plot_dir, distance_to_final_plot_name,
np.arange(len(distance_to_final_ppo)), distance_to_final_ppo,
"num generation", "distance_to_final", False)
def visualize_policy_and_collect_COM(seed, run_num, policy_env,
policy_num_timesteps, policy_seed,
policy_run_num):
logger.log(sys.argv)
common_arg_parser = get_common_parser()
args, cma_unknown_args = common_arg_parser.parse_known_args()
args.env = policy_env
args.seed = policy_seed
args.num_timesteps = policy_num_timesteps
args.run_num = policy_run_num
this_run_dir = get_dir_path_for_this_run(args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
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.env.disableViewer = False
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
env_out.seed(seed)
return env_out
env = DummyVecEnv([make_env])
if args.normalize:
env = VecNormalize(env)
model = PPO2.load(f"{save_dir}/ppo2", seed=seed)
model.set_pi_from_flat(final_params)
if args.normalize:
env.load_running_average(save_dir)
sk = env.venv.envs[0].env.env.robot_skeleton
lagrangian_values = {}
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
plot_dir = get_plot_dir(policy_env=args.env,
policy_num_timesteps=policy_num_timesteps,
policy_run_num=policy_run_num,
policy_seed=policy_seed,
eval_seed=seed,
eval_run_num=run_num,
additional_note="")
if os.path.exists(plot_dir):
shutil.rmtree(plot_dir)
os.makedirs(plot_dir)
env = VecVideoRecorder(env,
plot_dir,
record_video_trigger=lambda x: x == 0,
video_length=3000,
name_prefix="3000000agent-{}".format(args.env))
lagrangian_values["M"] = [sk.M.reshape((-1, 1))]
lagrangian_values["COM"] = [sk.C.reshape((-1, 1))]
lagrangian_values["Coriolis"] = [sk.c.reshape((-1, 1))]
lagrangian_values["q"] = [sk.q.reshape((-1, 1))]
lagrangian_values["dq"] = [sk.dq.reshape((-1, 1))]
contact_values = {}
neuron_values = model.give_neuron_values(obs)
raw_layer_values_list = [[neuron_value.reshape((-1, 1))]
for neuron_value in neuron_values]
env.render()
ep_infos = []
steps_to_first_done = 0
first_done = False
# epi_rew = 0
for _ in range(3000):
actions = model.step(obs)[0]
# yield neuron_values
obs, rew, done, infos = env.step(actions)
# epi_rew+= rew[0]
if done and not first_done:
first_done = True
if not first_done:
steps_to_first_done += 1
neuron_values = model.give_neuron_values(obs)
for i, layer in enumerate(neuron_values):
raw_layer_values_list[i].append(layer.reshape((-1, 1)))
# fill_contacts_jac_dict(infos[0]["contacts"], contact_dict=contact_values, neuron_values=neuron_values)
lagrangian_values["M"].append(sk.M.reshape((-1, 1)))
lagrangian_values["q"].append(sk.q.reshape((-1, 1)))
lagrangian_values["dq"].append(sk.dq.reshape((-1, 1)))
lagrangian_values["COM"].append(sk.C.reshape((-1, 1)))
lagrangian_values["Coriolis"].append(sk.c.reshape((-1, 1)))
# env.render()
# time.sleep(1)
for info in infos:
maybe_ep_info = info.get('episode')
if maybe_ep_info is not None:
ep_infos.append(maybe_ep_info)
env.render()
done = done.any()
if done:
episode_rew = safe_mean([ep_info['r'] for ep_info in ep_infos])
print(f'episode_rew={episode_rew}')
# print(f'episode_rew={epi_rew}')
# epi_rew = 0
obs = env.reset()
#Hstack into a big matrix
lagrangian_values["M"] = np.hstack(lagrangian_values["M"])
lagrangian_values["COM"] = np.hstack(lagrangian_values["COM"])
lagrangian_values["Coriolis"] = np.hstack(lagrangian_values["Coriolis"])
lagrangian_values["q"] = np.hstack(lagrangian_values["q"])
lagrangian_values["dq"] = np.hstack(lagrangian_values["dq"])
# for contact_body_name, l in contact_values.items():
# body_contact_dict = contact_values[contact_body_name]
# for name, l in body_contact_dict.items():
# body_contact_dict[name] = np.hstack(body_contact_dict[name])
input_values = np.hstack(raw_layer_values_list[0])
layers_values = [
np.hstack(layer_list) for layer_list in raw_layer_values_list
][1:-2] # drop variance and inputs
for i, com in enumerate(lagrangian_values["COM"]):
plt.figure()
plt.plot(np.arange(len(com)), com)
plt.xlabel("time")
plt.ylabel(f"COM{i}")
plt.savefig(f"{plot_dir}/COM{i}.jpg")
plt.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]
fig = plt.figure(figsize=(12, 12))
ax = fig.gca()
ax.axis('off')
preload_neuron_values_list = preload_neurons(args, save_dir, final_params,
args.eval_num_timesteps)
obz_norm, latent_norm, dist_norm = give_normalizers(
preload_neuron_values_list)
latent_cmap = plt.get_cmap("Oranges")
obz_cmap = plt.get_cmap("Blues")
dist_cmap = plt.get_cmap("Greys")
neuron_values_gen = neuron_values_generator(args, save_dir, final_params,
args.eval_num_timesteps)
left, right, bottom, top = 0.1, 0.9, 0.1, 0.9
result_artists = []
def init():
try:
first_neurons = neuron_values_gen.__next__()
except StopIteration:
return
layer_sizes = [layer.shape[1] for layer in first_neurons]
first_neurons = np.array(
[neuron_value.reshape(-1) for neuron_value in first_neurons])
v_spacing = (top - bottom) / float(max(layer_sizes))
h_spacing = (right - left) / float(len(layer_sizes) - 1)
# Nodes
for n, neuron_layer_value in enumerate(first_neurons):
neuron_layer_value = neuron_layer_value.reshape(-1)
layer_size = len(neuron_layer_value)
layer_top = v_spacing * (layer_size - 1) / 2. + (top + bottom) / 2.
for m, neuron_value in enumerate(neuron_layer_value):
if n == 0:
# obz
circle = plt.Circle(
(n * h_spacing + left, layer_top - m * v_spacing),
v_spacing / 4.,
color=obz_cmap(obz_norm(neuron_value)),
ec='k',
zorder=4)
elif n >= len(first_neurons) - 2:
# dist
circle = plt.Circle(
(n * h_spacing + left, layer_top - m * v_spacing),
v_spacing / 4.,
color=dist_cmap(dist_norm(neuron_value)),
ec='k',
zorder=4)
else:
#latent
circle = plt.Circle(
(n * h_spacing + left, layer_top - m * v_spacing),
v_spacing / 4.,
color=latent_cmap(latent_norm(neuron_value)),
ec='k',
zorder=4)
ax.add_artist(circle)
result_artists.append(circle)
return result_artists
def update_neuron(neuron_values):
num_of_obz_neurons = neuron_values[0].reshape(-1).shape[0]
num_of_dist_neurons = np.concatenate(neuron_values[-2:],
axis=1).shape[1]
neuron_values = np.concatenate(neuron_values,
axis=1).reshape(-1).ravel()
for i, neuron_value in enumerate(neuron_values):
if i < num_of_obz_neurons:
# obz
result_artists[i].set_color(obz_cmap(obz_norm(neuron_value)))
elif i >= neuron_values.shape[0] - num_of_dist_neurons:
# dist
result_artists[i].set_color(dist_cmap(dist_norm(neuron_value)))
else:
#latent
result_artists[i].set_color(
latent_cmap(latent_norm(neuron_value)))
# plt.draw()
return result_artists
rot_animation = FuncAnimation(fig,
update_neuron,
frames=neuron_values_gen,
init_func=init,
interval=3)
plt.show()
print(f"~~~~~~~~~~~~~~~~~~~~~~saving to {plot_dir_alg}/neuron_vis.pdf")
file_path = f"{plot_dir_alg}/neuron_firing.gif"
if os.path.isfile(file_path):
os.remove(file_path)
rot_animation.save(file_path, dpi=80, writer='imagemagick')
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)
save_dir = get_save_dir(this_run_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])
# env_out = gym.make(args.env)
# env_out = bench.Monitor(env_out, logger.get_dir(), allow_early_resets=True)
if args.normalize:
env = VecNormalize(env)
# policy = MlpPolicy
model = PPO2.load(f"{save_dir}/ppo2") # this also loads V function
# 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.set_pi_from_flat(final_params)
if args.normalize:
env.load_running_average(save_dir)
sk = env.venv.envs[0].env.env.robot_skeleton
lagrangian_values = {}
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
# env = VecVideoRecorder(env, "./",
# record_video_trigger=lambda x: x == 0, video_length=3000,
# name_prefix="3000000agent-{}".format(args.env))
lagrangian_values["M"] = [sk.M.reshape((-1, 1))]
lagrangian_values["COM"] = [sk.C.reshape((-1, 1))]
lagrangian_values["Coriolis"] = [sk.c.reshape((-1, 1))]
lagrangian_values["q"] = [sk.q.reshape((-1, 1))]
lagrangian_values["dq"] = [sk.dq.reshape((-1, 1))]
contact_values = {}
neuron_values = model.give_neuron_values(obs)
layer_values_list = [[neuron_value.reshape((-1, 1))]
for neuron_value in neuron_values]
env.render()
ep_infos = []
steps_to_first_done = 0
first_done = False
for _ in range(3000):
actions = model.step(obs)[0]
# yield neuron_values
obs, rew, done, infos = env.step(actions)
if done and not first_done:
first_done = True
if not first_done:
steps_to_first_done += 1
neuron_values = model.give_neuron_values(obs)
for i, layer in enumerate(neuron_values):
layer_values_list[i].append(layer.reshape((-1, 1)))
fill_contacts_jac_dict(infos[0]["contacts"],
contact_dict=contact_values,
neuron_values=neuron_values)
lagrangian_values["M"].append(sk.M.reshape((-1, 1)))
lagrangian_values["q"].append(sk.q.reshape((-1, 1)))
lagrangian_values["dq"].append(sk.dq.reshape((-1, 1)))
lagrangian_values["COM"].append(sk.C.reshape((-1, 1)))
lagrangian_values["Coriolis"].append(sk.c.reshape((-1, 1)))
env.render()
# time.sleep(1)
for info in infos:
maybe_ep_info = info.get('episode')
if maybe_ep_info is not None:
ep_infos.append(maybe_ep_info)
# env.render()
done = done.any()
if done:
episode_rew = safe_mean([ep_info['r'] for ep_info in ep_infos])
print(f'episode_rew={episode_rew}')
obs = env.reset()
#Hstack into a big matrix
lagrangian_values["M"] = np.hstack(lagrangian_values["M"])
lagrangian_values["COM"] = np.hstack(lagrangian_values["COM"])
lagrangian_values["Coriolis"] = np.hstack(lagrangian_values["Coriolis"])
lagrangian_values["q"] = np.hstack(lagrangian_values["q"])
lagrangian_values["dq"] = np.hstack(lagrangian_values["dq"])
for contact_body_name, l in contact_values.items():
body_contact_dict = contact_values[contact_body_name]
for name, l in body_contact_dict.items():
body_contact_dict[name] = np.hstack(body_contact_dict[name])
layer_values_list = [
np.hstack(layer_list) for layer_list in layer_values_list
][1:-2] # drop variance
# plt.scatter(lagrangian_values["M"][15], layer_values_list[1][2])
# plt.scatter(lagrangian_values["M"][11], layer_values_list[0][63])
out_dir = f"/home/panda-linux/PycharmProjects/low_dim_update_dart/low_dim_update_stable/neuron_vis/plots_{args.env}_{args.num_timesteps}"
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
os.makedirs(out_dir)
all_weights = model.get_all_weight_values()
for ind, weights in enumerate(all_weights):
fname = f"{out_dir}/weights_layer_{ind}.txt"
np.savetxt(fname, weights)
PLOT_CUTOFF = steps_to_first_done
plot_everything(lagrangian_values, layer_values_list, out_dir, PLOT_CUTOFF)
scatter_the_linear_significant_ones(lagrangian_values,
layer_values_list,
threshold=0.6,
out_dir=out_dir)
scatter_the_nonlinear_significant_but_not_linear_ones(
lagrangian_values,
layer_values_list,
linear_threshold=0.3,
nonlinear_threshold=0.6,
out_dir=out_dir)
#
# contact_dicts = {}
# for contact_body_name, l in contact_values.items():
# body_contact_dict = contact_values[contact_body_name]
#
#
# contact_dicts[contact_body_name] = {}
#
# build_dict = contact_dicts[contact_body_name]
#
# build_dict["body"] = {}
# build_dict["layer"] = {}
# for name, l in body_contact_dict.items():
# for i in range(len(l)):
#
# if name == contact_body_name:
# build_dict["body"][f"{contact_body_name}_{i}"] = l[i]
# else:
# build_dict["layer"][f"layer_{name}_neuron_{i}"] = l[i]
#
# body_contact_df = pd.DataFrame.from_dict(build_dict["body"], "index")
# layer_contact_df = pd.DataFrame.from_dict(build_dict["layer"], "index")
# body_contact_df.to_csv(f"{data_dir}/{contact_body_name}_contact.txt", sep='\t')
# layer_contact_df.to_csv(f"{data_dir}/{contact_body_name}_layers.txt", sep='\t')
# #TO CSV format
# data_dir = f"/home/panda-linux/PycharmProjects/low_dim_update_dart/mictools/examples/neuron_vis_data{args.env}_time_steps_{args.num_timesteps}"
# if os.path.exists(data_dir):
# shutil.rmtree(data_dir)
#
# os.makedirs(data_dir)
#
# for contact_body_name, d in contact_dicts.items():
#
# build_dict = d
#
# body_contact_df = pd.DataFrame.from_dict(build_dict["body"], "index")
# layer_contact_df = pd.DataFrame.from_dict(build_dict["layer"], "index")
#
# body_contact_df.to_csv(f"{data_dir}/{contact_body_name}_contact.txt", sep='\t')
# layer_contact_df.to_csv(f"{data_dir}/{contact_body_name}_layers.txt", sep='\t')
#
#
#
# neurons_dict = {}
# for layer_index in range(len(layer_values_list)):
# for neuron_index in range(len(layer_values_list[layer_index])):
# neurons_dict[f"layer_{layer_index}_neuron_{neuron_index}"] = layer_values_list[layer_index][neuron_index]
#
# for i in range(len(lagrangian_values["COM"])):
# neurons_dict[f"COM_index_{i}"] = lagrangian_values["COM"][i]
#
# neuron_df = pd.DataFrame.from_dict(neurons_dict, "index")
#
#
#
# lagrangian_dict = {}
# for k,v in lagrangian_values.items():
# for i in range(len(v)):
# lagrangian_dict[f"{k}_index_{i}"] = v[i]
#
# lagrangian_df = pd.DataFrame.from_dict(lagrangian_dict, "index")
#
#
# neuron_df.to_csv(f"{data_dir}/neurons.txt", sep='\t')
# lagrangian_df.to_csv(f"{data_dir}/lagrangian.txt", sep='\t')
# cor = {}
# best_cor = {}
# cor["M"] = get_correlations(lagrangian_values["M"], layer_values_list)
# best_cor["M"] = [np.max(np.abs(cor_m)) for cor_m in cor["M"]]
#
#
# cor["COM"] = get_correlations(lagrangian_values["COM"], layer_values_list)
# best_cor["COM"] = [np.max(np.abs(cor_m)) for cor_m in cor["COM"]]
#
# cor["Coriolis"] = get_correlations(lagrangian_values["Coriolis"], layer_values_list)
# best_cor["Coriolis"] = [np.max(np.abs(cor_m)) for cor_m in cor["Coriolis"]]
# best_cor["Coriolis_argmax"] = [np.argmax(np.abs(cor_m)) for cor_m in cor["Coriolis"]]
#
#
#
#
# ncor = {}
# nbest_cor = {}
# ncor["M"] = get_normalized_correlations(lagrangian_values["M"], layer_values_list)
# nbest_cor["M"] = [np.max(np.abs(cor_m)) for cor_m in ncor["M"]]
#
#
# ncor["COM"] = get_normalized_correlations(lagrangian_values["COM"], layer_values_list)
# nbest_cor["COM"] = [np.max(np.abs(cor_m)) for cor_m in ncor["COM"]]
#
# ncor["Coriolis"] = get_normalized_correlations(lagrangian_values["Coriolis"], layer_values_list)
# nbest_cor["Coriolis"] = [np.max(np.abs(cor_m)) for cor_m in ncor["Coriolis"]]
# nbest_cor["Coriolis_argmax"] = [np.argmax(np.abs(cor_m)) for cor_m in ncor["Coriolis"]]
#
#
#
#
#
# lin_reg = {"perm_1":{}, "perm_2":{}}
# best_lin_reg = {"perm_1":{}, "perm_2":{}}
# lin_reg["perm_1"]["M"], best_lin_reg["perm_1"]["M"] = get_results("M", lagrangian_values, layer_values_list, perm_num=1)
# lin_reg["perm_2"]["M"], best_lin_reg["perm_2"]["M"] = get_results("M", lagrangian_values, layer_values_list, perm_num=2)
# lin_reg["perm_1"]["COM"], best_lin_reg["perm_1"]["COM"] = get_results("COM", lagrangian_values, layer_values_list, perm_num=1)
# lin_reg["perm_2"]["COM"], best_lin_reg["perm_2"]["COM"] = get_results("COM", lagrangian_values, layer_values_list, perm_num=2)
#
#
# lin_reg_1["M"] = get_linear_regressions_1_perm(lagrangian_values["M"], layer_values_list)
# lin_reg_2["M"] = get_linear_regressions_2_perm(lagrangian_values["M"], layer_values_list)
# best_lin_reg_2["M"] = []
# for lin_l in lin_reg_2["M"]:
# if lin_l == []:
# best_lin_reg_2["M"].append([])
# else:
# best_lin_reg_2["M"].append(lin_l[np.argmin(lin_l[:,0])])
#
# best_lin_reg_1["M"] = []
# for lin_l in lin_reg_1["M"]:
# if lin_l == []:
# best_lin_reg_1["M"].append([])
# else:
# best_lin_reg_1["M"].append(lin_l[np.argmin(lin_l[:,0])])
# best_lin_reg_1["M"] = np.array(best_lin_reg_1["M"])
# best_lin_reg_2["M"] = np.array(best_lin_reg_2["M"])
#
#
# lin_reg_1["M"].dump("lin_reg_1_M.txt")
# lin_reg_2["M"].dump("lin_reg_2_M.txt")
# best_lin_reg_1["M"].dump("best_lin_reg_1_M.txt")
# best_lin_reg_2["M"].dump("best_lin_reg_2_M.txt")
#
# lin_reg_1["COM"] = get_linear_regressions_1_perm(lagrangian_values["COM"], layer_values_list)
# lin_reg_2["COM"] = get_linear_regressions_2_perm(lagrangian_values["COM"], layer_values_list)
# best_lin_reg_2["COM"] = []
# for lin_l in lin_reg_2["COM"]:
# if lin_l == []:
# best_lin_reg_2["COM"].append([])
# else:
# best_lin_reg_2["COM"].append(lin_l[np.argmin(lin_l[:, 0])])
#
# best_lin_reg_1["COM"] = []
# for lin_l in lin_reg_1["COM"]:
# if lin_l == []:
# best_lin_reg_1["COM"].append([])
# else:
# best_lin_reg_1["COM"].append(lin_l[np.argmin(lin_l[:, 0])])
#
#
# best_lin_reg_1["COM"] = np.array(best_lin_reg_1["M"])
# best_lin_reg_2["COM"] = np.array(best_lin_reg_2["M"])
# lin_reg_1["COM"].dump("lin_reg_1_COM.txt")
# lin_reg_2["COM"].dump("lin_reg_2_COM.txt")
# best_lin_reg_1["COM"].dump("best_lin_reg_1_COM.txt")
# best_lin_reg_2["COM"].dump("best_lin_reg_2_COM.txt")
pass
def do_ppos(ppos_args, result, intermediate_data_dir, origin_param):
ppos_args.alg = "ppo_subspace"
logger.log(f"#######TRAIN: {ppos_args}")
this_run_dir = get_dir_path_for_this_run(ppos_args)
if os.path.exists(this_run_dir):
import shutil
shutil.rmtree(this_run_dir)
os.makedirs(this_run_dir)
log_dir = get_log_dir(this_run_dir)
save_dir = get_save_dir(this_run_dir)
full_param_traj_dir_path = get_full_params_dir(this_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(save_dir):
import shutil
shutil.rmtree(save_dir)
os.makedirs(save_dir)
run_info = {"full_param_traj_dir_path": full_param_traj_dir_path}
logger.configure(log_dir)
tic = time.time()
def make_env():
env_out = gym.make(ppos_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 ppos_args.normalize:
env = VecNormalize(env)
set_global_seeds(ppos_args.seed)
policy = MlpMultPolicy
model = PPO2(policy=policy,
env=env,
n_steps=ppos_args.n_steps,
nminibatches=ppos_args.nminibatches,
lam=0.95,
gamma=0.99,
noptepochs=10,
ent_coef=0.0,
learning_rate=3e-4,
cliprange=0.2,
policy_kwargs={"num_comp": len(result["first_n_pcs"])},
pcs=result["first_n_pcs"],
origin_theta=origin_param)
model.tell_run_info(run_info)
eprews, optimization_path = model.learn(
total_timesteps=ppos_args.ppos_num_timesteps,
give_optimization_path=True)
toc = time.time()
logger.log(
f"####################################PPOS took {toc-tic} seconds")
moving_ave_rewards = get_moving_aves(eprews, 100)
return eprews, moving_ave_rewards, optimization_path
def main():
import sys
logger.log(sys.argv)
ppos_arg_parser = get_common_parser()
ppos_args, ppos_unknown_args = ppos_arg_parser.parse_known_args()
full_space_alg = ppos_args.alg
# origin = "final_param"
origin = ppos_args.origin
this_run_dir = get_dir_path_for_this_run(ppos_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)
ppos_run_num, ppos_intermediate_data_dir = generate_run_dir(
get_ppos_returns_dirname,
intermediate_dir=intermediate_data_dir,
n_comp=ppos_args.n_comp_to_use)
'''
==========================================================================================
get the pc vectors
==========================================================================================
'''
proj_or_not = (ppos_args.n_comp_to_use == 2)
result = do_pca(ppos_args.n_components,
ppos_args.n_comp_to_use,
traj_params_dir_name,
intermediate_data_dir,
proj=proj_or_not,
origin=origin,
use_IPCA=ppos_args.use_IPCA,
chunk_size=ppos_args.chunk_size)
'''
==========================================================================================
eval all xy coords
==========================================================================================
'''
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)
if origin == "final_param":
back_final_param = low_dim_to_old_basis(last_proj_coord,
result["first_n_pcs"],
origin_param)
assert np.testing.assert_almost_equal(back_final_param, final_param)
starting_coord = last_proj_coord
logger.log(f"PPOS 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] == ppos_args.n_comp_to_use
eprews, moving_ave_rewards, optimization_path = do_ppos(
ppos_args, result, intermediate_data_dir, origin_param)
ppos_args.alg = full_space_alg
plot_dir = get_plot_dir(ppos_args)
ppos_plot_dir = get_ppos_plot_dir(plot_dir, ppos_args.n_comp_to_use,
ppos_run_num)
if not os.path.exists(ppos_plot_dir):
os.makedirs(ppos_plot_dir)
ret_plot_name = f"cma return on {ppos_args.n_comp_to_use} dim space of real pca plane, " \
f"explained {np.sum(result['explained_variance_ratio'][:ppos_args.n_comp_to_use])}"
plot_ppos_returns(ppos_plot_dir,
ret_plot_name,
moving_ave_rewards,
show=False)
if ppos_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(
ppos_args,
np.vstack((proj_coords, optimization_path)).T)
eval_returns = do_eval_returns(ppos_args,
intermediate_data_dir,
result["first_n_pcs"],
origin_param,
xcoordinates_to_eval,
ycoordinates_to_eval,
save_dir,
pca_center=origin)
plot_contour_trajectory(ppos_plot_dir,
"end_point_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=optimization_path)
opt_mean_path_in_old_basis = [
low_dim_to_old_basis(projected_opt_params, result["first_n_pcs"],
origin_param)
for projected_opt_params in optimization_path
]
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(ppos_plot_dir, distance_to_final_plot_name,
np.arange(len(distance_to_final)), distance_to_final,
"num generation", "distance_to_final", False)
def eval_trained_policy_and_collect_data(eval_seed, eval_run_num, policy_env, policy_num_timesteps, policy_seed, policy_run_num, additional_note):
logger.log(sys.argv)
common_arg_parser = get_common_parser()
args, cma_unknown_args = common_arg_parser.parse_known_args()
args.env = policy_env
args.seed = policy_seed
args.num_timesteps = policy_num_timesteps
args.run_num = policy_run_num
this_run_dir = get_dir_path_for_this_run(args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
save_dir = get_save_dir( this_run_dir)
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)
env_out.seed(eval_seed)
return env_out
env = DummyVecEnv([make_env])
running_env = env.envs[0].env.env
set_global_seeds(eval_seed)
running_env.seed(eval_seed)
if args.normalize:
env = VecNormalize(env)
model = PPO2.load(f"{save_dir}/ppo2", seed=eval_seed)
model.set_pi_from_flat(final_params)
if args.normalize:
env.load_running_average(save_dir)
# is it necessary?
running_env = env.venv.envs[0].env.env
lagrangian_values = {}
obs = np.zeros((env.num_envs,) + env.observation_space.shape)
obs[:] = env.reset()
# env = VecVideoRecorder(env, "./",
# record_video_trigger=lambda x: x == 0, video_length=3000,
# name_prefix="3000000agent-{}".format(args.env))
#init lagrangian values
for lagrangian_key in lagrangian_keys:
flat_array = running_env.get_lagrangian_flat_array(lagrangian_key)
lagrangian_values[lagrangian_key] = [flat_array]
neuron_values = model.give_neuron_values(obs)
raw_layer_values_list = [[neuron_value.reshape((-1,1))] for neuron_value in neuron_values]
# env.render()
ep_infos = []
steps_to_first_done = 0
first_done = False
for _ in range(30000):
actions = model.step(obs)[0]
# yield neuron_values
obs, rew, done, infos = env.step(actions)
if done and not first_done:
first_done = True
if not first_done:
steps_to_first_done += 1
neuron_values = model.give_neuron_values(obs)
for i, layer in enumerate(neuron_values):
raw_layer_values_list[i].append(layer.reshape((-1,1)))
# fill_contacts_jac_dict(infos[0]["contacts"], contact_dict=contact_values, neuron_values=neuron_values)
# filling lagrangian values
for lagrangian_key in lagrangian_keys:
flat_array = running_env.get_lagrangian_flat_array(lagrangian_key)
lagrangian_values[lagrangian_key].append(flat_array)
# env.render()
# time.sleep(1)
for info in infos:
maybe_ep_info = info.get('episode')
if maybe_ep_info is not None:
ep_infos.append(maybe_ep_info)
# env.render()
done = done.any()
if done:
episode_rew = safe_mean([ep_info['r'] for ep_info in ep_infos])
print(f'episode_rew={episode_rew}')
obs = env.reset()
#Hstack into a big matrix
for lagrangian_key in lagrangian_keys:
lagrangian_values[lagrangian_key] = np.hstack(lagrangian_values[lagrangian_key])
# for contact_body_name, l in contact_values.items():
# body_contact_dict = contact_values[contact_body_name]
# for name, l in body_contact_dict.items():
# body_contact_dict[name] = np.hstack(body_contact_dict[name])
input_values = np.hstack(raw_layer_values_list[0])
layers_values = [np.hstack(layer_list) for layer_list in raw_layer_values_list][1:-2]# drop variance and inputs
data_dir = get_data_dir(policy_env=args.env, policy_num_timesteps=policy_num_timesteps, policy_run_num=policy_run_num
, policy_seed=policy_seed, eval_seed=eval_seed, eval_run_num=eval_run_num, additional_note=additional_note)
if os.path.exists(data_dir):
shutil.rmtree(data_dir)
os.makedirs(data_dir)
lagrangian_values_fn = f"{data_dir}/lagrangian.pickle"
with open(lagrangian_values_fn, 'wb') as handle:
pickle.dump(lagrangian_values, handle, protocol=pickle.HIGHEST_PROTOCOL)
input_values_fn = f"{data_dir}/input_values.npy"
layers_values_fn = f"{data_dir}/layer_values.npy"
np.save(input_values_fn, input_values)
np.save(layers_values_fn, layers_values)
all_weights = model.get_all_weight_values()
for ind, weights in enumerate(all_weights):
fname = f"{data_dir}/weights_layer_{ind}.txt"
np.savetxt(fname, weights)
