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 neuron_values_generator(args, save_dir, pi_theta, eval_timesteps):
# logger.log(f"#######EVAL: {args}")
neuron_values_list = []
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)
# 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 = PPO2.load(f"{save_dir}/ppo2") # this also loads V function
if pi_theta is not None:
model.set_pi_from_flat(pi_theta)
if args.normalize:
env.load_running_average(save_dir)
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
env.render()
ep_infos = []
while 1:
neuron_values, actions, _, _, _ = model.step_with_neurons(obs)
# neuron_values = model.give_neuron_values(obs)
# neuron_values_list.append( neuron_values )
yield neuron_values
obs, rew, done, infos = env.step(actions)
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()
def visualize_neurons(args, save_dir, pi_theta, eval_timesteps):
# logger.log(f"#######EVAL: {args}")
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"{save_dir}/ppo2") # this also loads V function
if pi_theta is not None:
model.set_pi_from_flat(pi_theta)
if args.normalize:
env.load_running_average(save_dir)
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
ep_infos = []
for _ in range(eval_timesteps):
actions = model.step(obs)[0]
neuron_values = model.give_neuron_values(obs)
obs, rew, done, infos = env.step(actions)
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:
if pi_theta is None:
episode_rew = safe_mean([ep_info['r'] for ep_info in ep_infos])
print(f'episode_rew={episode_rew}')
obs = env.reset()
return safe_mean([ep_info['r'] for ep_info in ep_infos])
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 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)
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 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)
def run_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,
keys_to_include,
metric_param,
linear_top_vars_list=None,
linear_correlation_neuron_list=None,
visualize=False):
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)
if visualize:
vis_dir = get_aug_plot_dir(this_run_dir) + "_vis"
final_file = get_full_param_traj_file_path(full_param_traj_dir_path,
"pi_final")
final_params = pd.read_csv(final_file, header=None).values[0]
else:
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 linear_top_vars_list 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,
metric_param=metric_param)
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 = visualize
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 = not visualize
if args.normalize:
env = VecNormalize(env)
policy = MlpPolicy
set_global_seeds(args.seed)
walker_env.seed(args.seed)
num_dof = walker_env.robot_skeleton.ndofs
show_M_matrix(num_dof, lagrangian_inds_to_include,
top_num_to_include_slice, log_dir)
if visualize:
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)
run_model(model=model, env=env, vedio_dir=vis_dir)
else:
# 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]
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