def _make_warmstart_cartpole():
"""Warm-start VecNormalize by stepping through CartPole"""
venv = DummyVecEnv([lambda: gym.make("CartPole-v1")])
venv = VecNormalize(venv)
venv.reset()
venv.get_original_obs()
for _ in range(100):
actions = [venv.action_space.sample()]
venv.step(actions)
return venv
def main():
# Parse command line args
parser = arg_parser()
parser.add_argument("-hw", "--use-hardware", action="store_true")
parser.add_argument("-l", "--load", type=str, default=None)
args = parser.parse_args()
env = "QubeSwingupEnv"
def make_env():
env_out = QubeSwingupEnv(use_simulator=not args.use_hardware,
frequency=250)
return env_out
try:
env = DummyVecEnv([make_env])
policy = MlpPolicy
model = PPO2(policy=policy, env=env)
model.load_parameters(args.load)
print("Running trained model")
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
while True:
actions = model.step(obs)[0]
obs[:], reward, done, _ = env.step(actions)
if not args.use_hardware:
env.render()
if done:
print("done")
obs[:] = env.reset()
finally:
env.close()
def test(env_id, seed, policy):
"""
Train PPO2 model for atari environment, for testing purposes
:param env_id: (str) the environment id string
:param seed: (int) Used to seed the random generator.
:param policy: (Object) The policy model to use (MLP, CNN, LSTM, ...)
"""
# if 'lstm' in policy:
# print('LSTM policies not supported for drawing')
# return 1
env = DummyVecEnv([PadEnvRender for _ in range(1)]) # Need for lstm
# else:
# env = PadEnvRender()
env = VecFrameStack(env, 8)
model = PPO2.load('./pad_5combo_ppo2.pkl', env)
while True:
obs, done = env.reset(), False
episode_rew = 0
while not done:
env.render()
action, _ = model.predict(obs)
obs, rew, done, _ = env.step(action)
done = done.any()
episode_rew += rew
time.sleep(1 / 24.)
if done:
print('Episode reward:', rew)
def test_identity_multibinary(model_class):
"""
Test if the algorithm (with a given policy)
can learn an identity transformation (i.e. return observation as an action)
with a multibinary action space
:param model_class: (BaseRLModel) A RL Model
"""
env = DummyVecEnv([lambda: IdentityEnvMultiBinary(10)])
model = model_class("MlpPolicy", env)
model.learn(total_timesteps=1000, seed=0)
n_trials = 1000
reward_sum = 0
obs = env.reset()
for _ in range(n_trials):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
reward_sum += reward
assert model.action_probability(obs).shape == (1, 10), \
"Error: action_probability not returning correct shape"
assert np.prod(model.action_probability(obs, actions=env.action_space.sample()).shape) == 1, \
"Error: not scalar probability"
def run_model(save_name,
nw_type,
log_dir='./Logs/',
log_name=None,
env_name='CartPole-v2',
runs=100,
save_results=False):
# Sets up an environment and a model:
env = DummyVecEnv([lambda: gym.make(env_name)])
model = load_model(nw_type=nw_type,
log_dir=log_dir,
env_name=env_name,
log_name=log_name,
save_name=save_name)
# Runs environment with the loaded model "runs" times
max_reward = 0
max_steps = 0
rew_vec = []
header = 'theta1,alpha1,dtheta1,dalpha1,theta2,alpha2,dtheta2,dalpha2'
for i in range(runs):
# Resets the environment
obs, done = env.reset(), False
episode_rew = 0
ep_steps = 0
obs_vec = obs.reshape(-1, 1)
# This loop runs the environment until a terminal state is reached
while not done:
action, _states = model.predict(obs)
obs, rewards, done, info = env.step(action)
env.render()
episode_rew += rewards[-1]
ep_steps += 1
obs_vec = np.append(obs_vec,
obs.reshape(-1, 1) * 180 / np.pi,
axis=1)
# Saves the reached reward and checks if its a record etc.
rew_vec.append(episode_rew)
print("Ep reward: ", '{0:.2f}'.format(episode_rew), '\tRecord: ',
'{0:.2f}'.format(max_reward), '\tEp steps: ', ep_steps,
'\tSteps record: ', max_steps)
np.savetxt('rew_vec.csv', rew_vec, delimiter=',')
if episode_rew > max_reward:
max_reward = episode_rew
if save_results:
np.savetxt('obs_vec.csv',
obs_vec.T,
delimiter=',',
header=header,
fmt='%1.3f',
comments='')
if ep_steps > max_steps:
max_steps = ep_steps
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 test_vec_env():
"""Test VecNormalize Object"""
def make_env():
return gym.make(ENV_ID)
env = DummyVecEnv([make_env])
env = VecNormalize(env,
norm_obs=True,
norm_reward=True,
clip_obs=10.,
clip_reward=10.)
_, done = env.reset(), [False]
obs = None
while not done[0]:
actions = [env.action_space.sample()]
obs, _, done, _ = env.step(actions)
assert np.max(obs) <= 10
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 test_identity_multidiscrete(model_func):
"""
Test if the algorithm (with a given policy)
can learn an identity transformation (i.e. return observation as an action)
with a multidiscrete action space
:param model_func: (lambda (Gym Environment): BaseRLModel) the model generator
"""
env = DummyVecEnv([lambda: IdentityEnvMultiDiscrete(10)])
model = model_func(env)
model.learn(total_timesteps=1000, seed=0)
n_trials = 1000
reward_sum = 0
obs = env.reset()
for _ in range(n_trials):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
reward_sum += reward
def test_identity_multibinary(model_class):
"""
Test if the algorithm (with a given policy)
can learn an identity transformation (i.e. return observation as an action)
with a multibinary action space
:param model_class: (BaseRLModel) A RL Model
"""
env = DummyVecEnv([lambda: IdentityEnvMultiBinary(10)])
model = model_class("MlpPolicy", env)
model.learn(total_timesteps=1000, seed=0)
n_trials = 1000
reward_sum = 0
obs = env.reset()
for _ in range(n_trials):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
reward_sum += reward
def test_identity(learn_func):
"""
Test if the algorithm (with a given policy)
can learn an identity transformation (i.e. return observation as an action)
:param learn_func: (lambda (Gym Environment): A2CPolicy) the policy generator
"""
env = DummyVecEnv([lambda: IdentityEnv(10)])
model = learn_func(env)
n_trials = 1000
reward_sum = 0
obs = env.reset()
for _ in range(n_trials):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
reward_sum += reward
assert reward_sum > 0.9 * n_trials
# Free memory
del model, env
def test(model):
env = DummyVecEnv([make_env] * n_env)
#env = VecNormalize.load("models/machine_snap_env.bin", venv=env)
#env.training = False
for trial in range(1):
obs = env.reset()
running_reward = 0.0
alpha = 0.01
for _ in range(5000):
action, _states = model.predict(obs)
obs, reward, done, info = env.step(action)
reward = reward[0]
done = done[0]
info = info[0]
#running_reward = running_reward * (1-alpha) + alpha * reward
running_reward += reward
#print(obs, reward, done, info, running_reward)
if done:
print("Finished after {} timesteps".format(_ + 1))
break
else:
env.envs[0].render()
def test_model_manipulation(model_class):
"""
Test if the algorithm can be loaded and saved without any issues, the environment switching
works and that the action prediction works
:param model_class: (BaseRLModel) A model
"""
try:
env = gym.make(ENV_ID)
env = DummyVecEnv([lambda: env])
# create and train
model = model_class(policy=MlpPolicy, env=env)
model.learn(total_timesteps=NUM_TIMESTEPS)
# predict and measure the acc reward
acc_reward = 0
obs = env.reset()
set_global_seeds(0)
for _ in range(N_TRIALS):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
acc_reward += reward
acc_reward = sum(acc_reward) / N_TRIALS
# saving
model.save("./test_model")
del model, env
# loading
model = model_class.load("./test_model")
# changing environment (note: this can be done at loading)
env = gym.make(ENV_ID)
env = DummyVecEnv([lambda: env])
model.set_env(env)
# predict the same output before saving
loaded_acc_reward = 0
obs = env.reset()
set_global_seeds(0)
for _ in range(N_TRIALS):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
loaded_acc_reward += reward
loaded_acc_reward = sum(loaded_acc_reward) / N_TRIALS
# assert <5% diff
assert abs(acc_reward - loaded_acc_reward) / max(acc_reward, loaded_acc_reward) < 0.05, \
"Error: the prediction seems to have changed between loading and saving"
# learn post loading
model.learn(total_timesteps=int(NUM_TIMESTEPS / 2))
# validate no reset post learning
loaded_acc_reward = 0
obs = env.reset()
set_global_seeds(0)
for _ in range(N_TRIALS):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
loaded_acc_reward += reward
loaded_acc_reward = sum(loaded_acc_reward) / N_TRIALS
# assert <5% diff
assert abs(acc_reward - loaded_acc_reward) / max(acc_reward, loaded_acc_reward) < 0.05, \
"Error: the prediction seems to have changed between pre learning and post learning"
# predict new values
obs = env.reset()
for _ in range(N_TRIALS):
action, _ = model.predict(obs)
obs, _, _, _ = env.step(action)
# Free memory
del model, env
finally:
if os.path.exists("./test_model"):
os.remove("./test_model")
def test_model_manipulation(model_policy):
"""
Test if the algorithm (with a given policy) can be loaded and saved without any issues, the environment switching
works and that the action prediction works
:param model_policy: (BaseRLModel, Object) A model, policy pair
"""
model_class, policy = model_policy
try:
env = DummyVecEnv([lambda: IdentityEnv(10)])
# check the env is deterministic
action = [env.action_space.sample()]
set_global_seeds(0)
obs = env.step(action)[0]
for _ in range(N_TRIALS):
set_global_seeds(0)
assert obs == env.step(action)[0], "Error: environment tested not deterministic with the same seed"
# create and train
model = model_class(policy=policy, env=env)
model.learn(total_timesteps=50000)
# predict and measure the acc reward
acc_reward = 0
obs = env.reset()
set_global_seeds(0)
for _ in range(N_TRIALS):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
acc_reward += reward
acc_reward = sum(acc_reward) / N_TRIALS
# saving
model.save("./test_model")
del model, env
# loading
model = model_class.load("./test_model")
# changing environment (note: this can be done at loading)
env = DummyVecEnv([lambda: IdentityEnv(10)])
model.set_env(env)
# predict the same output before saving
loaded_acc_reward = 0
obs = env.reset()
set_global_seeds(0)
for _ in range(N_TRIALS):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
loaded_acc_reward += reward
loaded_acc_reward = sum(loaded_acc_reward) / N_TRIALS
assert abs(acc_reward - loaded_acc_reward) < 0.1, "Error: the prediction seems to have changed between " \
"loading and saving"
# learn post loading
model.learn(total_timesteps=1000)
# validate no reset post learning
loaded_acc_reward = 0
obs = env.reset()
set_global_seeds(0)
for _ in range(N_TRIALS):
action, _ = model.predict(obs)
obs, reward, _, _ = env.step(action)
loaded_acc_reward += reward
loaded_acc_reward = sum(loaded_acc_reward) / N_TRIALS
assert abs(acc_reward - loaded_acc_reward) < 0.1, "Error: the prediction seems to have changed between " \
"pre learning and post learning"
# predict new values
obs = env.reset()
for _ in range(N_TRIALS):
action, _ = model.predict(obs)
obs, _, _, _ = env.step(action)
del model, env
finally:
if os.path.exists("./test_model"):
os.remove("./test_model")
def main():
"""
Runs the test
"""
parser = mujoco_arg_parser()
parser.add_argument(
'--model-path',
default="/cvgl2/u/surajn/workspace/saved_models/sawyerlift_ppo2/model")
parser.add_argument('--images', default=False)
args = parser.parse_args()
logger.configure()
if not args.play:
model, env = train(args.env,
num_timesteps=args.num_timesteps,
seed=args.seed,
model_path=args.model_path,
images=args.images)
if args.play:
def make_env():
env_out = GymWrapper(
suite.make(
"SawyerLift",
use_camera_obs=False, # do not use pixel observations
has_offscreen_renderer=
False, # not needed since not using pixel obs
has_renderer=True, # make sure we can render to the screen
reward_shaping=True, # use dense rewards
control_freq=
10, # control should happen fast enough so that simulation looks smooth
))
env_out.reward_range = None
env_out.metadata = None
env_out.spec = None
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
#env = make_env()
env = DummyVecEnv([make_env])
env = VecNormalize(env)
policy = MlpPolicy
#model = PPO1(MlpPolicy, env, timesteps_per_actorbatch=2048, clip_param=0.2, entcoeff=0.0, optim_epochs=10,
# optim_stepsize=3e-4, optim_batchsize=64, gamma=0.99, lam=0.95, schedule='linear', verbose=1)
model = TRPO(MlpPolicy,
env,
timesteps_per_batch=1024,
max_kl=0.01,
cg_iters=10,
cg_damping=0.1,
entcoeff=0.0,
gamma=0.99,
lam=0.98,
vf_iters=5,
vf_stepsize=1e-3)
model.load(args.model_path)
logger.log("Running trained model")
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
while True:
env.render()
actions = model.step(obs)[0]
obs[:] = env.step(actions)[0]
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 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()
plt.setp(lines, linewidth=1.0, alpha=0.8)
plt.xlabel('update')
plt.ylabel('rewards')
plt.show()
pass
if __name__ == '__main__':
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.simplefilter(action='ignore', category=Warning)
log_path = os.path.join('/', 'home', 'user', 'Dropbox', 'MATLAB_dropbox',
'DeepMimic', 'log')
run_id = 'run_' + '10071436'
run_file = run_id + '_simpleHumanoid.zip'
plot_reward_portions(os.path.join(log_path, run_id, 'reward_portions.txt'))
envs = DummyVecEnv([make_env(1)])
model = PPO2.load(os.path.join(log_path, run_file), envs)
obs = envs.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = envs.step(action)
envs.render()
time.sleep(1 / 30)
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 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()