def do_ppo(args, start_theta, parent_this_run_dir, full_space_save_dir):
"""
Runs the test
"""
logger.log(f"#######CMA and then PPO TRAIN: {args}")
this_conti_ppo_run_dir = get_ppo_part(parent_this_run_dir)
log_dir = get_log_dir(this_conti_ppo_run_dir)
conti_ppo_save_dir = get_save_dir(this_conti_ppo_run_dir)
logger.configure(log_dir)
full_param_traj_dir_path = get_full_params_dir(this_conti_ppo_run_dir)
if os.path.exists(full_param_traj_dir_path):
import shutil
shutil.rmtree(full_param_traj_dir_path)
os.makedirs(full_param_traj_dir_path)
if os.path.exists(conti_ppo_save_dir):
import shutil
shutil.rmtree(conti_ppo_save_dir)
os.makedirs(conti_ppo_save_dir)
def make_env():
env_out = gym.make(args.env)
env_out.env.disableViewer = True
env_out.env.visualize = False
env_out = bench.Monitor(env_out, logger.get_dir(), allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
if args.normalize:
env = VecNormalize(env)
model = PPO2.load(f"{full_space_save_dir}/ppo2")
model.set_from_flat(start_theta)
if args.normalize:
env.load_running_average(full_space_save_dir)
model.set_env(env)
run_info = {"run_num": args.run_num,
"env_id": args.env,
"full_param_traj_dir_path": full_param_traj_dir_path}
# model = PPO2(policy=policy, env=env, n_steps=args.n_steps, nminibatches=args.nminibatches, lam=0.95, gamma=0.99,
# noptepochs=10,
# ent_coef=0.0, learning_rate=3e-4, cliprange=0.2, optimizer=args.optimizer)
model.tell_run_info(run_info)
episode_returns = model.learn(total_timesteps=args.ppo_num_timesteps)
model.save(f"{conti_ppo_save_dir}/ppo2")
env.save_running_average(conti_ppo_save_dir)
return episode_returns, full_param_traj_dir_path
def train(env_id, num_timesteps, seed):
"""
Train PPO2 model for Mujoco environment, for testing purposes
:param env_id: (str) the environment id string
:param num_timesteps: (int) the number of timesteps to run
:param seed: (int) Used to seed the random generator.
"""
def make_env():
env_out = gym.make(env_id)
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
env = VecNormalize(env)
set_global_seeds(seed)
policy = MlpPolicy
model = PPO2(policy=policy,
env=env,
n_steps=2048,
nminibatches=32,
lam=0.95,
gamma=0.99,
noptepochs=10,
ent_coef=0.0,
learning_rate=3e-4,
cliprange=0.2)
model.learn(total_timesteps=num_timesteps)
return model, env
def test_lstm_train():
"""Test that LSTM models are able to achieve >=150 (out of 500) reward on CartPoleNoVelEnv.
This environment requires memory to perform well in."""
def make_env(i):
env = CartPoleNoVelEnv()
env = TimeLimit(env, max_episode_steps=500)
env = bench.Monitor(env, None, allow_early_resets=True)
env.seed(i)
return env
env = SubprocVecEnv([lambda: make_env(i) for i in range(NUM_ENVS)])
env = VecNormalize(env)
model = PPO2(MlpLstmPolicy, env, n_steps=128, nminibatches=NUM_ENVS, lam=0.95, gamma=0.99,
noptepochs=10, ent_coef=0.0, learning_rate=3e-4, cliprange=0.2, verbose=1)
eprewmeans = []
def reward_callback(local, _):
nonlocal eprewmeans
eprewmeans.append(safe_mean([ep_info['r'] for ep_info in local['ep_info_buf']]))
model.learn(total_timesteps=100000, callback=reward_callback)
# Maximum episode reward is 500.
# In CartPole-v1, a non-recurrent policy can easily get >= 450.
# In CartPoleNoVelEnv, a non-recurrent policy doesn't get more than ~50.
# LSTM policies can reach above 400, but it varies a lot between runs; consistently get >=150.
# See PR #244 for more detailed benchmarks.
average_reward = sum(eprewmeans[-NUM_EPISODES_FOR_SCORE:]) / NUM_EPISODES_FOR_SCORE
assert average_reward >= 150, "Mean reward below 150; per-episode rewards {}".format(average_reward)
def _train(env_id, agent, model_params, total_steps, is_evaluation=False):
if is_evaluation: # evaluate_policy() must only take one environment
envs = SubprocVecEnv([make_env(env_id)])
else:
envs = SubprocVecEnv([make_env(env_id) for _ in range(NUM_CPU)])
envs = VecNormalize(
envs) # normalize the envs during training and evaluation
# Load pretrained model during training.
if not is_evaluation and os.path.exists(agent + '_' + env_id):
if agent == 'ppo2':
model = PPO2.load(agent + '_' + env_id)
elif agent == 'a2c':
model = A2C.load(agent + '_' + env_id)
else:
if agent == 'ppo2':
model = PPO2(MlpLstmPolicy,
envs,
nminibatches=1,
verbose=1,
**model_params)
elif agent == 'a2c':
model = A2C(MlpLstmPolicy, envs, verbose=1, **model_params)
model.learn(total_timesteps=total_steps)
return envs, model
def makeEnv(cls, args, env_kwargs=None, load_path_normalise=None):
if "num_population" in args.__dict__:
args.num_cpu = args.num_population * 2
assert not (registered_env[args.env][3] is ThreadingType.NONE and args.num_cpu != 1), \
"Error: cannot have more than 1 CPU for the environment {}".format(args.env)
if env_kwargs is not None and env_kwargs.get("use_srl", False):
srl_model = MultiprocessSRLModel(args.num_cpu, args.env,
env_kwargs)
env_kwargs["state_dim"] = srl_model.state_dim
env_kwargs["srl_pipe"] = srl_model.pipe
envs = [
makeEnv(args.env,
args.seed,
i,
args.log_dir,
allow_early_resets=True,
env_kwargs=env_kwargs) for i in range(args.num_cpu)
]
envs = SubprocVecEnv(envs)
envs = VecFrameStack(envs, args.num_stack)
if args.srl_model != "raw_pixels" and args.algo_type == "v2":
envs = VecNormalize(envs, norm_obs=True, norm_reward=False)
envs = loadRunningAverage(envs,
load_path_normalise=load_path_normalise)
return envs
def train(env_id, num_timesteps, seed, lam, sgd_steps, klcoeff, log):
"""
Train TRPO model for the mujoco environment, for testing purposes
:param env_id: (str) Environment ID
:param num_timesteps: (int) The total number of samples
:param seed: (int) The initial seed for training
"""
with tf_util.single_threaded_session():
rank = MPI.COMM_WORLD.Get_rank()
log_path = './experiments/' + str(
env_id) + './OURS-LOADED/noent_klcoeffanneal_samesgdsteps' + str(
sgd_steps) + '_longer_wgae0.95_exp1_2_' + str(seed)
#log_path = './experiments/'+str(env_id)+'./TRPO-3x/TRPOR-oldsampling/noent_klcoeff'+str(sgd_steps)+'_sgdstep_steps5_'+str(seed)
if not log:
if rank == 0:
logger.configure(log_path)
else:
logger.configure(log_path, format_strs=[])
logger.set_level(logger.DISABLED)
else:
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
logger.set_level(logger.DISABLED)
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
#env = make_mujoco_env(env_id, workerseed)
def make_env():
env_out = gym.make(env_id)
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
env_out.seed(seed)
return env_out
env = DummyVecEnv([make_env])
env = VecNormalize(env) #, norm_reward=False, norm_obs=False)
#env = VecNormalize(env)
model = TRPO(MlpPolicy,
env,
timesteps_per_batch=2048,
max_kl=0.01,
cg_iters=10,
cg_damping=0.1,
entcoeff=0.0,
gamma=0.99,
lam=0.95,
vf_iters=5,
vf_stepsize=1e-3,
verbose=1,
seed=seed,
sgd_steps=sgd_steps,
klcoeff=klcoeff,
method="multistep-SGD")
model.learn(total_timesteps=10e6) #num_timesteps, seed=seed)
env.close()
def main(_):
def make_env():
env_out = gym.make('CartPole-v0')
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
env = VecNormalize(env)
policy = {
'cnn': CnnPolicy,
'lstm': CnnLstmPolicy,
'lnlstm': CnnLnLstmPolicy,
'mlp': MlpPolicy
}[FLAGS.policy]
model = PPO2(policy=policy,
env=env,
n_steps=FLAGS.n_steps,
nminibatches=FLAGS.nminibatches,
lam=FLAGS.lam,
gamma=FLAGS.gamma,
noptepochs=FLAGS.noptepochs,
ent_coef=FLAGS.ent_coef,
learning_rate=FLAGS.learning_rate,
cliprange=FLAGS.cliprange,
verbose=FLAGS.verbose)
model.learn(total_timesteps=FLAGS.num_timesteps)
def train(env_id, num_timesteps, seed, lam, sgd_steps, klcoeff, log):
"""
Train TRPO model for the mujoco environment, for testing purposes
:param env_id: (str) Environment ID
:param num_timesteps: (int) The total number of samples
:param seed: (int) The initial seed for training
"""
with tf_util.single_threaded_session():
rank = MPI.COMM_WORLD.Get_rank()
log_path = './experiments/' + str(
env_id) + './SAC-M/nips_test19/m' + str(sgd_steps) + '_c' + str(
0.5) + '_e' + str(klcoeff) + '_' + str(seed)
#log_path = './experiments/'+str(env_id)+'./TRPO-3x/TRPOR-oldsampling/noent_klcoeff'+str(sgd_steps)+'_sgdstep_steps5_'+str(seed)
if not log:
if rank == 0:
logger.configure(log_path)
else:
logger.configure(log_path, format_strs=[])
logger.set_level(logger.DISABLED)
else:
if rank == 0:
logger.configure()
else:
logger.configure(format_strs=[])
logger.set_level(logger.DISABLED)
workerseed = seed + 10000 * MPI.COMM_WORLD.Get_rank()
#env = make_mujoco_env(env_id, workerseed)
def make_env():
env_out = gym.make(env_id)
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
env_out.seed(seed)
return env_out
env = DummyVecEnv([make_env])
env = VecNormalize(env, norm_reward=False, norm_obs=False)
#env = VecNormalize(env)
model = MDPO(MlpPolicy,
env,
gamma=0.99,
verbose=1,
seed=seed,
buffer_size=1000000,
ent_coef=1.0,
gradient_steps=sgd_steps,
lam=klcoeff,
train_freq=1,
tsallis_q=1,
reparameterize=True,
klconst=0.5)
model.learn(
total_timesteps=int(num_timesteps)) #num_timesteps, seed=seed)
env.close()
def main():
import sys
logger.log(sys.argv)
common_arg_parser = get_common_parser()
args, cma_unknown_args = common_arg_parser.parse_known_args()
this_run_dir = get_dir_path_for_this_run(args)
plot_dir_alg = get_plot_dir(args)
traj_params_dir_name = get_full_params_dir(this_run_dir)
intermediate_data_dir = get_intermediate_data_dir(this_run_dir,
params_scope="pi")
save_dir = get_save_dir(this_run_dir)
if not os.path.exists(intermediate_data_dir):
os.makedirs(intermediate_data_dir)
if not os.path.exists(plot_dir_alg):
os.makedirs(plot_dir_alg)
final_file = get_full_param_traj_file_path(traj_params_dir_name,
"pi_final")
final_params = pd.read_csv(final_file, header=None).values[0]
def make_env():
env_out = gym.make(args.env)
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
if args.normalize:
env = VecNormalize(env)
model = PPO2.load(f"{save_dir}/ppo2") # this also loads V function
model.set_pi_from_flat(final_params)
if args.normalize:
env.load_running_average(save_dir)
obz_tensor = model.act_model.fake_input_tensor
some_neuron = model.act_model.policy_neurons[2][-1]
grads = tf.gradients(tf.math.negative(some_neuron), obz_tensor)
grads = list(zip(grads, obz_tensor))
trainer = tf.train.AdamOptimizer(learning_rate=0.01, epsilon=1e-5)
train_op = trainer.apply_gradients(grads)
for i in range(10000):
obz, _ = model.sess.run([obz_tensor, train_op])
def 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 _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 load_old_ppo2(root_dir, env, env_name, index, transparent_params):
try:
from baselines.ppo2 import ppo2 as ppo2_old
except ImportError as e:
msg = "{}. HINT: you need to install (OpenAI) Baselines to use old_ppo2".format(
e)
raise ImportError(msg)
denv = FakeSingleSpacesVec(env, agent_id=index)
possible_fnames = ["model.pkl", "final_model.pkl"]
model_path = None
for fname in possible_fnames:
candidate_path = os.path.join(root_dir, fname)
if os.path.exists(candidate_path):
model_path = candidate_path
if model_path is None:
raise FileNotFoundError(f"Could not find model at '{root_dir}' "
f"under any filename '{possible_fnames}'")
graph = tf.Graph()
sess = tf.Session(graph=graph)
with sess.as_default():
with graph.as_default():
pylog.info(f"Loading Baselines PPO2 policy from '{model_path}'")
policy = ppo2_old.learn(
network="mlp",
env=denv,
total_timesteps=1,
seed=0,
nminibatches=4,
log_interval=1,
save_interval=1,
load_path=model_path,
)
stable_policy = OpenAIToStablePolicy(policy,
ob_space=denv.observation_space,
ac_space=denv.action_space)
model = PolicyToModel(stable_policy)
try:
normalize_path = os.path.join(root_dir, "normalize.pkl")
with open(normalize_path, "rb") as f:
old_vec_normalize = pickle.load(f)
vec_normalize = VecNormalize(denv, training=False)
vec_normalize.obs_rms = old_vec_normalize.ob_rms
vec_normalize.ret_rms = old_vec_normalize.ret_rms
model = NormalizeModel(model, vec_normalize)
pylog.info(f"Loaded normalization statistics from '{normalize_path}'")
except FileNotFoundError:
# We did not use VecNormalize during training, skip
pass
return model
def f(root_dir, env, env_name, index, transparent_params):
denv = FakeSingleSpacesVec(env, agent_id=index)
pylog.info(
f"Loading Stable Baselines policy for '{cls}' from '{root_dir}'")
model = load_backward_compatible_model(cls, root_dir, denv)
try:
vec_normalize = VecNormalize(denv, training=False)
vec_normalize.load_running_average(root_dir)
model = NormalizeModel(model, vec_normalize)
pylog.info(f"Loaded normalization statistics from '{root_dir}'")
except FileNotFoundError:
# We did not use VecNormalize during training, skip
pass
return model
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_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 single_wrappers(single_venv, scheduler, our_idx, normalize, rew_shape,
rew_shape_params, victim_index, victim_path, victim_type,
debug, env_name, load_policy, lookback_params,
transparent_params, log_callbacks, save_callbacks):
if rew_shape:
rew_shape_venv = apply_reward_wrapper(single_env=single_venv,
scheduler=scheduler,
shaping_params=rew_shape_params,
agent_idx=our_idx)
log_callbacks.append(lambda logger, locals, globals: rew_shape_venv.
log_callback(logger))
single_venv = rew_shape_venv
for anneal_type in ['noise', 'rew_shape']:
if scheduler.is_conditional(anneal_type):
scheduler.set_annealer_get_logs(anneal_type,
rew_shape_venv.get_logs)
if lookback_params['lb_num'] > 0:
lookback_venv = LookbackRewardVecWrapper(single_venv, env_name, debug,
victim_index, victim_path,
victim_type,
transparent_params,
**lookback_params)
single_venv = lookback_venv
if normalize:
normalized_venv = VecNormalize(single_venv)
if load_policy['path'] is not None:
if load_policy['type'] == 'zoo':
raise ValueError(
"Trying to normalize twice. Bansal et al's Zoo agents normalize "
"implicitly. Please set normalize=False to disable VecNormalize."
)
normalized_venv.load_running_average(load_policy['path'])
save_callbacks.append(
lambda root_dir: normalized_venv.save_running_average(root_dir))
single_venv = normalized_venv
return single_venv
def main(_):
p_dic = getattr(conf.dic.path_dic, FLAGS.env_name)
register(id=FLAGS.env_id,
entry_point='env.env_ep:Env',
kwargs={
'env_name': FLAGS.env_name,
'done_step': 8760
})
def make_env():
env_out = gym.make(FLAGS.env_id)
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
env = VecNormalize(env)
policy = {
'cnn': CnnPolicy,
'lstm': CnnLstmPolicy,
'lnlstm': CnnLnLstmPolicy,
'mlp': MlpPolicy
}[FLAGS.policy]
model = PPO2(policy=policy,
env=env,
n_steps=FLAGS.n_steps,
nminibatches=FLAGS.nminibatches,
lam=FLAGS.lam,
gamma=FLAGS.gamma,
noptepochs=FLAGS.noptepochs,
ent_coef=FLAGS.ent_coef,
learning_rate=FLAGS.learning_rate,
cliprange=FLAGS.cliprange,
verbose=FLAGS.verbose,
log_dir=p_dic.get('agent_log_dir'))
model.learn(total_timesteps=FLAGS.num_timesteps)
def single_wrappers(single_venv, scheduler, our_idx, normalize, load_policy,
rew_shape, rew_shape_params, log_callbacks, save_callbacks):
if rew_shape:
rew_shape_venv = apply_reward_wrapper(single_env=single_venv, scheduler=scheduler,
shaping_params=rew_shape_params, agent_idx=our_idx)
log_callbacks.append(lambda logger, locals, globals: rew_shape_venv.log_callback(logger))
single_venv = rew_shape_venv
for anneal_type in ['noise', 'rew_shape']:
if scheduler.is_conditional(anneal_type):
scheduler.set_annealer_get_logs(anneal_type, rew_shape_venv.get_logs)
if normalize:
if load_policy['type'] == 'zoo':
raise ValueError("Trying to normalize twice. Bansal et al's Zoo agents normalize "
"implicitly. Please set normalize=False to disable VecNormalize.")
normalized_venv = VecNormalize(single_venv)
save_callbacks.append(lambda root_dir: normalized_venv.save_running_average(root_dir))
single_venv = normalized_venv
return single_venv
def test_vec_env(tmpdir):
"""Test VecNormalize Object"""
clip_obs = 0.5
clip_reward = 5.0
orig_venv = DummyVecEnv([make_env])
norm_venv = VecNormalize(orig_venv,
norm_obs=True,
norm_reward=True,
clip_obs=clip_obs,
clip_reward=clip_reward)
_, done = norm_venv.reset(), [False]
while not done[0]:
actions = [norm_venv.action_space.sample()]
obs, rew, done, _ = norm_venv.step(actions)
assert np.max(np.abs(obs)) <= clip_obs
assert np.max(np.abs(rew)) <= clip_reward
path = str(tmpdir.join("vec_normalize"))
norm_venv.save(path)
deserialized = VecNormalize.load(path, venv=orig_venv)
check_vec_norm_equal(norm_venv, deserialized)
def _train(env_id, model_params, total_epochs, use_sigmoid_layer=False, is_evaluation=False):
if is_evaluation: # evaluate_policy() must only take one environment
envs = SubprocVecEnv([make_env(env_id)])
else:
envs = SubprocVecEnv([make_env(env_id) for _ in range(NUM_CPU)])
envs = VecNormalize(envs) # normalize the envs during training and evaluation
# activation fn: use tanh for delta hedging and relu for mean reversion
# learning rate: use 1e-7 for delta hedging and 1e-5 for mean reversion
if use_sigmoid_layer:
model = PPO2(SigmoidMlpPolicy, envs, n_steps=1, nminibatches=1,
learning_rate=lambda f: f * 1e-5, verbose=1,
policy_kwargs=dict(act_fun=tf.nn.relu),
**model_params)
else:
model = PPO2(MlpLstmPolicy, envs, n_steps=1, nminibatches=1,
learning_rate=lambda f: f * 1e-5, verbose=1,
policy_kwargs=dict(act_fun=tf.nn.relu),
**model_params)
model.learn(total_timesteps=total_epochs * L)
return envs, model
def train(num_timesteps, model_to_load):
try:
env = DummyVecEnv([dsgym])
env = VecNormalize(env)
policy = MlpPolicy
lr = 3e-4 * 0.75
model = PPO2(policy=policy,
env=env,
n_steps=2048,
nminibatches=32,
lam=0.95,
gamma=0.99,
noptepochs=10,
ent_coef=0.01,
learning_rate=linear_schedule(lr),
cliprange=0.2)
if model_to_load:
env = DummyVecEnv([dsgym])
env = VecNormalize.load(
model_to_load.replace(".zip", "vec_normalize.pkl"), env)
model = model.load(model_to_load)
model.set_env(env)
print("Loaded model from: ", model_to_load)
model.set_learning_rate_func(linear_schedule_start_zero(lr))
model.learn(total_timesteps=num_timesteps)
except KeyboardInterrupt:
print("Saving on keyinterrupt")
model.save("D:/openAi/ppo2save/" + time.strftime("%Y_%m_%d-%H_%M_%S"))
# quit
sys.exit()
except BaseException as error:
model.save("D:/openAi/ppo2save/" + time.strftime("%Y_%m_%d-%H_%M_%S"))
print('An exception occurred: {}'.format(error))
traceback.print_exception(*sys.exc_info())
sys.exit()
model.save("D:/openAi/ppo2save/" + time.strftime("%Y_%m_%d-%H_%M_%S"))
def run_experiment_with_trained(augment_num_timesteps, linear_co_threshold, 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, lagrangian_inds_to_include=None,
neurons_inds_to_include=None, use_lagrangian=True):
trained_model = None
if not use_lagrangian:
with tf.variable_scope("trained_model"):
common_arg_parser = get_common_parser()
trained_args, cma_unknown_args = common_arg_parser.parse_known_args()
trained_args.env = policy_env
trained_args.seed = policy_seed
trained_args.num_timesteps = policy_num_timesteps
trained_args.run_num = policy_run_num
trained_this_run_dir = get_dir_path_for_this_run(trained_args)
trained_traj_params_dir_name = get_full_params_dir(trained_this_run_dir)
trained_save_dir = get_save_dir(trained_this_run_dir)
trained_final_file = get_full_param_traj_file_path(trained_traj_params_dir_name, "pi_final")
trained_final_params = pd.read_csv(trained_final_file, header=None).values[0]
trained_model = PPO2.load(f"{trained_save_dir}/ppo2", seed=augment_seed)
trained_model.set_pi_from_flat(trained_final_params)
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{linear_co_threshold.start}_{linear_co_threshold.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=linear_co_threshold,
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)
linear_top_vars_list_wanted_to_print = []
if (use_lagrangian and lagrangian_inds_to_include is None) or (not use_lagrangian and neurons_inds_to_include is None):
# note this is only linear
if linear_top_vars_list is None or linear_correlation_neuron_list is None:
linear_top_vars_list, linear_correlation_neuron_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, neurons_inds_to_include, linear_top_vars_list_wanted_to_print = \
get_wanted_lagrangians_and_neurons(keys_to_include, linear_top_vars_list, linear_correlation_neuron_list, linear_co_threshold)
with open(f"{log_dir}/lagrangian_inds_to_include.json", 'w') as fp:
json.dump(lagrangian_inds_to_include, fp)
with open(f"{log_dir}/linear_top_vars_list_wanted_to_print.json", 'w') as fp:
json.dump(linear_top_vars_list_wanted_to_print, fp)
with open(f"{log_dir}/neurons_inds_to_include.json", 'w') as fp:
json.dump(neurons_inds_to_include, fp)
args.env = f'{experiment_label}_{entry_point}-v1'
if not use_lagrangian:
register(
id=args.env,
entry_point=entry_point,
max_episode_steps=1000,
kwargs={"lagrangian_inds_to_include": None, "trained_model": trained_model,
"neurons_inds_to_include": neurons_inds_to_include}
)
else:
register(
id=args.env,
entry_point=entry_point,
max_episode_steps=1000,
kwargs={"lagrangian_inds_to_include": lagrangian_inds_to_include, "trained_model": None,
"neurons_inds_to_include": None}
)
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, linear_co_threshold, log_dir)
# 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
def single_wrappers(
single_venv,
scheduler,
our_idx,
normalize,
normalize_observations,
rew_shape,
rew_shape_params,
embed_index,
embed_paths,
embed_types,
debug,
env_name,
load_policy,
lookback_params,
transparent_params,
log_callbacks,
save_callbacks,
):
if rew_shape:
rew_shape_venv = apply_reward_wrapper(
single_env=single_venv,
scheduler=scheduler,
shaping_params=rew_shape_params,
agent_idx=our_idx,
)
log_callbacks.append(LoggerOnlyLogCallback(rew_shape_venv))
single_venv = rew_shape_venv
for anneal_type in ["noise", "rew_shape"]:
if scheduler.is_conditional(anneal_type):
scheduler.set_annealer_get_logs(anneal_type,
rew_shape_venv.get_logs)
if lookback_params["lb_num"] > 0:
if len(embed_types) > 1:
raise ValueError(
"Lookback is not supported with multiple embedded agents")
embed_path = embed_paths[0]
embed_type = embed_types[0]
lookback_venv = LookbackRewardVecWrapper(
single_venv,
env_name,
debug,
embed_index,
embed_path,
embed_type,
transparent_params,
**lookback_params,
)
single_venv = lookback_venv
if normalize:
if normalize_observations:
if load_policy["path"] is not None:
if load_policy["type"] == "zoo":
raise ValueError(
"Trying to normalize twice. Bansal et al's Zoo agents normalize "
"implicitly. Please set normalize=False to disable VecNormalize."
)
normalized_venv = VecNormalize(single_venv)
else:
normalized_venv = VecNormalize(single_venv, norm_obs=False)
if load_policy["path"] is not None and load_policy["type"] != "zoo":
normalized_venv.load_running_average(load_policy["path"])
save_callbacks.append(lambda root_dir: normalized_venv.save(
os.path.join(root_dir, "vec_normalize.pkl")))
single_venv = normalized_venv
return single_venv
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_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 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()
:param seed: (int) the inital seed for RNG
:param rank: (int) index of the subprocess
"""
def _init():
env = gym.make(env_id)
env.seed(seed + rank)
env = bench.Monitor(env, logger.get_dir(), allow_early_resets=True)
return env
set_global_seeds(seed)
return _init
if __name__ == '__main__':
log_str = sys.argv[1]
env_id = 'ROAMHandGraspCube-v1'
model = PPO2.load("logs/{}/trained_model".format(log_str))
# render trained agent
env = VecNormalize(DummyVecEnv([lambda: gym.make(env_id)]),
norm_reward=False)
env.load_running_average("logs/{}".format(log_str))
obs = env.reset()
while True:
action, _states = model.predict(obs, deterministic=True)
obs, rewards, dones, info = env.step(action)
env.render()
def main(_):
p_dic = getattr(conf.dic.path_dic, FLAGS.env_name)
register(id=FLAGS.env_id,
entry_point='env.env_ep:Env',
kwargs={
'env_name': FLAGS.env_name,
'done_step': 8760
})
def make_env():
env_out = gym.make(FLAGS.env_id)
env_out = bench.Monitor(env_out,
logger.get_dir(),
allow_early_resets=True)
return env_out
env = DummyVecEnv([make_env])
env = VecNormalize(env)
policy = {
'cnn': CnnPolicy,
'lstm': CnnLstmPolicy,
'lnlstm': CnnLnLstmPolicy,
'mlp': MlpPolicy
}[FLAGS.policy]
model = PPO2(policy=policy,
env=env,
n_steps=FLAGS.n_steps,
nminibatches=FLAGS.nminibatches,
lam=FLAGS.lam,
gamma=FLAGS.gamma,
noptepochs=FLAGS.noptepochs,
ent_coef=FLAGS.ent_coef,
learning_rate=FLAGS.learning_rate,
cliprange=FLAGS.cliprange,
verbose=FLAGS.verbose)
with model.graph.as_default():
tf_util.load_state(fname=tf.train.latest_checkpoint(
p_dic.get('agent_log_dir')),
sess=model.sess)
epenv = EnergyPlusEnv(
energyplus_file="/usr/local/EnergyPlus-8-8-0/energyplus",
model_file=p_dic.get('idf_path'),
weather_file=
"/usr/local/EnergyPlus-8-8-0/WeatherData/USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw",
log_dir=p_dic.get('eplog_dir'))
os.environ['ENERGYPLUS'] = "/usr/local/EnergyPlus-8-8-0/energyplus"
os.environ['ENERGYPLUS_MODEL'] = p_dic.get('idf_path')
os.environ[
'ENERGYPLUS_WEATHER'] = "/usr/local/EnergyPlus-8-8-0/WeatherData/USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw"
os.environ['ENERGYPLUS_LOG'] = p_dic.get('eplog_dir')
epenv.start_instance()
def signal_handler(signal, frame):
epenv.stop_instance
print('=====Energy plus terminated=====')
print('==========Pipe closed==========')
sys.exit()
signal.signal(signal.SIGINT, signal_handler)
state = epenv.reset()
env = Env('ep', 8760)
for i in range(10000000000000):
# state = np.array([[state[0], state[1], state[2], state[3], state[4], state[5], state[6], state[7], state[8]]])
state = np.array(
[[state[0], state[1], state[2], state[3], state[4], state[5]]])
action, _, _, _ = model.step(state)
action = env.set_action(action)
action = action.reshape([-1])
state, done = epenv.step(action)
epenv.stop_instance()
env_path = args.surrogate_model
mimic_model_path = args.mimic_model_path
env = gym.make(env_name)
venv = SubprocVecEnv([
lambda: make_adv_multi2single_env(env_name, adv_agent_path,
adv_agent_norm_path, False)
for i in range(n_cpu)
])
venv = Monitor(venv, 0)
rew_shape_venv = apply_reward_wrapper(single_env=venv,
scheduler=scheduler,
agent_idx=0,
shaping_params=rew_shape_params)
venv = VecNormalize(rew_shape_venv, norm_obs=False)
# makedir output
out_dir, logger = setup_logger(args.root_dir, args.exp_name)
model = MyPPO2(MlpPolicy,
venv,
ent_coef=ent_coef,
nminibatches=nminibatches,
noptepochs=noptepochs,
learning_rate=learning_rate,
verbose=1,
n_steps=n_steps,
gamma=gamma,
tensorboard_log=out_dir,
model_saved_loc=out_dir,
env_name=env_name,
env_path=env_path,