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ppo.py
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ppo.py
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from collections import deque
import time
import tensorflow as tf
import numpy as np
from mpi4py import MPI
from stable_baselines.common import Dataset, explained_variance, fmt_row, zipsame, ActorCriticRLModel, SetVerbosity, \
TensorboardWriter
# from stable_baselines import logger
from baselines import logger
import stable_baselines.common.tf_util as tf_util
from stable_baselines.common.policies import LstmPolicy, ActorCriticPolicy
from stable_baselines.common.mpi_adam import MpiAdam
from stable_baselines.common.mpi_moments import mpi_moments
from stable_baselines.trpo_mpi.utils import traj_segment_generator, flatten_lists
from stable_baselines.a2c.utils import total_episode_reward_logger
import copy
def add_vtarg_and_adv(seg, gamma, lam):
"""
Compute target value using TD(lambda) estimator, and advantage with GAE(lambda)
:param seg: (dict) the current segment of the trajectory (see traj_segment_generator return for more information)
:param gamma: (float) Discount factor
:param lam: (float) GAE factor
"""
# last element is only used for last vtarg, but we already zeroed it if last new = 1
new = seg["dones"]
vpred = np.append(seg["vpred"], seg["nextvpred"])
rew_len = len(seg["rew"])
seg["adv"] = gaelam = np.empty(rew_len, 'float32')
rew = seg["rew"]
lastgaelam = 0
for step in reversed(range(rew_len)):
nonterminal = 1 - new[step]
delta = rew[step] + gamma * vpred[step + 1] * nonterminal - vpred[step]
gaelam[step] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam
seg["tdlamret"] = seg["adv"] + seg["vpred"]
class PPO1(ActorCriticRLModel):
"""
Proximal Policy Optimization algorithm (MPI version).
Paper: https://arxiv.org/abs/1707.06347
:param env: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
:param policy: (ActorCriticPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, CnnLstmPolicy, ...)
:param timesteps_per_actorbatch: (int) timesteps per actor per update
:param clip_param: (float) clipping parameter epsilon
:param entcoeff: (float) the entropy loss weight
:param optim_epochs: (float) the optimizer's number of epochs
:param optim_stepsize: (float) the optimizer's stepsize
:param optim_batchsize: (int) the optimizer's the batch size
:param gamma: (float) discount factor
:param lam: (float) advantage estimation
:param adam_epsilon: (float) the epsilon value for the adam optimizer
:param schedule: (str) The type of scheduler for the learning rate update ('linear', 'constant',
'double_linear_con', 'middle_drop' or 'double_middle_drop')
:param verbose: (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
:param tensorboard_log: (str) the log location for tensorboard (if None, no logging)
:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
:param wd: (float) weight decay coefficient
"""
def __init__(self, policy, env, gamma=0.99, timesteps_per_actorbatch=256, clip_param=0.2, entcoeff=0.01,
optim_epochs=4, optim_stepsize=1e-3, optim_batchsize=64, lam=0.95, adam_epsilon=1e-5,
schedule='linear', verbose=0, tensorboard_log=None, _init_setup_model=True, wd=0.0):
super().__init__(policy=policy, env=env, verbose=verbose, requires_vec_env=False,
_init_setup_model=_init_setup_model)
self.gamma = gamma
self.timesteps_per_actorbatch = timesteps_per_actorbatch
self.clip_param = clip_param
self.entcoeff = entcoeff
self.optim_epochs = optim_epochs
self.optim_stepsize = optim_stepsize
self.optim_batchsize = optim_batchsize
self.lam = lam
self.adam_epsilon = adam_epsilon
self.schedule = schedule
self.tensorboard_log = tensorboard_log
self.wd = wd
self.graph = None
self.sess = None
self.policy_pi = None
self.loss_names = None
self.lossandgrad = None
self.adam = None
self.assign_old_eq_new = None
self.compute_losses = None
self.params = None
self.step = None
self.proba_step = None
self.initial_state = None
self.summary = None
self.episode_reward = None
if _init_setup_model:
self.setup_model()
def setup_model(self):
with SetVerbosity(self.verbose):
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.single_threaded_session(graph=self.graph)
# Construct network for new policy
self.policy_pi = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
None, reuse=False)
# Network for old policy
with tf.variable_scope("oldpi", reuse=False):
old_pi = self.policy(self.sess, self.observation_space, self.action_space, self.n_envs, 1,
None, reuse=False)
with tf.variable_scope("loss", reuse=False):
# Target advantage function (if applicable)
atarg = tf.placeholder(dtype=tf.float32, shape=[None])
# Empirical return
ret = tf.placeholder(dtype=tf.float32, shape=[None])
# learning rate multiplier, updated with schedule
lrmult = tf.placeholder(name='lrmult', dtype=tf.float32, shape=[])
# Annealed cliping parameter epislon
clip_param = self.clip_param * lrmult
obs_ph = self.policy_pi.obs_ph
action_ph = self.policy_pi.pdtype.sample_placeholder([None])
kloldnew = old_pi.proba_distribution.kl(self.policy_pi.proba_distribution)
ent = self.policy_pi.proba_distribution.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
pol_entpen = (-self.entcoeff) * meanent
# pnew / pold
ratio = tf.exp(self.policy_pi.proba_distribution.logp(action_ph) -
old_pi.proba_distribution.logp(action_ph))
# surrogate from conservative policy iteration
surr1 = ratio * atarg
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param, 1.0 + clip_param) * atarg
# PPO's pessimistic surrogate (L^CLIP)
pol_surr = - tf.reduce_mean(tf.minimum(surr1, surr2))
vf_loss = tf.reduce_mean(tf.square(self.policy_pi.value_fn[:, 0] - ret))
# weight decay
vars = tf_util.get_trainable_vars("model") # tf.trainable_variables()
wd_loss = tf.add_n([ tf.nn.l2_loss(v) for v in vars if 'bias' not in v.name ]) * self.wd
total_loss = pol_surr + pol_entpen + vf_loss + wd_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent, wd_loss]
self.loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent", "wd"]
tf.summary.scalar('entropy_loss', pol_entpen)
tf.summary.scalar('policy_gradient_loss', pol_surr)
tf.summary.scalar('value_function_loss', vf_loss)
tf.summary.scalar('approximate_kullback-leiber', meankl)
tf.summary.scalar('clip_factor', clip_param)
tf.summary.scalar('loss', total_loss)
self.params = tf_util.get_trainable_vars("model")
self.assign_old_eq_new = tf_util.function(
[], [], updates=[tf.assign(oldv, newv) for (oldv, newv) in
zipsame(tf_util.get_globals_vars("oldpi"), tf_util.get_globals_vars("model"))])
with tf.variable_scope("Adam_mpi", reuse=False):
self.adam = MpiAdam(self.params, epsilon=self.adam_epsilon, sess=self.sess)
with tf.variable_scope("input_info", reuse=False):
tf.summary.scalar('discounted_rewards', tf.reduce_mean(ret))
tf.summary.histogram('discounted_rewards', ret)
tf.summary.scalar('learning_rate', tf.reduce_mean(self.optim_stepsize))
tf.summary.histogram('learning_rate', self.optim_stepsize)
tf.summary.scalar('advantage', tf.reduce_mean(atarg))
tf.summary.histogram('advantage', atarg)
tf.summary.scalar('clip_range', tf.reduce_mean(self.clip_param))
tf.summary.histogram('clip_range', self.clip_param)
if len(self.observation_space.shape) == 3:
tf.summary.image('observation', obs_ph)
else:
tf.summary.histogram('observation', obs_ph)
self.step = self.policy_pi.step
self.proba_step = self.policy_pi.proba_step
self.initial_state = self.policy_pi.initial_state
tf_util.initialize(sess=self.sess)
self.summary = tf.summary.merge_all()
self.lossandgrad = tf_util.function([obs_ph, old_pi.obs_ph, action_ph, atarg, ret, lrmult],
[self.summary, tf_util.flatgrad(total_loss, self.params)] + losses)
self.compute_losses = tf_util.function([obs_ph, old_pi.obs_ph, action_ph, atarg, ret, lrmult],
losses)
def learn(self, total_timesteps, callback=None, seed=None, log_interval=100, tb_log_name="PPO1"):
with SetVerbosity(self.verbose), TensorboardWriter(self.graph, self.tensorboard_log, tb_log_name) as writer:
self._setup_learn(seed)
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the PPO1 model must be " \
"an instance of common.policies.ActorCriticPolicy."
with self.sess.as_default():
self.adam.sync()
# Prepare for rollouts
seg_gen = traj_segment_generator(self.policy_pi, self.env, self.timesteps_per_actorbatch)
# seg_gen = filtered_traj_segment_generator(self.policy_pi, self.env, self.timesteps_per_actorbatch, imbalance_limit = self.timesteps_per_actorbatch // 100, waste_limit=self.timesteps_per_actorbatch*10)
# seg_gen = balanced_traj_segment_generator(self.policy_pi, self.env, self.timesteps_per_actorbatch, waste_limit=self.timesteps_per_actorbatch*10)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
t_start = time.time()
# rolling buffer for episode lengths
lenbuffer = deque(maxlen=100)
# rolling buffer for episode rewards
rewbuffer = deque(maxlen=100)
self.episode_reward = np.zeros((self.n_envs,))
while True:
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) == False:
break
if total_timesteps and timesteps_so_far >= total_timesteps:
break
if self.schedule == 'constant':
cur_lrmult = 1.0
elif self.schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / total_timesteps, 0)
else:
raise NotImplementedError
logger.log("********** Iteration %i ************" % iters_so_far)
# logger.record_tabular("update_no", iters_so_far)
logger.logkv("update_no", iters_so_far)
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, self.gamma, self.lam)
# seg = balanced_sample(seg_gen, self.timesteps_per_actorbatch, self.gamma, self.lam, waste_limit=self.timesteps_per_actorbatch*10)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
obs_ph, action_ph, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg["tdlamret"]
# true_rew is the reward without discount
if writer is not None:
self.episode_reward = total_episode_reward_logger(self.episode_reward,
seg["true_rew"].reshape((self.n_envs, -1)),
seg["dones"].reshape((self.n_envs, -1)),
writer, timesteps_so_far)
# predicted value function before udpate
vpredbefore = seg["vpred"]
# standardized advantage function estimate
atarg = (atarg - atarg.mean()) / atarg.std()
dataset = Dataset(dict(ob=obs_ph, ac=action_ph, atarg=atarg, vtarg=tdlamret),
shuffle=not issubclass(self.policy, LstmPolicy))
optim_batchsize = self.optim_batchsize or obs_ph.shape[0]
# set old parameter values to new parameter values
self.assign_old_eq_new(sess=self.sess)
logger.log("Optimizing...")
logger.log(fmt_row(13, self.loss_names))
# Here we do a bunch of optimization epochs over the data
for k in range(self.optim_epochs):
# list of tuples, each of which gives the loss for a minibatch
losses = []
for i, batch in enumerate(dataset.iterate_once(optim_batchsize)):
steps = (timesteps_so_far +
k * optim_batchsize +
int(i * (optim_batchsize / len(dataset.data_map))))
if writer is not None:
# run loss backprop with summary, but once every 10 runs save the metadata
# (memory, compute time, ...)
if (1 + k) % 10 == 0:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
summary, grad, *newlosses = self.lossandgrad(batch["ob"], batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult, sess=self.sess,
options=run_options,
run_metadata=run_metadata)
writer.add_run_metadata(run_metadata, 'step%d' % steps)
else:
summary, grad, *newlosses = self.lossandgrad(batch["ob"], batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult, sess=self.sess)
writer.add_summary(summary, steps)
else:
_, grad, *newlosses = self.lossandgrad(batch["ob"], batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"], cur_lrmult,
sess=self.sess)
self.adam.update(grad, self.optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in dataset.iterate_once(optim_batchsize):
newlosses = self.compute_losses(batch["ob"], batch["ob"], batch["ac"], batch["atarg"],
batch["vtarg"], cur_lrmult, sess=self.sess)
losses.append(newlosses)
mean_losses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, mean_losses))
for (loss_val, name) in zipsame(mean_losses, self.loss_names):
logger.record_tabular("loss_" + name, loss_val)
logger.record_tabular("ev_tdlam_before", explained_variance(vpredbefore, tdlamret))
# local values
lrlocal = (seg["ep_lens"], seg["ep_rets"])
# list of tuples
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal)
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += MPI.COMM_WORLD.allreduce(seg["total_timestep"])
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - t_start)
if self.verbose >= 1 and MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
return self
def save(self, save_path):
data = {
"gamma": self.gamma,
"timesteps_per_actorbatch": self.timesteps_per_actorbatch,
"clip_param": self.clip_param,
"entcoeff": self.entcoeff,
"optim_epochs": self.optim_epochs,
"optim_stepsize": self.optim_stepsize,
"optim_batchsize": self.optim_batchsize,
"lam": self.lam,
"adam_epsilon": self.adam_epsilon,
"schedule": self.schedule,
"verbose": self.verbose,
"policy": self.policy,
"observation_space": self.observation_space,
"action_space": self.action_space,
"n_envs": self.n_envs,
"_vectorize_action": self._vectorize_action
}
params = self.sess.run(self.params)
self._save_to_file(save_path, data=data, params=params)
# Returns states where the imbalance between positive and negative rewards is at most imbalance_limit
# if we have discarded more than waste_limit, then we stop caring about imbalance
def filtered_traj_segment_generator(policy, env, horizon, imbalance_limit=100, waste_limit=10000):
observation = env.reset()
action = env.action_space.sample() # not used, just so we have the datatype
# real rollout generator
seg_gen = traj_segment_generator(policy, env, horizon)
while True:
# Initialize history arrays
observations = np.array([observation for _ in range(horizon)])
true_rews = np.zeros(horizon, 'float32')
rews = np.zeros(horizon, 'float32')
vpreds = np.zeros(horizon, 'float32')
dones = np.zeros(horizon, 'int32')
actions = np.array([action for _ in range(horizon)])
prev_actions = actions.copy()
ep_rets = []
ep_lens = []
ep_true_rets = []
total_timesteps = 0
nextvpreds = 0
positive = 0
negative = 0
counter = 0
wasted = 0
while counter < horizon:
result = next(seg_gen)
total_timesteps += result["total_timestep"]
nextvpreds = result["nextvpred"]
ep_rets += result["ep_rets"]
ep_lens += result["ep_lens"]
ep_true_rets + result["ep_true_rets"]
for i in range(horizon):
isPositive = result["rew"][i] > 0
if (wasted >= waste_limit) or\
(isPositive and positive - negative <= imbalance_limit) or \
(not isPositive and negative - positive <= imbalance_limit):
if isPositive:
positive +=1
else:
negative +=1
observations[counter] = result["ob"][i]
true_rews[counter] = result["true_rew"][i]
rews[counter] = result["rew"][i]
vpreds[counter] = result["vpred"][i]
dones[counter] = result["dones"][i]
actions[counter] = result["ac"][i]
prev_actions[counter] = result["prevac"][i]
counter +=1
if counter == horizon:
break
else:
wasted += 1
print("AAActor episode: {} positive, {} negative, {} wasted".format(positive, negative, wasted))
logger.logkv("actor_positive", positive)
logger.logkv("actor_negative", negative)
logger.logkv("actor_", wasted)
yield {"ob": observations, "rew": rews, "dones": dones, "true_rew": true_rews, "vpred": vpreds,
"ac": actions, "prevac": prev_actions, "nextvpred": nextvpreds, "ep_rets": ep_rets,
"ep_lens": ep_lens, "ep_true_rets": ep_true_rets, "total_timestep": total_timesteps}
# Returns totally balanced (pos-neg) dataset using oversampling
def balanced_traj_segment_generator(policy, env, horizon, waste_limit=10000):
assert horizon % 2 == 0, "Horizon {} should be divisible by two".format(horizon)
half_size = horizon // 2
observation = env.reset()
action = env.action_space.sample() # not used, just so we have the datatype
# real rollout generator
seg_gen = traj_segment_generator(policy, env, horizon)
while True:
# Initialize history arrays
pos_observations = np.array([observation for _ in range(horizon)])
pos_true_rews = np.zeros(horizon, 'float32')
pos_rews = np.zeros(horizon, 'float32')
pos_vpreds = np.zeros(horizon, 'float32')
pos_dones = np.zeros(horizon, 'int32')
pos_actions = np.array([action for _ in range(horizon)])
pos_prev_actions = pos_actions.copy()
neg_observations = np.array([observation for _ in range(horizon)])
neg_true_rews = np.zeros(horizon, 'float32')
neg_rews = np.zeros(horizon, 'float32')
neg_vpreds = np.zeros(horizon, 'float32')
neg_dones = np.zeros(horizon, 'int32')
neg_actions = np.array([action for _ in range(horizon)])
neg_prev_actions = neg_actions.copy()
ep_rets = []
ep_lens = []
ep_true_rets = []
total_timesteps = 0
nextvpreds = 0
positive = 0
negative = 0
wasted = 0
true_pos = 0
true_neg = 0
while positive + negative < horizon:
result = next(seg_gen)
total_timesteps += result["total_timestep"]
nextvpreds = result["nextvpred"]
ep_rets += result["ep_rets"]
ep_lens += result["ep_lens"]
ep_true_rets + result["ep_true_rets"]
for i in range(horizon):
# assert wasted < waste_limit, "Too much wasted data"
isPositive = result["rew"][i] > 0
if isPositive:
if positive < half_size: # positive sample and there is still space for it
pos_observations[positive] = result["ob"][i]
pos_true_rews[positive] = result["true_rew"][i]
pos_rews[positive] = result["rew"][i]
pos_vpreds[positive] = result["vpred"][i]
pos_dones[positive] = result["dones"][i]
pos_actions[positive] = result["ac"][i]
pos_prev_actions[positive] = result["prevac"][i]
true_pos +=1
positive +=1
elif negative > 0: # no more place for positive, and we already have some negative to sample from
copy_index = np.random.choice(negative)
neg_observations[negative] = neg_observations[copy_index]
neg_true_rews[negative] = neg_true_rews[copy_index]
neg_rews[negative] = neg_rews[copy_index]
neg_vpreds[negative] = neg_vpreds[copy_index]
neg_dones[negative] = neg_dones[copy_index]
neg_actions[negative] = neg_actions[copy_index]
neg_prev_actions[negative] = neg_prev_actions[copy_index]
if neg_rews[copy_index] > 0:
true_pos += 1
else:
true_neg += 1
negative +=1
wasted +=1
elif wasted > waste_limit: # no more tolerance, so we just pretend the sample was negative
neg_observations[negative] = result["ob"][i]
neg_true_rews[negative] = result["true_rew"][i]
neg_rews[negative] = result["rew"][i]
neg_vpreds[negative] = result["vpred"][i]
neg_dones[negative] = result["dones"][i]
neg_actions[negative] = result["ac"][i]
neg_prev_actions[negative] = result["prevac"][i]
true_pos +=1
negative +=1
else:
wasted +=1
else:
if negative < half_size: # negative sample and there is still space for it
neg_observations[negative] = result["ob"][i]
neg_true_rews[negative] = result["true_rew"][i]
neg_rews[negative] = result["rew"][i]
neg_vpreds[negative] = result["vpred"][i]
neg_dones[negative] = result["dones"][i]
neg_actions[negative] = result["ac"][i]
neg_prev_actions[negative] = result["prevac"][i]
true_neg +=1
negative +=1
elif positive > 0: # no more place for negative, and we already have some positive to sample from
copy_index = np.random.choice(positive)
pos_observations[negative] = pos_observations[copy_index]
pos_true_rews[negative] = pos_true_rews[copy_index]
pos_rews[negative] = pos_rews[copy_index]
pos_vpreds[negative] = pos_vpreds[copy_index]
pos_dones[negative] = pos_dones[copy_index]
pos_actions[negative] = pos_actions[copy_index]
pos_prev_actions[negative] = pos_prev_actions[copy_index]
if pos_rews[copy_index] > 0:
true_pos += 1
else:
true_neg += 1
positive +=1
wasted +=1
elif wasted > waste_limit: # no more tolerance, so we just pretend the sample was positive
pos_observations[positive] = result["ob"][i]
pos_true_rews[positive] = result["true_rew"][i]
pos_rews[positive] = result["rew"][i]
pos_vpreds[positive] = result["vpred"][i]
pos_dones[positive] = result["dones"][i]
pos_actions[positive] = result["ac"][i]
pos_prev_actions[positive] = result["prevac"][i]
true_neg +=1
positive +=1
else:
wasted +=1
if negative + positive == horizon:
break
print("AAActor episode: {} positive, {} negative, {} wasted".format(true_pos, true_neg, wasted))
observations = np.concatenate((pos_observations, neg_observations))
rews = np.concatenate((pos_rews, neg_rews))
dones = np.concatenate((pos_dones, neg_dones))
true_rews = np.concatenate((pos_true_rews, neg_true_rews))
vpreds = np.concatenate((pos_vpreds, neg_vpreds))
actions = np.concatenate((pos_actions, neg_actions))
prev_actions = np.concatenate((pos_prev_actions, neg_prev_actions))
yield {"ob": observations, "rew": rews, "dones": dones, "true_rew": true_rews, "vpred": vpreds,
"ac": actions, "prevac": prev_actions, "nextvpred": nextvpreds, "ep_rets": ep_rets,
"ep_lens": ep_lens, "ep_true_rets": ep_true_rets, "total_timestep": total_timesteps}
def balanced_sample(seg_gen, horizon, gamma, lam, waste_limit=10000):
assert horizon % 2 == 0, "Horizon {} should be divisible by two".format(horizon)
half_size = horizon // 2
observations = []
dones = []
true_rews = []
vpreds = []
actions = []
advs = []
tdlamrets = []
ep_rets = []
ep_lens = []
total_timesteps = 0
pos = 0
neg = 0
wasted = 0
while pos + neg < horizon:
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
total_timesteps += seg["total_timestep"]
ep_rets += seg["ep_rets"]
ep_lens += seg["ep_lens"]
for i in range(horizon):
if pos + neg == horizon:
break
isPositive = seg["tdlamret"][i] > 0
if isPositive and (pos < half_size or wasted >= waste_limit):
pos += 1
add = True
elif not isPositive and (neg < half_size or wasted >= waste_limit):
neg += 1
add = True
else:
wasted += 1
add = False
if add:
observations.append(copy.deepcopy(seg["ob"][i]))
dones.append(copy.deepcopy(seg["dones"][i]))
true_rews.append(copy.deepcopy(seg["true_rew"][i]))
vpreds.append(copy.deepcopy(seg["vpred"][i]))
actions.append(copy.deepcopy(seg["ac"][i]))
advs.append(copy.deepcopy(seg["adv"][i]))
tdlamrets.append(copy.deepcopy(seg["tdlamret"][i]))
observations = np.array(observations)
dones = np.array(dones)
true_rews = np.array(true_rews)
vpreds = np.array(vpreds)
actions = np.array(actions)
advs = np.array(advs)
tdlamrets = np.array(tdlamrets)
print("AAActor episode: {} positive, {} negative, {} wasted".format(pos, neg, wasted))
return {
"ob": observations,
"dones": dones,
"true_rew": true_rews,
"vpred": vpreds,
"ac": actions,
"ep_rets": ep_rets,
"ep_lens": ep_lens,
"total_timestep": total_timesteps,
"adv": advs,
"tdlamret": tdlamrets
}