def evaluate(step, episode_number):
global max_eval_reward_mean, model_saved
print('Evaluate...')
eval_reward_sum = 0.0
# Run evaluation episodes
for eval_episode in range(n_eval_episodes):
obs = env.reset()
done = False
while not done:
# Choose action
action_idxes = np.array(
act(np.array(obs)[None],
stochastic=False)) # deterministic
actions_greedy = action_idxes / num_action_grains * actions_range + low
if eval_std == 0.0:
action = actions_greedy
else:
action = []
for index in range(len(actions_greedy)):
a_greedy = actions_greedy[index]
out_of_range_action = True
while out_of_range_action:
a_stoch = np.random.normal(loc=a_greedy,
scale=eval_std)
a_idx_stoch = np.rint(
(a_stoch + high[index]) /
actions_range[index] * num_action_grains)
if a_idx_stoch >= 0 and a_idx_stoch < num_actions_pad:
action.append(a_stoch)
out_of_range_action = False
# Step
obs, rew, done, _ = env.step(action)
eval_reward_sum += rew
# Average the rewards and log
eval_reward_mean = eval_reward_sum / n_eval_episodes
print(eval_reward_mean, 'over', n_eval_episodes, 'episodes')
game_scores.append(eval_reward_mean)
score_timesteps.append(step)
if max_eval_reward_mean is None or eval_reward_mean > max_eval_reward_mean:
logger.log(
"Saving model due to mean eval increase: {} -> {}".format(
max_eval_reward_mean, eval_reward_mean))
U.save_state(model_file)
model_saved = True
max_eval_reward_mean = eval_reward_mean
intact = ActWrapper(act, act_params)
intact.save(model_file + "_" + str(episode_number) + "_" +
str(int(np.round(max_eval_reward_mean))))
print('Act saved to ' + model_file + "_" + str(episode_number) +
"_" + str(int(np.round(max_eval_reward_mean))))
def evaluate(step, episode_number):
global max_eval_reward_mean, model_saved
print('Evaluate...')
eval_reward_sum = 0.0
# Run evaluation episodes
for eval_episode in range(n_eval_episodes):
obs = env.reset()
done = False
while not done:
# Choose action
action_idxes = np.array(
act(np.array(obs)[None],
stochastic=False)) # deterministic
actions_greedy = action_idxes / num_action_grains * actions_range + low
if eval_std == 0.0:
action = actions_greedy
else:
action = []
for index in range(len(actions_greedy)):
a_greedy = actions_greedy[index]
out_of_range_action = True
while out_of_range_action:
a_stoch = np.random.normal(loc=a_greedy,
scale=eval_std)
a_idx_stoch = np.rint(
(a_stoch + high[index]) /
actions_range[index] * num_action_grains)
if a_idx_stoch >= 0 and a_idx_stoch < num_actions_pad:
action.append(a_stoch)
out_of_range_action = False
# Step
obs, rew, done, _ = env.step(action)
eval_reward_sum += rew
# Average the rewards and log
eval_reward_mean = eval_reward_sum / n_eval_episodes
print(eval_reward_mean, 'over', n_eval_episodes, 'episodes')
with open("results/{}_{}_eval.csv".format(time_stamp, env_name),
"a") as eval_fw:
eval_writer = csv.writer(
eval_fw,
delimiter="\t",
lineterminator="\n",
)
eval_writer.writerow([episode_number, step, eval_reward_mean])
if max_eval_reward_mean is None or eval_reward_mean > max_eval_reward_mean:
logger.log(
"Saving model due to mean eval increase: {} -> {}".format(
max_eval_reward_mean, eval_reward_mean))
U.save_state(model_file)
model_saved = True
max_eval_reward_mean = eval_reward_mean
def learn(env,
policy_func,
dataset,
pretrained,
optim_batch_size=128,
max_iters=1e4,
adam_epsilon=1e-5,
optim_stepsize=3e-4,
ckpt_dir=None,
log_dir=None,
task_name=None):
val_per_iter = int(max_iters / 10)
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new policy
# placeholder
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
stochastic = U.get_placeholder_cached(name="stochastic")
loss = tf.reduce_mean(tf.square(ac - pi.ac))
var_list = pi.get_trainable_variables()
adam = MpiAdam(var_list, epsilon=adam_epsilon)
lossandgrad = U.function([ob, ac, stochastic],
[loss] + [U.flatgrad(loss, var_list)])
if not pretrained:
writer = U.FileWriter(log_dir)
ep_stats = stats(["Loss"])
U.initialize()
adam.sync()
logger.log("Pretraining with Behavior Cloning...")
for iter_so_far in tqdm(range(int(max_iters))):
ob_expert, ac_expert = dataset.get_next_batch(optim_batch_size,
'train')
loss, g = lossandgrad(ob_expert, ac_expert, True)
adam.update(g, optim_stepsize)
if not pretrained:
ep_stats.add_all_summary(writer, [loss], iter_so_far)
if iter_so_far % val_per_iter == 0:
ob_expert, ac_expert = dataset.get_next_batch(-1, 'val')
loss, g = lossandgrad(ob_expert, ac_expert, False)
logger.log("Validation:")
logger.log("Loss: %f" % loss)
if not pretrained:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iter_so_far)
if pretrained:
savedir_fname = tempfile.TemporaryDirectory().name
U.save_state(savedir_fname, var_list=pi.get_variables())
return savedir_fname
def save(self, path):
"""Save model to a pickle located at `path`"""
with tempfile.TemporaryDirectory() as td:
U.save_state(os.path.join(td, "model"))
arc_name = os.path.join(td, "packed.zip")
with zipfile.ZipFile(arc_name, 'w') as zipf:
for root, dirs, files in os.walk(td):
for fname in files:
file_path = os.path.join(root, fname)
if file_path != arc_name:
zipf.write(file_path,
os.path.relpath(file_path, td))
with open(arc_name, "rb") as f:
model_data = f.read()
with open(path, "wb") as f:
dill.dump((model_data, self._act_params), f)
agent2.experience(obs, [act2], rews[1], obs_n, goals[1])
print(env.scores, act1, act2, rews, goals, win)
if win:
break
ep_score = env.scores
agent1_score.append(ep_score[0])
agent2_score.append(ep_score[1])
q_loss1, p_loss1 = agent1.learn(arglist.batch_size, arglist.gamma)
q_loss2, p_loss2 = 0,0
if arglist.adv_agent == "agent":
q_loss2, p_loss2 = agent2.learn(arglist.batch_size, arglist.gamma)
agent1_q_loss.append(q_loss1)
agent1_p_loss.append(p_loss1)
agent2_q_loss.append(q_loss2)
agent2_p_loss.append(p_loss2)
print("Episodes {} scores: {}, losses {}".format(ep, ep_score,[q_loss1, p_loss1 , q_loss2, p_loss2 ]))
log.write('{}\n'.format(','.join(map(str,
[ep_score[0], ep_score[1] , q_loss1, p_loss1 , q_loss2, p_loss2 ]))))
print("Epoch {} scores: {}, losses {}".format(
epo, [np.mean(agent1_score), np.mean(agent2_score)],
[np.mean(agent1_q_loss), np.mean(agent1_p_loss) , np.mean(agent2_q_loss), np.mean(agent2_p_loss) ]))
U.save_state(model_path, saver)
def save(self, save_path):
tf_util.save_state(save_path, sess=self.sess)
def learn(
env,
policy_func,
discriminator,
expert_dataset,
embedding_z,
pretrained,
pretrained_weight,
*,
g_step,
d_step,
timesteps_per_batch, # what to train on
max_kl,
cg_iters,
gamma,
lam, # advantage estimation
entcoeff=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
d_stepsize=3e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
save_per_iter=100,
ckpt_dir=None,
log_dir=None,
load_model_path=None,
task_name=None):
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi",
ob_space,
ac_space,
reuse=(pretrained_weight != None))
oldpi = policy_func("oldpi", ob_space, ac_space)
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = U.mean(kloldnew)
meanent = U.mean(ent)
entbonus = entcoeff * meanent
vferr = U.mean(tf.square(pi.vpred - ret))
ratio = tf.exp(pi.pd.logp(ac) -
oldpi.pd.logp(ac)) # advantage * pnew / pold
surrgain = U.mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = pi.get_trainable_variables()
var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("pol")
]
vf_var_list = [
v for v in all_var_list if v.name.split("/")[1].startswith("vf")
]
d_adam = MpiAdam(discriminator.get_trainable_variables())
vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start + sz], shape))
start += sz
gvp = tf.add_n(
[U.sum(g * tangent) for (g, tangent) in zipsame(klgrads, tangents)]) # pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function([ob, ac, atarg], losses)
compute_lossandgrad = U.function([ob, ac, atarg], losses +
[U.flatgrad(optimgain, var_list)])
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob, ret],
U.flatgrad(vferr, vf_var_list))
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
writer = U.FileWriter(log_dir)
U.initialize()
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
d_adam.sync()
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
discriminator,
embedding=embedding_z,
timesteps_per_batch=timesteps_per_batch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
true_rewbuffer = deque(maxlen=40)
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
g_loss_stats = stats(loss_names)
d_loss_stats = stats(discriminator.loss_name)
ep_stats = stats(["True_rewards", "Rewards", "Episode_length"])
# if provide pretrained weight
if pretrained_weight is not None:
U.load_state(pretrained_weight, var_list=pi.get_variables())
# if provieded model path
if load_model_path is not None:
U.load_state(load_model_path)
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
# Save model
if iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iters_so_far)
logger.log("********** Iteration %i ************" % iters_so_far)
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
# ------------------ Update G ------------------
logger.log("Optimizing Policy...")
for _ in range(g_step):
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg[
"vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()) / atarg.std(
) # standardized advantage function estimate
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
assign_old_eq_new(
) # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(),
vfadam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=128):
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(
mbob) # update running mean/std for policy
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
g_losses = meanlosses
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
# ------------------ Update D ------------------
logger.log("Optimizing Discriminator...")
logger.log(fmt_row(13, discriminator.loss_name))
ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob))
batch_size = len(ob) // d_step
d_losses = [
] # list of tuples, each of which gives the loss for a minibatch
for ob_batch, ac_batch in dataset.iterbatches(
(ob, ac), include_final_partial_batch=False,
batch_size=batch_size):
ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob_batch))
# update running mean/std for discriminator
if hasattr(discriminator, "obs_rms"):
discriminator.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
*newlosses, g = discriminator.lossandgrad(ob_batch, ac_batch,
ob_expert, ac_expert)
d_adam.update(allmean(g), d_stepsize)
d_losses.append(newlosses)
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"]
) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, true_rets = map(flatten_lists, zip(*listoflrpairs))
true_rewbuffer.extend(true_rets)
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpTrueRewMean", np.mean(true_rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if rank == 0:
logger.dump_tabular()
g_loss_stats.add_all_summary(writer, g_losses, iters_so_far)
d_loss_stats.add_all_summary(writer, np.mean(d_losses, axis=0),
iters_so_far)
ep_stats.add_all_summary(writer, [
np.mean(true_rewbuffer),
np.mean(rewbuffer),
np.mean(lenbuffer)
], iters_so_far)
def train_maddpg(arglist):
with U.single_threaded_session():
# Create environment
env = make_env(arglist.scenario, arglist, arglist.benchmark)
# Create agent trainers
obs_shape_n = [env.observation_space[i].shape for i in range(env.n)]
num_adversaries = min(env.n, arglist.num_adversaries)
trainers = get_trainers(env,
num_adversaries,
obs_shape_n,
arglist,
good_agent_mode=arglist.good_policy,
adv_agent_mode=arglist.adv_policy)
print('Using good policy {} and adv policy {}'.format(
arglist.good_policy, arglist.adv_policy))
# Initialize
U.initialize()
# Load previous results, if necessary
if arglist.load_dir == "":
arglist.load_dir = arglist.save_dir
if arglist.display or arglist.restore or arglist.benchmark:
print('Loading previous state...')
U.load_state(arglist.load_dir)
episode_rewards = [0.0] # sum of rewards for all agents
agent_rewards = [[0.0]
for _ in range(env.n)] # individual agent reward
final_ep_rewards = [] # sum of rewards for training curve
final_ep_ag_rewards = [] # agent rewards for training curve
agent_info = [[[]]] # placeholder for benchmarking info
saver = tf.train.Saver(max_to_keep=None)
obs_n = env.reset()
episode_step = 0
train_step = 0
t_start = time.time()
if arglist.real_q_log:
world_state_buffer, action_n_buffer, start_episode_step_buffer, obs_n_buffer = [], [], [], []
q_means, real_means = [], []
print('Starting iterations...')
while True:
# get action
action_n = [
agent.action(obs) for agent, obs in zip(trainers, obs_n)
]
# environment step
new_obs_n, rew_n, done_n, info_n = env.step(action_n)
episode_step += 1
done = all(done_n) # note: unused, never happens
terminal = (episode_step >= arglist.max_episode_len)
done = done or terminal
if arglist.real_q_log:
world_state_buffer.append(deepcopy(env.world))
obs_n_buffer.append(obs_n)
action_n_buffer.append(action_n)
start_episode_step_buffer.append(episode_step)
# collect experience
for i, agent in enumerate(trainers):
agent.experience(obs_n[i], action_n[i], rew_n[i], new_obs_n[i],
done, terminal)
obs_n = new_obs_n
for i, rew in enumerate(rew_n):
episode_rewards[-1] += rew
agent_rewards[i][-1] += rew
if done or terminal:
obs_n = env.reset()
episode_step = 0
episode_rewards.append(0) # add element for next episode
for a in agent_rewards:
a.append(0)
agent_info.append([[]])
# increment global step counter
train_step += 1
# for benchmarking learned policies
if arglist.benchmark:
for i, info in enumerate(info_n):
agent_info[-1][i].append(info_n['n'])
if train_step > arglist.benchmark_iters and (done or terminal):
file_name = arglist.benchmark_dir + arglist.exp_name + '.pkl'
print('Finished benchmarking, now saving...')
with open(file_name, 'wb') as fp:
pickle.dump(agent_info[:-1], fp)
break
continue
# for displaying learned policies
if arglist.display:
time.sleep(0.1)
env.render()
continue
for agent in trainers:
loss = agent.update(trainers, train_step)
# save model, display training output
if terminal and (len(episode_rewards) % arglist.save_rate == 0):
if arglist.save_dir != '/tmp/policy/':
U.save_state(arglist.save_dir + arglist.exp_name,
saver=saver,
global_step=len(episode_rewards))
else:
U.save_state(
arglist.save_dir, saver=saver
) # print statement depends on whether or not there are adversaries
if num_adversaries == 0:
print(
"steps: {}, episodes: {}, mean episode reward: {}, time: {}"
.format(
train_step, len(episode_rewards),
np.mean(episode_rewards[-arglist.save_rate:-1]),
round(time.time() - t_start, 3)))
else:
print(
"steps: {}, episodes: {}, mean episode reward: {}, agent episode reward: {}, time: {}"
.format(
train_step, len(episode_rewards),
np.mean(episode_rewards[-arglist.save_rate:-1]), [
np.mean(rew[-arglist.save_rate:])
for rew in agent_rewards
], round(time.time() - t_start, 3)))
t_start = time.time()
# Keep track of final episode reward
final_ep_rewards.append(
np.mean(episode_rewards[-arglist.save_rate:-1]))
for rew in agent_rewards:
final_ep_ag_rewards.append(
np.mean(rew[-arglist.save_rate:-1]))
if arglist.real_q_log and (len(episode_rewards) %
(5 * arglist.save_rate) == 0):
q_mean, real_mean = calculate_real_q_value(
deepcopy(env),
trainers,
world_state_buffer=world_state_buffer,
action_n_buffer=action_n_buffer,
obs_n_buffer=obs_n_buffer,
start_episode_step_buffer=start_episode_step_buffer,
num_start_states=200,
args=arglist)
world_state_buffer, action_n_buffer, start_episode_step_buffer, obs_n_buffer = [], [], [], []
q_means.append(q_mean)
real_means.append(real_mean)
print('Q-mean: ' + str(q_mean) + ' Real mean: ' +
str(real_mean))
# saves final episode reward for plotting training curve later
if len(episode_rewards) > arglist.num_episodes:
rew_file_name = arglist.plots_dir + arglist.exp_name + '_rewards.pkl'
with open(rew_file_name, 'wb') as fp:
pickle.dump(final_ep_rewards, fp)
agrew_file_name = arglist.plots_dir + arglist.exp_name + '_agrewards.pkl'
with open(agrew_file_name, 'wb') as fp:
pickle.dump(final_ep_ag_rewards, fp)
args_file_name = arglist.plots_dir + arglist.exp_name + '_args.pkl'
with open(args_file_name, 'wb') as fp:
pickle.dump(arglist, fp)
if arglist.real_q_log:
real_q_path = arglist.plots_dir + arglist.exp_name + '_q_values.pkl'
with open(real_q_path, 'wb') as fp:
pickle.dump(
{
'q_means': q_means,
'real_means': real_means
}, fp)
print('...Finished total of {} episodes.'.format(
len(episode_rewards)))
break
def learn(
env,
policy_func,
discriminator,
expert_dataset,
timesteps_per_batch,
*,
g_step,
d_step, # timesteps per actor per update
clip_param,
entcoeff, # clipping parameter epsilon, entropy coeff
optim_epochs,
optim_stepsize,
optim_batchsize, # optimization hypers
gamma,
lam, # advantage estimation
max_timesteps=0,
max_episodes=0,
max_iters=0,
max_seconds=0, # time constraint
callback=None, # you can do anything in the callback, since it takes locals(), globals()
adam_epsilon=1e-5,
d_stepsize=3e-4,
schedule='constant', # annealing for stepsize parameters (epsilon and adam)
save_per_iter=100,
ckpt_dir=None,
task="train",
sample_stochastic=True,
load_model_path=None,
task_name=None,
max_sample_traj=1500):
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new policy
oldpi = policy_func("oldpi", ob_space, ac_space) # Network for old policy
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
lrmult = tf.placeholder(
name='lrmult', dtype=tf.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = U.mean(kloldnew)
meanent = U.mean(ent)
pol_entpen = (-entcoeff) * meanent
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = U.clip(ratio, 1.0 - clip_param, 1.0 + clip_param) * atarg #
pol_surr = -U.mean(tf.minimum(
surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
vf_loss = U.mean(tf.square(pi.vpred - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent"]
var_list = pi.get_trainable_variables()
lossandgrad = U.function([ob, ac, atarg, ret, lrmult],
losses + [U.flatgrad(total_loss, var_list)])
d_adam = MpiAdam(discriminator.get_trainable_variables())
adam = MpiAdam(var_list, epsilon=adam_epsilon)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function([ob, ac, atarg, ret, lrmult], losses)
U.initialize()
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
d_adam.sync()
adam.sync()
def allmean(x):
assert isinstance(x, np.ndarray)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
discriminator,
timesteps_per_batch,
stochastic=True)
traj_gen = traj_episode_generator(pi,
env,
timesteps_per_batch,
stochastic=sample_stochastic)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards
true_rewbuffer = deque(maxlen=100)
assert sum(
[max_iters > 0, max_timesteps > 0, max_episodes > 0,
max_seconds > 0]) == 1, "Only one time constraint permitted"
if task == 'sample_trajectory':
# not elegant, i know :(
sample_trajectory(load_model_path, max_sample_traj, traj_gen,
task_name, sample_stochastic)
sys.exit()
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
elif max_seconds and time.time() - tstart >= max_seconds:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0)
else:
raise NotImplementedError
# Save model
if iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iters_so_far)
logger.log("********** Iteration %i ************" % iters_so_far)
for _ in range(g_step):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg[
"vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()) / atarg.std(
) # standardized advantage function estimate
d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret),
shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
assign_old_eq_new(
) # set old parameter values to new parameter values
logger.log("Optimizing...")
logger.log(fmt_row(13, loss_names))
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs):
losses = [
] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize):
*newlosses, g = lossandgrad(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
adam.update(g, 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 d.iterate_once(optim_batchsize):
newlosses = compute_losses(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
losses.append(newlosses)
meanlosses, _, _ = mpi_moments(losses, axis=0)
# ------------------ Update D ------------------
logger.log("Optimizing Discriminator...")
logger.log(fmt_row(13, discriminator.loss_name))
ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob))
batch_size = len(ob) // d_step
d_losses = [
] # list of tuples, each of which gives the loss for a minibatch
ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob))
batch_size = len(ob) // d_step
d_losses = [
] # list of tuples, each of which gives the loss for a minibatch
for ob_batch, ac_batch in dataset.iterbatches(
(ob, ac), include_final_partial_batch=False,
batch_size=batch_size):
ob_expert, ac_expert = expert_dataset.get_next_batch(len(ob_batch))
# update running mean/std for discriminator
if hasattr(discriminator, "obs_rms"):
discriminator.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
*newlosses, g = discriminator.lossandgrad(ob_batch, ac_batch,
ob_expert, ac_expert)
d_adam.update(allmean(g), d_stepsize)
d_losses.append(newlosses)
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
# ----------------- logger --------------------
logger.log(fmt_row(13, meanlosses))
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"]
) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, true_rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
true_rewbuffer.extend(true_rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpTrueRewMean", np.mean(true_rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
def learn(env_id,
q_func,
lr=5e-4,
max_timesteps=10000,
buffer_size=5000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
train_steps=10,
learning_starts=500,
batch_size=32,
print_freq=10,
checkpoint_freq=100,
model_dir=None,
gamma=1.0,
target_network_update_freq=50,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
player_processes=None,
player_connections=None):
env, _, _ = create_gvgai_environment(env_id)
# Create all the functions necessary to train the model
# expert_decision_maker = ExpertDecisionMaker(env=env)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise)
session = tf.Session()
session.__enter__()
policy_path = os.path.join(model_dir, "Policy.pkl")
model_path = os.path.join(model_dir, "model", "model")
if os.path.isdir(os.path.join(model_dir, "model")):
load_state(model_path)
else:
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
act.save(policy_path)
save_state(model_path)
env.close()
# Create the replay buffer
if prioritized_replay:
replay_buffer_path = os.path.join(model_dir, "Prioritized_replay.pkl")
if os.path.isfile(replay_buffer_path):
with open(replay_buffer_path, 'rb') as input_file:
replay_buffer = pickle.load(input_file)
else:
replay_buffer = PrioritizedReplayBuffer(
buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = max_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer_path = os.path.join(model_dir, "Normal_replay.pkl")
if os.path.isfile(replay_buffer_path):
with open(replay_buffer_path, 'rb') as input_file:
replay_buffer = pickle.load(input_file)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
max_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
episode_rewards = list()
saved_mean_reward = -999999999
signal.signal(signal.SIGQUIT, signal_handler)
global terminate_learning
total_timesteps = 0
for timestep in range(max_timesteps):
if terminate_learning:
break
for connection in player_connections:
experiences, reward = connection.recv()
episode_rewards.append(reward)
for experience in experiences:
replay_buffer.add(*experience)
total_timesteps += 1
if total_timesteps < learning_starts:
if timestep % 10 == 0:
print("not strated yet", flush=True)
continue
if timestep % train_freq == 0:
for i in range(train_steps):
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(
batch_size, beta=beta_schedule.value(total_timesteps))
(obses_t, actions, rewards, obses_tp1, dones, weights,
batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(
batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones,
weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes,
new_priorities)
if timestep % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if print_freq is not None and timestep % print_freq == 0:
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular(
"% time spent exploring",
int(100 * exploration.value(total_timesteps)))
logger.dump_tabular()
if timestep % checkpoint_freq == 0 and mean_100ep_reward > saved_mean_reward:
act.save(policy_path)
save_state(model_path)
saved_mean_reward = mean_100ep_reward
with open(replay_buffer_path, 'wb') as output_file:
pickle.dump(replay_buffer, output_file,
pickle.HIGHEST_PROTOCOL)
send_message_to_all(player_connections, Message.UPDATE)
send_message_to_all(player_connections, Message.TERMINATE)
if mean_100ep_reward > saved_mean_reward:
act.save(policy_path)
with open(replay_buffer_path, 'wb') as output_file:
pickle.dump(replay_buffer, output_file, pickle.HIGHEST_PROTOCOL)
for player_process in player_processes:
player_process.join()
# player_process.terminate()
return act.load(policy_path)
def learn(
env,
policy_func,
*,
timesteps=4,
timesteps_per_batch, # timesteps per actor per update
clip_param,
entcoeff, # clipping parameter epsilon, entropy coeff
optim_epochs,
optim_stepsize,
optim_batchsize, # optimization hypers
gamma,
lam, # advantage estimation
max_timesteps=0,
max_episodes=0,
max_iters=0,
max_seconds=0, # time constraint
callback=None, # you can do anything in the callback, since it takes locals(), globals()
adam_epsilon=1e-5,
schedule='constant', # annealing for stepsize parameters (epsilon and adam)
save_per_iter=100,
ckpt_dir=None,
task="train",
sample_stochastic=True,
load_model_path=None,
task_name=None,
max_sample_traj=1500):
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", timesteps, ob_space,
ac_space) # Construct network for new policy
oldpi = policy_func("oldpi", timesteps, ob_space,
ac_space) # Network for old policy
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
pi_vpred = tf.placeholder(dtype=tf.float32, shape=[None])
lrmult = tf.placeholder(
name='lrmult', dtype=tf.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
ob = U.get_placeholder_cached(name="ob")
# ob_now = tf.placeholder(dtype=tf.float32, shape=[optim_batchsize, list(ob_space.shape)[0]])
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = U.mean(kloldnew)
meanent = U.mean(ent)
pol_entpen = (-entcoeff) * meanent
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = U.clip(ratio, 1.0 - clip_param, 1.0 + clip_param) * atarg #
pol_surr = -U.mean(tf.minimum(
surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
vf_loss = U.mean(tf.square(pi.vpred - ret))
# total_loss = pol_surr + pol_entpen + vf_loss
total_loss = pol_surr + pol_entpen
losses = [pol_surr, pol_entpen, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "kl", "ent"]
var_list = pi.get_trainable_variables()
vf_var_list = [
v for v in var_list if v.name.split("/")[1].startswith("vf")
]
pol_var_list = [
v for v in var_list if not v.name.split("/")[1].startswith("vf")
]
# lossandgrad = U.function([ob, ac, atarg ,ret, lrmult], losses + [U.flatgrad(total_loss, var_list)])
lossandgrad = U.function([ob, ac, atarg, ret, lrmult],
losses + [U.flatgrad(total_loss, pol_var_list)])
vf_grad = U.function([ob, ac, atarg, ret, lrmult],
U.flatgrad(vf_loss, vf_var_list))
# adam = MpiAdam(var_list, epsilon=adam_epsilon)
pol_adam = MpiAdam(pol_var_list, epsilon=adam_epsilon)
vf_adam = MpiAdam(vf_var_list, epsilon=adam_epsilon)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function([ob, ac, atarg, ret, lrmult], losses)
U.initialize()
#adam.sync()
pol_adam.sync()
vf_adam.sync()
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
timesteps,
env,
timesteps_per_batch,
stochastic=True)
traj_gen = traj_episode_generator(pi,
env,
timesteps_per_batch,
stochastic=sample_stochastic)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards
EpRewMean_MAX = 2.5e3
assert sum(
[max_iters > 0, max_timesteps > 0, max_episodes > 0,
max_seconds > 0]) == 1, "Only one time constraint permitted"
if task == 'sample_trajectory':
# not elegant, i know :(
sample_trajectory(load_model_path, max_sample_traj, traj_gen,
task_name, sample_stochastic)
sys.exit()
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
elif max_seconds and time.time() - tstart >= max_seconds:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0)
else:
raise NotImplementedError
# Save model
if iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iters_so_far)
logger.log("********** Iteration %i ************" % iters_so_far)
# if(iters_so_far == 1):
# a = 1
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, vpred, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"vpred"], seg["tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
d = Dataset(
dict(ob=ob, ac=ac, atarg=atarg, vpred=vpred, vtarg=tdlamret),
shuffle=False
) #d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vpred = vpred, vtarg=tdlamret), shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
assign_old_eq_new() # set old parameter values to new parameter values
logger.log("Optimizing...")
logger.log(fmt_row(13, loss_names))
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs):
losses = [
] # list of tuples, each of which gives the loss for a minibatch
pre_obs = [seg["ob_reset"] for jmj in range(timesteps - 1)]
for batch in d.iterate_once(optim_batchsize):
##feed ob, 重新处理一下ob,在batch["ob"]的最前面插入timesteps-1个env.reset的ob,然后滑动串口划分一下batch['ob]
ob_now = np.append(pre_obs, batch['ob']).reshape(
optim_batchsize + timesteps - 1,
list(ob_space.shape)[0])
pre_obs = ob_now[-(timesteps - 1):]
ob_fin = []
for jmj in range(optim_batchsize):
ob_fin.append(ob_now[jmj:jmj + timesteps])
*newlosses, g = lossandgrad(ob_fin, batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult) ###这里的g好像都是0
#adam.update(g, optim_stepsize * cur_lrmult)
pol_adam.update(g, optim_stepsize * cur_lrmult)
vf_g = vf_grad(ob_fin, batch["ac"], batch["atarg"],
batch["vtarg"], cur_lrmult)
vf_adam.update(vf_g, optim_stepsize * cur_lrmult)
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
pre_obs = [seg["ob_reset"] for jmj in range(timesteps - 1)]
for batch in d.iterate_once(optim_batchsize):
##feed ob, 重新处理一下ob,在batch["ob"]的最前面插入timesteps-1个env.reset的ob,然后滑动串口划分一下batch['ob]
ob_now = np.append(pre_obs, batch['ob']).reshape(
optim_batchsize + timesteps - 1,
list(ob_space.shape)[0])
pre_obs = ob_now[-(timesteps - 1):]
ob_fin = []
for jmj in range(optim_batchsize):
ob_fin.append(ob_now[jmj:jmj + timesteps])
*newlosses, g = lossandgrad(ob_fin, batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult) ###这里的g好像都是0
#adam.update(g, optim_stepsize * cur_lrmult)
pol_adam.update(g, optim_stepsize * cur_lrmult)
vf_g = vf_grad(ob_fin, batch["ac"], batch["atarg"],
batch["vtarg"], cur_lrmult)
vf_adam.update(vf_g, optim_stepsize * cur_lrmult)
logger.log("Evaluating losses...")
losses = []
loss_pre_obs = [seg["ob_reset"] for jmj in range(timesteps - 1)]
for batch in d.iterate_once(optim_batchsize):
### feed ob
ob_now = np.append(loss_pre_obs, batch['ob']).reshape(
optim_batchsize + timesteps - 1,
list(ob_space.shape)[0])
loss_pre_obs = ob_now[-(timesteps - 1):]
ob_fin = []
for jmj in range(optim_batchsize):
ob_fin.append(ob_now[jmj:jmj + timesteps])
newlosses = compute_losses(ob_fin, batch["ac"], batch["atarg"],
batch["vtarg"], cur_lrmult)
losses.append(newlosses)
meanlosses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, meanlosses))
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
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))
if (np.mean(rewbuffer) > EpRewMean_MAX):
EpRewMean_MAX = np.mean(rewbuffer)
print(iters_so_far)
print(np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
