def learn(network, env,
seed=None,
total_timesteps=None,
nb_epochs=None, # with default settings, perform 1M steps total
nb_epoch_cycles=20,
nb_rollout_steps=100,
reward_scale=1.0,
render=False,
render_eval=False,
noise_type='adaptive-param_0.2',
normalize_returns=False,
normalize_observations=True,
critic_l2_reg=1e-2,
actor_lr=1e-4,
critic_lr=1e-3,
popart=False,
gamma=0.99,
clip_norm=None,
nb_train_steps=50, # per epoch cycle and MPI worker,
nb_eval_steps=100,
batch_size=64, # per MPI worker
tau=0.01,
eval_env=None,
param_noise_adaption_interval=50,
load_path=None,
**network_kwargs):
set_global_seeds(seed)
if total_timesteps is not None:
assert nb_epochs is None
nb_epochs = int(total_timesteps) // (nb_epoch_cycles * nb_rollout_steps)
else:
nb_epochs = 500
if MPI is not None:
rank = MPI.COMM_WORLD.Get_rank()
else:
rank = 0
nb_actions = env.action_space.shape[-1]
assert (np.abs(env.action_space.low) == env.action_space.high).all() # we assume symmetric actions.
memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
critic = Critic(network=network, **network_kwargs)
actor = Actor(nb_actions, network=network, **network_kwargs)
action_noise = None
param_noise = None
if noise_type is not None:
for current_noise_type in noise_type.split(','):
current_noise_type = current_noise_type.strip()
if current_noise_type == 'none':
pass
elif 'adaptive-param' in current_noise_type:
_, stddev = current_noise_type.split('_')
param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
elif 'normal' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
elif 'ou' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
else:
raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
max_action = env.action_space.high
logger.info('scaling actions by {} before executing in env'.format(max_action))
agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape,
gamma=gamma, tau=tau, normalize_returns=normalize_returns, normalize_observations=normalize_observations,
batch_size=batch_size, action_noise=action_noise, param_noise=param_noise, critic_l2_reg=critic_l2_reg,
actor_lr=actor_lr, critic_lr=critic_lr, enable_popart=popart, clip_norm=clip_norm,
reward_scale=reward_scale)
logger.info('Using agent with the following configuration:')
logger.info(str(agent.__dict__.items()))
eval_episode_rewards_history = deque(maxlen=100)
episode_rewards_history = deque(maxlen=100)
sess = U.get_session()
# Prepare everything.
agent.initialize(sess)
if load_path is not None:
agent.load(load_path)
sess.graph.finalize()
agent.reset()
obs = env.reset()
if eval_env is not None:
eval_obs = eval_env.reset()
nenvs = obs.shape[0]
episode_reward = np.zeros(nenvs, dtype = np.float32) #vector
episode_step = np.zeros(nenvs, dtype = int) # vector
episodes = 0 #scalar
t = 0 # scalar
epoch = 0
start_time = time.time()
epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actions = []
epoch_qs = []
epoch_episodes = 0
for epoch in range(nb_epochs):
for cycle in range(nb_epoch_cycles):
# Perform rollouts.
if nenvs > 1:
# if simulating multiple envs in parallel, impossible to reset agent at the end of the episode in each
# of the environments, so resetting here instead
agent.reset()
for t_rollout in range(nb_rollout_steps):
# Predict next action.
action, q, _, _ = agent.step(obs, apply_noise=True, compute_Q=True)
# Execute next action.
if rank == 0 and render:
env.render()
# max_action is of dimension A, whereas action is dimension (nenvs, A) - the multiplication gets broadcasted to the batch
new_obs, r, done, info = env.step(max_action * action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
# note these outputs are batched from vecenv
t += 1
if rank == 0 and render:
env.render()
episode_reward += r
episode_step += 1
# Book-keeping.
epoch_actions.append(action)
epoch_qs.append(q)
agent.store_transition(obs, action, r, new_obs, done) #the batched data will be unrolled in memory.py's append.
obs = new_obs
for d in range(len(done)):
if done[d]:
# Episode done.
epoch_episode_rewards.append(episode_reward[d])
episode_rewards_history.append(episode_reward[d])
epoch_episode_steps.append(episode_step[d])
episode_reward[d] = 0.
episode_step[d] = 0
epoch_episodes += 1
episodes += 1
if nenvs == 1:
agent.reset()
# Train.
epoch_actor_losses = []
epoch_critic_losses = []
epoch_adaptive_distances = []
for t_train in range(nb_train_steps):
# Adapt param noise, if necessary.
if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
distance = agent.adapt_param_noise()
epoch_adaptive_distances.append(distance)
cl, al = agent.train()
epoch_critic_losses.append(cl)
epoch_actor_losses.append(al)
agent.update_target_net()
# Evaluate.
eval_episode_rewards = []
eval_qs = []
if eval_env is not None:
nenvs_eval = eval_obs.shape[0]
eval_episode_reward = np.zeros(nenvs_eval, dtype = np.float32)
for t_rollout in range(nb_eval_steps):
eval_action, eval_q, _, _ = agent.step(eval_obs, apply_noise=False, compute_Q=True)
eval_obs, eval_r, eval_done, eval_info = eval_env.step(max_action * eval_action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
if render_eval:
eval_env.render()
eval_episode_reward += eval_r
eval_qs.append(eval_q)
for d in range(len(eval_done)):
if eval_done[d]:
eval_episode_rewards.append(eval_episode_reward[d])
eval_episode_rewards_history.append(eval_episode_reward[d])
eval_episode_reward[d] = 0.0
if MPI is not None:
mpi_size = MPI.COMM_WORLD.Get_size()
else:
mpi_size = 1
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
stats = agent.get_stats()
combined_stats = stats.copy()
combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
combined_stats['rollout/return_std'] = np.std(epoch_episode_rewards)
combined_stats['rollout/return_history'] = np.mean(episode_rewards_history)
combined_stats['rollout/return_history_std'] = np.std(episode_rewards_history)
combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
combined_stats['train/param_noise_distance'] = np.mean(epoch_adaptive_distances)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(t) / float(duration)
combined_stats['total/episodes'] = episodes
combined_stats['rollout/episodes'] = epoch_episodes
combined_stats['rollout/actions_std'] = np.std(epoch_actions)
# Evaluation statistics.
if eval_env is not None:
combined_stats['eval/return'] = eval_episode_rewards
combined_stats['eval/return_history'] = np.mean(eval_episode_rewards_history)
combined_stats['eval/Q'] = eval_qs
combined_stats['eval/episodes'] = len(eval_episode_rewards)
def as_scalar(x):
if isinstance(x, np.ndarray):
assert x.size == 1
return x[0]
elif np.isscalar(x):
return x
else:
raise ValueError('expected scalar, got %s'%x)
combined_stats_sums = np.array([ np.array(x).flatten()[0] for x in combined_stats.values()])
if MPI is not None:
combined_stats_sums = MPI.COMM_WORLD.allreduce(combined_stats_sums)
combined_stats = {k : v / mpi_size for (k,v) in zip(combined_stats.keys(), combined_stats_sums)}
# Total statistics.
combined_stats['total/epochs'] = epoch + 1
combined_stats['total/steps'] = t
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
if rank == 0:
logger.dump_tabular()
logger.info('')
logdir = logger.get_dir()
if rank == 0 and logdir:
if hasattr(env, 'get_state'):
with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f:
pickle.dump(env.get_state(), f)
if eval_env and hasattr(eval_env, 'get_state'):
with open(os.path.join(logdir, 'eval_env_state.pkl'), 'wb') as f:
pickle.dump(eval_env.get_state(), f)
return agent
def learn(
network,
env,
seed=None,
total_timesteps=None,
nb_epochs=None, # with default settings, perform 1M steps total
nb_epoch_cycles=20,
nb_rollout_steps=100,
reward_scale=1.0,
render=False,
render_eval=False,
noise_type='adaptive-param_0.2',
normalize_returns=False,
normalize_observations=True,
actor_l2_reg=0.0,
critic_l2_reg=1e-2,
actor_lr=1e-4,
critic_lr=1e-3,
popart=False,
gamma=0.99,
clip_norm=None,
nb_train_steps=50, # per epoch cycle and MPI worker,
nb_eval_steps=1000,
batch_size=64, # per MPI worker
tau=0.01,
eval_env=None,
param_noise_adaption_interval=50,
rb_size=1e6,
save_interval=1,
pretrain_epochs=0,
load_path=None,
demos_path=None,
bc_teacher_lambda=0.0,
use_qfilter=False,
**network_kwargs):
"""Learns policy using DDPG, with vectorized environments.
If we pass other arguments that aren't specified here, they are considered
as network_kwargs.
Parameters
----------
noise_type: for noise to be added to the behavior policy. They are NOT
using the noise type from the paper but 'AdaptiveParamNoiseSpec'. I
_think_ that if one does the OU process, we get action noise, but not
parameter noise. Also, be sure to use `name_stdev` in that convention,
as the code will split the argument at the underscores.
actor_lr: 1e-4 (matches paper)
critic_lr: 1e-3 (matches paper)
critic_l2: 1e-2 (matches paper)
gamma: 0.99 (matches paper)
batch_size: 64 (matches paper for lower-dim env obs/states)
tau: 0.01 for soft target updates of actor and critic nets. Paper used 0.001.
nb_epoch_cycles: number of times we go through this cycle of: (1) get
rollouts with noise added to policy and apply to replay buffer, (2)
gradient updates for actor/critic, (3) evaluation rollouts (if any).
AFTER all of these cycles happen, THEN we log statistics.
nb_rollout_steps: number of steps in each parallel env we take with
exploration policy without training, so this is just to populate the
replay buffer. More parallel envs *should* mean that we get more
samples in the buffer between each gradient updates of the network, so
this might need to be environment *and* machine (# of CPUs) specific.
nb_train_steps: after doing `nb_rollout_steps` in each parallel env, we do
this many updates; each involves sampling from the replay buffer and
updating the actor and critic (via lagged target updates).
nb_eval_steps: 1000, I changed from the 100 as default. Using 1000 ensures
that fixed length envs like Ant-v2 can get one full episode (assuming
one parallel env) during evaluation stagtes.
eval_env: A separate environment for evaluation only, where no noise is
applied, similar to how rlkit does it.
save_interval: Frequency between saving.
"""
set_global_seeds(seed)
# Daniel: this helps to maintain compatibility with PPO2 code. For now
# we're ignoring it, but we should check that we're always clipping. I
# changed the nb_epochs to match with PPO2 in that we divide by nenvs.
if 'limit_act_range' in network_kwargs:
network_kwargs.pop('limit_act_range')
nenvs = env.num_envs
nbatchsize = nenvs * nb_epoch_cycles * nb_rollout_steps
if total_timesteps is not None:
assert nb_epochs is None
nb_epochs = int(total_timesteps) // nbatchsize
else:
nb_epochs = 500
if MPI is not None:
rank = MPI.COMM_WORLD.Get_rank()
else:
rank = 0
# we assume symmetric actions.
nb_actions = env.action_space.shape[-1]
assert (np.abs(env.action_space.low) == env.action_space.high).all()
# Form XP (1M steps, same as in paper), and critic/actor networks.
# Daniel: force dtype here so we can use uint8 type images.
assert env.observation_space.low.dtype == env.observation_space.high.dtype
memory = Memory(limit=int(rb_size),
action_shape=env.action_space.shape,
observation_shape=env.observation_space.shape,
dtype=env.observation_space.low.dtype)
critic = Critic(network=network, **network_kwargs)
actor = Actor(nb_actions, network=network, **network_kwargs)
action_noise = None
param_noise = None
if noise_type is not None:
for current_noise_type in noise_type.split(','):
current_noise_type = current_noise_type.strip()
if current_noise_type == 'none':
pass
elif 'adaptive-param' in current_noise_type:
_, stddev = current_noise_type.split('_')
param_noise = AdaptiveParamNoiseSpec(
initial_stddev=float(stddev),
desired_action_stddev=float(stddev))
elif 'normal' in current_noise_type:
_, stddev = current_noise_type.split('_')
#action_noise = NormalActionNoise(mu=np.zeros(nb_actions),
# sigma=float(stddev)*np.ones(nb_actions))
#if nenvs > 1:
# Daniel: adding this to replace the former.
action_noise = NormalActionNoise(mu=np.zeros(nb_actions),
sigma=float(stddev) *
np.ones(nb_actions),
shape=(nenvs, nb_actions))
elif 'ou' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = OrnsteinUhlenbeckActionNoise(
mu=np.zeros(nb_actions),
sigma=float(stddev) * np.ones(nb_actions))
else:
raise RuntimeError(
'unknown noise type "{}"'.format(current_noise_type))
max_action = env.action_space.high
logger.info(
'scaling actions by {} before executing in env'.format(max_action))
# The `learn` defaults above have priority over defaults in DDPG class.
agent = DDPG(actor,
critic,
memory,
env.observation_space.shape,
env.action_space.shape,
gamma=gamma,
tau=tau,
normalize_returns=normalize_returns,
normalize_observations=normalize_observations,
batch_size=batch_size,
action_noise=action_noise,
param_noise=param_noise,
actor_l2_reg=actor_l2_reg,
critic_l2_reg=critic_l2_reg,
actor_lr=actor_lr,
critic_lr=critic_lr,
enable_popart=popart,
clip_norm=clip_norm,
reward_scale=reward_scale,
bc_teacher_lambda=bc_teacher_lambda,
use_qfilter=use_qfilter)
logger.info('Using agent with the following configuration:')
logger.info(str(agent.__dict__.items()))
# Prepare everything.
sess = U.get_session()
agent.initialize(sess)
# --------------------------------------------------------------------------
# Daniel: similar as PPO2 code as `agent` is similar to `model` but has to
# be initialized explicitly above. Must call after `agent.load` gets
# created. Not sure if this works with parameter space noise or with
# normalization, but I don't plan to resume training (for now). It also has
# to be *before* the `graph.finalize()` because otherwise we get an error.
# --------------------------------------------------------------------------
if load_path is not None:
logger.info("\nInside ddpg, loading model from: {}".format(load_path))
agent.load(load_path)
# --------------------------------------------------------------------------
sess.graph.finalize()
agent.reset()
# --------------------------------------------------------------------------
# Daniel: populate replay buffer, followed by pretraining stage.
# But if load_path is not None, then doesn't make sense -- we want to load.
# We also don't need to do this if timesteps is 0 (e.g., for playing policy).
# --------------------------------------------------------------------------
if total_timesteps == 0:
return agent
assert seed == 1500, 'We normally want seed 1500, yet: {}'.format(seed)
if (demos_path is not None and load_path is None):
_ddpg_demos(demos_path, agent, memory)
assert memory.nb_entries == memory.nb_teach_entries, memory.nb_entries
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
# Pretrain, based on their training code for some # of minibatches.
pt_actor_losses = []
pt_critic_losses = []
batches_per_ep = int(memory.nb_entries / batch_size)
logger.info(
'Running pre-training for {} epochs'.format(pretrain_epochs))
logger.info(' data size in memory: {}'.format(memory.nb_entries))
logger.info(' each batch: {}, epoch mbs: {}'.format(
batch_size, batches_per_ep))
pt_start = time.time()
for epoch in range(1, pretrain_epochs + 1):
c_losses = []
a_losses = []
for _ in range(batches_per_ep):
cl, al = agent.train(during_pretrain=True)
agent.update_target_net()
c_losses.append(cl)
a_losses.append(al)
pt_critic_losses.append(np.mean(c_losses))
pt_actor_losses.append(np.mean(a_losses))
# Check and save model occasionally.
if epoch == 1 or epoch % 5 == 0:
pt_time = (time.time() - pt_start) / 60.
logger.info(
' epoch done: {}, loss over past epoch: {:.4f}'.format(
str(epoch).zfill(4), pt_actor_losses[-1]))
logger.info(' critic loss over past epoch: {:.4f}'.format(
pt_critic_losses[-1]))
logger.info(' elapsed time: {:.1f}m'.format(pt_time))
savepath = osp.join(
checkdir, 'pretrain_epoch_{}'.format(str(epoch).zfill(4)))
logger.info('Saving model checkpoint to: ', savepath)
agent.save(savepath)
pt_time = (time.time() - pt_start) / 60.
logger.info('losses a: {}'.format(np.array(pt_actor_losses)))
logger.info('losses c: {}'.format(np.array(pt_critic_losses)))
logger.info('Finished loading teacher samples + pre-training.')
logger.info('Pre-training took {:.1f}m.\n'.format(pt_time))
# --------------------------------------------------------------------------
# Back to their code. For cloth, `env.reset()` takes a while so we put it here.
obs = env.reset()
if eval_env is not None:
eval_obs = eval_env.reset()
nenvs = obs.shape[0]
# Daniel: Debugging/sanity checks.
_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
U.display_var_info(_variables)
logger.info("\nInside DDPG, about to start epochs")
logger.info(
"nbatchsize: {}, get this in buffer before DDPG updates".format(
nbatchsize))
logger.info(" i.e.: (nenv {}) * (cycles {}) * (nsteps {})".format(
nenvs, nb_epoch_cycles, nb_rollout_steps))
logger.info("nb_epochs: {}, number of cycles to use".format(nb_epochs))
logger.info("eval_env None? {}".format(eval_env is None))
logger.info("(end of debugging messages)\n")
# File paths.
checkdir = osp.join(logger.get_dir(), 'checkpoints')
action_dir = osp.join(logger.get_dir(), 'actions')
episode_dir = osp.join(logger.get_dir(), 'ep_all_infos')
os.makedirs(checkdir, exist_ok=True)
os.makedirs(action_dir, exist_ok=True)
os.makedirs(episode_dir, exist_ok=True)
# Daniel: use these two to store past 100 episode history. Report these stats!
eval_episode_rewards_history = deque(maxlen=100)
episode_rewards_history = deque(maxlen=100)
all_eval_episode_rewards = []
# reward/step: cumulative quantities for each episode in vecenv.
# epoch_{actions,qs} will grow without bound, fyi.
episode_reward = np.zeros(nenvs, dtype=np.float32) #vector
episode_step = np.zeros(nenvs, dtype=int) # vector
episodes = 0 #scalar
t = 0 # scalar
epoch = 0
start_time = time.time()
epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actions = []
epoch_qs = []
epoch_episodes = 0
for epoch in range(nb_epochs):
mb_actions = []
mb_epinfos = []
for cycle in range(nb_epoch_cycles):
# Perform rollouts.
if nenvs > 1:
# if simulating multiple envs in parallel, impossible to reset
# agent at the end of the episode in each of the environments,
# so resetting here instead
agent.reset()
# Daniel: pure data collection (noise added) to populate replay buffer.
# No training until after this, and note the parallel stepping (VecEnv).
for t_rollout in range(nb_rollout_steps):
# Predict next action.
# action: (#_parallel_envs, ac_dim), q: (#_parallel_envs, 1)
action, q, _, _ = agent.step(obs,
apply_noise=True,
compute_Q=True)
# Execute next action.
if rank == 0 and render:
env.render()
# max_action is of dimension A, whereas action is dimension
# (nenvs, A) - the multiplication gets broadcasted to the batch
# scale for execution in env (as far as DDPG is concerned,
# every action is in [-1, 1])
new_obs, r, done, info = env.step(max_action * action)
r = r.astype(np.float32)
# note these outputs are batched from vecenv (first dim = batch).
t += 1
if rank == 0 and render:
env.render()
episode_reward += r
episode_step += 1
# Book-keeping. (Daniel: agent.train() doesn't require these two lists)
epoch_actions.append(action)
epoch_qs.append(q)
# Daniel: Same as PPO2 code.
mb_actions.append(action)
for inf in info:
maybeepinfo = inf.get('episode')
if maybeepinfo:
mb_epinfos.append(inf)
#the batched data will be unrolled in memory.py's append.
agent.store_transition(obs, action, r, new_obs, done)
obs = new_obs
for d in range(len(done)):
if done[d]:
# Episode done.
epoch_episode_rewards.append(
episode_reward[d]) # Entire history
episode_rewards_history.append(
episode_reward[d]) # Last 100 only
epoch_episode_steps.append(episode_step[d])
episode_reward[d] = 0.
episode_step[d] = 0
epoch_episodes += 1
episodes += 1
if nenvs == 1:
agent.reset()
# Train.
epoch_actor_losses = []
epoch_critic_losses = []
epoch_adaptive_distances = []
for t_train in range(nb_train_steps):
# Adapt param noise, if necessary.
if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
distance = agent.adapt_param_noise()
epoch_adaptive_distances.append(distance)
cl, al = agent.train()
epoch_critic_losses.append(cl)
epoch_actor_losses.append(al)
agent.update_target_net()
# Evaluate. (Daniel: note that no noise is applied here.)
# Also it seems like episodes do not naturally reset before this starts?
# Also, unlike epoch_episode_reward, here we create eval_episode_reward here ...
eval_episode_rewards = []
eval_qs = []
if eval_env is not None:
logger.info('Now on the eval_env for {} steps...'.format(
nb_eval_steps))
nenvs_eval = eval_obs.shape[0]
eval_episode_reward = np.zeros(nenvs_eval, dtype=np.float32)
for t_rollout in range(nb_eval_steps):
eval_action, eval_q, _, _ = agent.step(eval_obs,
apply_noise=False,
compute_Q=True)
# scale for execution in env (for DDPG, every action is in [-1, 1])
eval_obs, eval_r, eval_done, eval_info = eval_env.step(
max_action * eval_action)
if render_eval:
eval_env.render()
eval_episode_reward += eval_r
eval_qs.append(eval_q)
for d in range(len(eval_done)):
if eval_done[d]:
eval_episode_rewards.append(eval_episode_reward[d])
eval_episode_rewards_history.append(
eval_episode_reward[d])
all_eval_episode_rewards.append(
eval_episode_reward[d])
eval_episode_reward[
d] = 0.0 # Daniel: reset for next episode.
if MPI is not None:
mpi_size = MPI.COMM_WORLD.Get_size()
else:
mpi_size = 1
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
stats = agent.get_stats()
combined_stats = stats.copy()
combined_stats['memory/nb_entries'] = memory.nb_entries
combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
combined_stats['rollout/return_history'] = np.mean(
episode_rewards_history)
combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
combined_stats['train/param_noise_distance'] = np.mean(
epoch_adaptive_distances)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(t) / float(duration)
combined_stats['total/episodes'] = episodes
combined_stats['rollout/episodes'] = epoch_episodes
combined_stats['rollout/actions_std'] = np.std(epoch_actions)
# Evaluation statistics. (Daniel: use eval/return_history for plots)
if eval_env is not None:
combined_stats['eval/return'] = np.mean(eval_episode_rewards)
combined_stats['eval/return_history'] = np.mean(
eval_episode_rewards_history)
combined_stats['eval/Q'] = eval_qs
combined_stats['eval/episodes'] = len(eval_episode_rewards)
def as_scalar(x):
if isinstance(x, np.ndarray):
assert x.size == 1
return x[0]
elif np.isscalar(x):
return x
else:
raise ValueError('expected scalar, got %s' % x)
combined_stats_sums = np.array(
[np.array(x).flatten()[0] for x in combined_stats.values()])
if MPI is not None:
combined_stats_sums = MPI.COMM_WORLD.allreduce(combined_stats_sums)
combined_stats = {
k: v / mpi_size
for (k, v) in zip(combined_stats.keys(), combined_stats_sums)
}
# Total statistics.
combined_stats['total/epochs'] = epoch + 1
combined_stats['total/steps_per_env'] = t
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
if rank == 0:
logger.dump_tabular()
logger.info('')
logdir = logger.get_dir()
if rank == 0 and logdir:
if hasattr(env, 'get_state'):
with open(osp.join(logdir, 'env_state.pkl'), 'wb') as f:
pickle.dump(env.get_state(), f)
if eval_env and hasattr(eval_env, 'get_state'):
with open(osp.join(logdir, 'eval_env_state.pkl'), 'wb') as f:
pickle.dump(eval_env.get_state(), f)
# Daniel: arguable, we can save all episodes but hard if we don't know the steps.
#if eval_env:
# with open(os.path.join(logdir, 'all_eval_episode_rewards.pkl'), 'wb') as f:
# pickle.dump(all_eval_episode_rewards, f)
# Daniel: we can use cycle or epoch for this if condition ... kind of annoying but w/e.
if cycle % save_interval == 0:
logger.info('We are now saving stuff!!')
savepath = osp.join(checkdir, '%.5i' % epoch)
logger.info('Saving model checkpoint to: ', savepath)
agent.save(savepath)
# ------------------------------------------------------------------
# Daniel: extra stuff for debugging PPO on cloth, actions and infos for each episode.
mb_actions = _sf01(np.asarray(mb_actions))
act_savepath = osp.join(action_dir, 'actions_%.5i.pkl' % epoch)
epi_savepath = osp.join(episode_dir, 'infos_%.5i.pkl' % epoch)
with open(act_savepath, 'wb') as fh:
pickle.dump(mb_actions, fh)
with open(epi_savepath, 'wb') as fh:
pickle.dump(mb_epinfos, fh)
# Daniel: we were not resetting earlier. Actually there are other
# epoch_stats which we might consider resetting here?
epoch_episodes = 0
return agent
