def train(hparams, output_dir, report_fn=None):
"""Train."""
hparams = initialize_env_specs(hparams)
learner = rl_utils.LEARNERS[hparams.base_algo](hparams.frame_stack_size,
FLAGS.output_dir,
output_dir)
policy_hparams = trainer_lib.create_hparams(hparams.base_algo_params)
rl_utils.update_hparams_from_hparams(policy_hparams, hparams,
hparams.base_algo + "_")
total_steps = policy_hparams.epochs_num
eval_every_epochs = policy_hparams.eval_every_epochs
if eval_every_epochs == 0:
eval_every_epochs = total_steps
policy_hparams.eval_every_epochs = 0
steps = list(range(eval_every_epochs, total_steps + 1, eval_every_epochs))
if not steps or steps[-1] < eval_every_epochs:
steps.append(eval_every_epochs)
metric_name = rl_utils.get_metric_name(
sampling_temp=hparams.eval_sampling_temps[0],
max_num_noops=hparams.eval_max_num_noops,
clipped=False)
for step in steps:
policy_hparams.epochs_num = step
learner.train(hparams.env_fn,
policy_hparams,
simulated=False,
save_continuously=True,
epoch=0)
eval_metrics = rl_utils.evaluate_all_configs(hparams, output_dir)
tf.logging.info("Agent eval metrics:\n{}".format(
pprint.pformat(eval_metrics)))
if report_fn:
report_fn(eval_metrics[metric_name], step)
def evaluate(
loop_hparams, planner_hparams, policy_dir, model_dir, eval_metrics_dir,
agent_type, eval_with_learner, log_every_steps, report_fn=None,
report_metric=None
):
"""Evaluate."""
if eval_with_learner:
assert agent_type == "policy"
if report_fn:
assert report_metric is not None
eval_metrics_writer = tf.summary.FileWriter(eval_metrics_dir)
kwargs = {}
if not eval_with_learner:
kwargs["eval_fn"] = make_eval_fn_with_agent(
agent_type, planner_hparams, model_dir, log_every_steps=log_every_steps
)
eval_metrics = rl_utils.evaluate_all_configs(
loop_hparams, policy_dir, **kwargs
)
rl_utils.summarize_metrics(eval_metrics_writer, eval_metrics, 0)
# Report metrics
if report_fn:
if report_metric == "mean_reward":
metric_name = rl_utils.get_metric_name(
sampling_temp=loop_hparams.eval_sampling_temps[0],
max_num_noops=loop_hparams.eval_max_num_noops,
clipped=False
)
report_fn(eval_metrics[metric_name], 0)
else:
report_fn(eval_metrics[report_metric], 0)
return eval_metrics
def evaluate(
loop_hparams, planner_hparams, policy_dir, model_dir, eval_metrics_dir,
agent_type, eval_mode, eval_with_learner, log_every_steps, debug_video_path,
num_debug_videos=1, random_starts_step_limit=None,
report_fn=None, report_metric=None
):
"""Evaluate."""
if eval_with_learner:
assert agent_type == "policy"
if report_fn:
assert report_metric is not None
eval_metrics_writer = tf.summary.FileWriter(eval_metrics_dir)
video_writers = ()
kwargs = {}
if eval_mode in ["agent_real", "agent_simulated"]:
if not eval_with_learner:
if debug_video_path:
tf.gfile.MakeDirs(debug_video_path)
video_writers = [
common_video.WholeVideoWriter( # pylint: disable=g-complex-comprehension
fps=10,
output_path=os.path.join(debug_video_path, "{}.avi".format(i)),
file_format="avi",
)
for i in range(num_debug_videos)
]
kwargs["eval_fn"] = make_eval_fn_with_agent(
agent_type, eval_mode, planner_hparams, model_dir,
log_every_steps=log_every_steps,
video_writers=video_writers,
random_starts_step_limit=random_starts_step_limit
)
eval_metrics = rl_utils.evaluate_all_configs(
loop_hparams, policy_dir, **kwargs
)
else:
eval_metrics = evaluate_world_model(
agent_type, loop_hparams, planner_hparams, model_dir, policy_dir,
random_starts_step_limit, debug_video_path, log_every_steps
)
rl_utils.summarize_metrics(eval_metrics_writer, eval_metrics, 0)
for video_writer in video_writers:
video_writer.finish_to_disk()
# Report metrics
if report_fn:
if report_metric == "mean_reward":
metric_name = rl_utils.get_metric_name(
sampling_temp=loop_hparams.eval_sampling_temps[0],
max_num_noops=loop_hparams.eval_max_num_noops,
clipped=False
)
report_fn(eval_metrics[metric_name], 0)
else:
report_fn(eval_metrics[report_metric], 0)
return eval_metrics
def evaluate(loop_hparams,
planner_hparams,
policy_dir,
model_dir,
eval_metrics_dir,
agent_type,
eval_with_learner,
log_every_steps,
debug_video_path,
report_fn=None,
report_metric=None):
"""Evaluate."""
if eval_with_learner:
assert agent_type == "policy"
if report_fn:
assert report_metric is not None
eval_metrics_writer = tf.summary.FileWriter(eval_metrics_dir)
kwargs = {}
if not eval_with_learner:
if debug_video_path:
video_writer = common_video.WholeVideoWriter(
fps=10, output_path=debug_video_path, file_format="avi")
else:
video_writer = None
kwargs["eval_fn"] = make_eval_fn_with_agent(
agent_type,
planner_hparams,
model_dir,
log_every_steps=log_every_steps,
video_writer=video_writer)
eval_metrics = rl_utils.evaluate_all_configs(loop_hparams, policy_dir,
**kwargs)
rl_utils.summarize_metrics(eval_metrics_writer, eval_metrics, 0)
if video_writer is not None:
video_writer.finish_to_disk()
# Report metrics
if report_fn:
if report_metric == "mean_reward":
metric_name = rl_utils.get_metric_name(
sampling_temp=loop_hparams.eval_sampling_temps[0],
max_num_noops=loop_hparams.eval_max_num_noops,
clipped=False)
report_fn(eval_metrics[metric_name], 0)
else:
report_fn(eval_metrics[report_metric], 0)
return eval_metrics
def train(hparams, output_dir, env_problem_name, report_fn=None):
"""Train."""
env_fn = initialize_env_specs(hparams, env_problem_name)
tf.logging.vlog(1, "HParams in trainer_model_free.train : %s",
misc_utils.pprint_hparams(hparams))
tf.logging.vlog(1, "Using hparams.base_algo: %s", hparams.base_algo)
learner = rl_utils.LEARNERS[hparams.base_algo](hparams.frame_stack_size,
output_dir,
output_dir,
total_num_epochs=1)
policy_hparams = trainer_lib.create_hparams(hparams.base_algo_params)
rl_utils.update_hparams_from_hparams(policy_hparams, hparams,
hparams.base_algo + "_")
tf.logging.vlog(1, "Policy HParams : %s",
misc_utils.pprint_hparams(policy_hparams))
total_steps = policy_hparams.epochs_num
tf.logging.vlog(2, "total_steps: %d", total_steps)
eval_every_epochs = policy_hparams.eval_every_epochs
tf.logging.vlog(2, "eval_every_epochs: %d", eval_every_epochs)
if eval_every_epochs == 0:
eval_every_epochs = total_steps
policy_hparams.eval_every_epochs = 0
metric_name = rl_utils.get_metric_name(
sampling_temp=hparams.eval_sampling_temps[0],
max_num_noops=hparams.eval_max_num_noops,
clipped=False)
tf.logging.vlog(1, "metric_name: %s", metric_name)
eval_metrics_dir = os.path.join(output_dir, "eval_metrics")
eval_metrics_dir = os.path.expanduser(eval_metrics_dir)
tf.gfile.MakeDirs(eval_metrics_dir)
eval_metrics_writer = tf.summary.FileWriter(eval_metrics_dir)
def evaluate_on_new_model(model_dir_path):
global step
eval_metrics = rl_utils.evaluate_all_configs(hparams, model_dir_path)
tf.logging.info("Agent eval metrics:\n{}".format(
pprint.pformat(eval_metrics)))
rl_utils.summarize_metrics(eval_metrics_writer, eval_metrics, step)
if report_fn:
report_fn(eval_metrics[metric_name], step)
step += 1
policy_hparams.epochs_num = total_steps
policy_hparams.save_models_every_epochs = eval_every_epochs
learner.train(env_fn,
policy_hparams,
simulated=False,
save_continuously=True,
epoch=0,
model_save_fn=evaluate_on_new_model)
def training_loop(hparams, output_dir, report_fn=None, report_metric=None):
"""Run the main training loop."""
if report_fn:
assert report_metric is not None
# Directories
subdirectories = [
"data", "tmp", "world_model", ("world_model", "debug_videos"),
"policy", "eval_metrics"
]
directories = setup_directories(output_dir, subdirectories)
epoch = -1
data_dir = directories["data"]
env = rl_utils.setup_env(
hparams, batch_size=hparams.real_batch_size,
max_num_noops=hparams.max_num_noops,
rl_env_max_episode_steps=hparams.rl_env_max_episode_steps
)
env.start_new_epoch(epoch, data_dir)
if hparams.wm_policy_param_sharing:
policy_model_dir = directories["world_model"]
else:
policy_model_dir = directories["policy"]
learner = rl_utils.LEARNERS[hparams.base_algo](
hparams.frame_stack_size, policy_model_dir,
policy_model_dir, hparams.epochs
)
# Timing log function
log_relative_time = make_relative_timing_fn()
# Per-epoch state
epoch_metrics = []
metrics = {}
# Collect data from the real environment.
tf.logging.info("Initial training of the policy in real environment.")
train_agent_real_env(env, learner, hparams, epoch)
metrics["mean_reward/train/clipped"] = rl_utils.compute_mean_reward(
env.current_epoch_rollouts(), clipped=True
)
tf.logging.info("Mean training reward (initial): {}".format(
metrics["mean_reward/train/clipped"]
))
env.generate_data(data_dir)
eval_metrics_writer = tf.summary.FileWriter(
directories["eval_metrics"]
)
world_model_steps_num = 0
for epoch in range(hparams.epochs):
log = make_log_fn(epoch, log_relative_time)
# Train world model
log("Training world model")
world_model_steps_num = train_world_model(
env, data_dir, directories["world_model"], hparams,
world_model_steps_num, epoch
)
# Train agent
log("Training policy in simulated environment.")
train_agent(env, learner, directories["world_model"], hparams, epoch)
env.start_new_epoch(epoch, data_dir)
# Train agent on real env (short)
log("Training policy in real environment.")
train_agent_real_env(env, learner, hparams, epoch)
if hparams.stop_loop_early:
return 0.0
env.generate_data(data_dir)
metrics = load_metrics(directories["eval_metrics"], epoch)
if metrics:
# Skip eval if metrics have already been written for this epoch. Otherwise
# we'd overwrite them with wrong data.
log("Metrics found for this epoch, skipping evaluation.")
else:
metrics["mean_reward/train/clipped"] = rl_utils.compute_mean_reward(
env.current_epoch_rollouts(), clipped=True
)
log("Mean training reward: {}".format(
metrics["mean_reward/train/clipped"]
))
eval_metrics = rl_utils.evaluate_all_configs(hparams, policy_model_dir)
log("Agent eval metrics:\n{}".format(pprint.pformat(eval_metrics)))
metrics.update(eval_metrics)
if hparams.eval_world_model:
debug_video_path = os.path.join(
directories["world_model", "debug_videos"],
"{}.avi".format(env.current_epoch)
)
wm_metrics = evaluate_world_model(
env, hparams, directories["world_model"], debug_video_path
)
log("World model eval metrics:\n{}".format(pprint.pformat(wm_metrics)))
metrics.update(wm_metrics)
rl_utils.summarize_metrics(eval_metrics_writer, metrics, epoch)
# Report metrics
if report_fn:
if report_metric == "mean_reward":
metric_name = rl_utils.get_metric_name(
sampling_temp=hparams.eval_sampling_temps[0],
max_num_noops=hparams.eval_max_num_noops,
clipped=False
)
report_fn(eval_metrics[metric_name], epoch)
else:
report_fn(eval_metrics[report_metric], epoch)
epoch_metrics.append(metrics)
# Return the evaluation metrics from the final epoch
return epoch_metrics[-1]
def train(hparams, output_dir, env_problem_name, report_fn=None):
"""Train."""
env_fn = initialize_env_specs(hparams, env_problem_name)
tf.logging.vlog(1, "HParams in trainer_model_free.train : %s",
misc_utils.pprint_hparams(hparams))
tf.logging.vlog(1, "Using hparams.base_algo: %s", hparams.base_algo)
learner = rl_utils.LEARNERS[hparams.base_algo](
hparams.frame_stack_size, output_dir, output_dir, total_num_epochs=1,
distributional_size=hparams.get("distributional_size", 1),
distributional_subscale=hparams.get("distributional_subscale", 0.04),
distributional_threshold=hparams.get("distributional_threshold", 0.0),
)
policy_hparams = trainer_lib.create_hparams(hparams.base_algo_params)
rl_utils.update_hparams_from_hparams(
policy_hparams, hparams, hparams.base_algo + "_"
)
tf.logging.vlog(1, "Policy HParams : %s",
misc_utils.pprint_hparams(policy_hparams))
# TODO(konradczechowski): remove base_algo dependance, when evaluation method
# will be decided
if hparams.base_algo == "ppo":
total_steps = policy_hparams.epochs_num
tf.logging.vlog(2, "total_steps: %d", total_steps)
eval_every_epochs = policy_hparams.eval_every_epochs
tf.logging.vlog(2, "eval_every_epochs: %d", eval_every_epochs)
if eval_every_epochs == 0:
eval_every_epochs = total_steps
policy_hparams.eval_every_epochs = 0
metric_name = rl_utils.get_metric_name(
sampling_temp=hparams.eval_sampling_temps[0],
max_num_noops=hparams.eval_max_num_noops,
clipped=False
)
tf.logging.vlog(1, "metric_name: %s", metric_name)
eval_metrics_dir = os.path.join(output_dir, "eval_metrics")
eval_metrics_dir = os.path.expanduser(eval_metrics_dir)
tf.gfile.MakeDirs(eval_metrics_dir)
eval_metrics_writer = tf.summary.FileWriter(eval_metrics_dir)
def evaluate_on_new_model(model_dir_path):
global step
eval_metrics = rl_utils.evaluate_all_configs(hparams, model_dir_path)
tf.logging.info(
"Agent eval metrics:\n{}".format(pprint.pformat(eval_metrics)))
rl_utils.summarize_metrics(eval_metrics_writer, eval_metrics, step)
if report_fn:
report_fn(eval_metrics[metric_name], step)
step += 1
policy_hparams.epochs_num = total_steps
policy_hparams.save_models_every_epochs = eval_every_epochs
else:
def evaluate_on_new_model(model_dir_path):
del model_dir_path
raise NotImplementedError(
"This function is currently implemented only for ppo")
learner.train(env_fn,
policy_hparams,
simulated=False,
save_continuously=True,
epoch=0,
model_save_fn=evaluate_on_new_model)
def train(hparams, output_dir, report_fn=None):
"""Train."""
hparams = initialize_env_specs(hparams)
tf.logging.vlog(1, "HParams in trainer_model_free.train : %s",
misc_utils.pprint_hparams(hparams))
tf.logging.vlog(1, "Using hparams.base_algo: %s", hparams.base_algo)
learner = rl_utils.LEARNERS[hparams.base_algo](hparams.frame_stack_size,
output_dir,
output_dir,
total_num_epochs=1)
policy_hparams = trainer_lib.create_hparams(hparams.base_algo_params)
rl_utils.update_hparams_from_hparams(policy_hparams, hparams,
hparams.base_algo + "_")
tf.logging.vlog(1, "Policy HParams : %s",
misc_utils.pprint_hparams(policy_hparams))
total_steps = policy_hparams.epochs_num
tf.logging.vlog(2, "total_steps: %d", total_steps)
eval_every_epochs = policy_hparams.eval_every_epochs
tf.logging.vlog(2, "eval_every_epochs: %d", eval_every_epochs)
if eval_every_epochs == 0:
eval_every_epochs = total_steps
policy_hparams.eval_every_epochs = 0
steps = list(range(eval_every_epochs, total_steps + 1, eval_every_epochs))
if not steps or steps[-1] < eval_every_epochs:
steps.append(eval_every_epochs)
tf.logging.vlog(1, "steps: [%s]", ",".join([str(s) for s in steps]))
metric_name = rl_utils.get_metric_name(
sampling_temp=hparams.eval_sampling_temps[0],
max_num_noops=hparams.eval_max_num_noops,
clipped=False)
tf.logging.vlog(1, "metric_name: %s", metric_name)
for i, step in enumerate(steps):
tf.logging.info("Starting training iteration [%d] for [%d] steps.", i,
step)
policy_hparams.epochs_num = step
learner.train(hparams.env_fn,
policy_hparams,
simulated=False,
save_continuously=True,
epoch=0)
tf.logging.info("Ended training iteration [%d] for [%d] steps.", i,
step)
eval_metrics = rl_utils.evaluate_all_configs(hparams, output_dir)
tf.logging.info("Agent eval metrics:\n{}".format(
pprint.pformat(eval_metrics)))
if report_fn:
report_fn(eval_metrics[metric_name], step)