def reward_fn_loader(
path: str, venv: vec_env.VecEnv) -> Iterator[common.RewardFn]:
"""Load a TensorFlow reward model, then convert it into a Callable."""
reward_model_loader = self.get(key)
with networks.make_session() as (_, sess):
reward_model = reward_model_loader(path, venv)
def reward_fn(obs: np.ndarray, actions: np.ndarray,
next_obs: np.ndarray,
steps: np.ndarray) -> np.ndarray:
"""Helper method computing reward for registered model."""
del steps
# TODO(adam): RewardFn should probably include dones?
dones = np.zeros(len(obs), dtype=np.bool)
transitions = types.Transitions(
obs=obs,
acts=actions,
next_obs=next_obs,
dones=dones,
infos=None,
)
fd = rewards.make_feed_dict([reward_model], transitions)
return sess.run(reward_model.reward, feed_dict=fd)
yield reward_fn
def plot_pm_reward(
styles: Iterable[str],
env_name: str,
discount: float,
models: Sequence[Tuple[str, str, str]],
data_root: str,
# Mesh parameters
pos_lim: float,
pos_density: int,
vel_lim: float,
act_lim: float,
density: int,
# Figure parameters
ncols: int,
cbar_kwargs: Mapping[str, Any],
log_dir: str,
fmt: str,
) -> xr.DataArray:
"""Entry-point into script to visualize a reward model for point mass."""
with stylesheets.setup_styles(styles):
env = gym.make(env_name)
venv = vec_env.DummyVecEnv([lambda: env])
goal = np.array([0.0])
rewards = {}
with networks.make_session():
for model_name, reward_type, reward_path in models:
reward_path = os.path.join(data_root, reward_path)
model = serialize.load_reward(reward_type, reward_path, venv,
discount)
reward = point_mass_analysis.evaluate_reward_model(
env,
model,
goal=goal,
pos_lim=pos_lim,
pos_density=pos_density,
vel_lim=vel_lim,
act_lim=act_lim,
density=density,
)
rewards[model_name] = reward
if len(rewards) == 1:
reward = next(iter(rewards.values()))
kwargs = {"col_wrap": ncols}
else:
reward = xr.Dataset(rewards).to_array("model")
kwargs = {"row": "Model"}
fig = point_mass_analysis.plot_reward(reward,
cbar_kwargs=cbar_kwargs,
**kwargs)
save_path = os.path.join(log_dir, "reward")
visualize.save_fig(save_path, fig, fmt=fmt)
return reward
def regress(
seed: int,
# Dataset
env_name: str,
discount: float,
# Target specification
target_reward_type: str,
target_reward_path: str,
# Model parameters
make_source: MakeModelFn,
source_init: bool,
make_trainer: MakeTrainerFn,
do_training: DoTrainingFn,
# Logging
log_dir: str,
checkpoint_interval: int,
) -> V:
"""Train a model on target and save the results, reporting training stats."""
# This venv is needed by serialize.load_reward, but is never stepped.
venv = vec_env.DummyVecEnv([lambda: gym.make(env_name)])
with networks.make_session() as (_, sess):
tf.random.set_random_seed(seed)
with tf.variable_scope("source") as model_scope:
model = make_source(venv)
with tf.variable_scope("target"):
target = serialize.load_reward(target_reward_type,
target_reward_path, venv, discount)
with tf.variable_scope("train") as train_scope:
trainer = make_trainer(model, model_scope, target)
# Do not initialize any variables from target, which have already been
# set during serialization.
init_vars = train_scope.global_variables()
if source_init:
init_vars += model_scope.global_variables()
sess.run(tf.initializers.variables(init_vars))
def callback(epoch: int) -> None:
if checkpoint_interval > 0 and epoch % checkpoint_interval == 0:
trainer.model.save(
os.path.join(log_dir, "checkpoints", f"{epoch:05d}"))
stats = do_training(target, trainer, callback)
# Trainer may wrap source, so save `trainer.model` not source directly
# (see e.g. RegressWrappedModel).
trainer.model.save(os.path.join(log_dir, "checkpoints", "final"))
with open(os.path.join(log_dir, "stats.pkl"), "wb") as f:
pickle.dump(stats, f)
return stats
def get_affine_from_models(env_name: str, paths: Iterable[str]):
"""Extract affine parameters from reward model."""
venv = vec_env.DummyVecEnv([lambda: gym.make(env_name)])
res = {}
with networks.make_session():
for path in paths:
model = serialize.load_reward(
"evaluating_rewards/RewardModel-v0",
os.path.join(path, "model"),
venv,
)
return model.models["wrapped"][0].get_weights()
return res
def loader(path: str, venv: VecEnv) -> Iterator[common.RewardFn]:
"""Load train (shaped) or test (not shaped) reward from path."""
del venv # Unused.
with networks.make_session() as (graph, sess):
net = reward_net.RewardNet.load(path)
reward = net.reward_output_train if shaped else net.reward_output_test
def rew_fn(
obs: np.ndarray,
act: np.ndarray,
next_obs: np.ndarray,
dones: np.ndarray,
) -> np.ndarray:
fd = {
net.obs_ph: obs,
net.act_ph: act,
net.next_obs_ph: next_obs,
net.done_ph: dones,
}
rew = sess.run(reward, feed_dict=fd)
assert rew.shape == (len(obs), )
return rew
yield rew_fn
def rollouts_and_policy(
_run,
_seed: int,
env_name: str,
total_timesteps: int,
*,
log_dir: str,
num_vec: int,
parallel: bool,
max_episode_steps: Optional[int],
normalize: bool,
normalize_kwargs: dict,
init_rl_kwargs: dict,
n_episodes_eval: int,
reward_type: Optional[str],
reward_path: Optional[str],
rollout_save_interval: int,
rollout_save_final: bool,
rollout_save_n_timesteps: Optional[int],
rollout_save_n_episodes: Optional[int],
policy_save_interval: int,
policy_save_final: bool,
init_tensorboard: bool,
) -> dict:
"""Trains an expert policy from scratch and saves the rollouts and policy.
Checkpoints:
At applicable training steps `step` (where step is either an integer or
"final"):
- Policies are saved to `{log_dir}/policies/{step}/`.
- Rollouts are saved to `{log_dir}/rollouts/{step}.pkl`.
Args:
env_name: The gym.Env name. Loaded as VecEnv.
total_timesteps: Number of training timesteps in `model.learn()`.
log_dir: The root directory to save metrics and checkpoints to.
num_vec: Number of environments in VecEnv.
parallel: If True, then use DummyVecEnv. Otherwise use SubprocVecEnv.
max_episode_steps: If not None, then environments are wrapped by
TimeLimit so that they have at most `max_episode_steps` steps per
episode.
normalize: If True, then rescale observations and reward.
normalize_kwargs: kwargs for `VecNormalize`.
init_rl_kwargs: kwargs for `init_rl`.
n_episodes_eval: The number of episodes to average over when calculating
the average ground truth reward return of the final policy.
reward_type: If provided, then load the serialized reward of this type,
wrapping the environment in this reward. This is useful to test
whether a reward model transfers. For more information, see
`imitation.rewards.serialize.load_reward`.
reward_path: A specifier, such as a path to a file on disk, used by
reward_type to load the reward model. For more information, see
`imitation.rewards.serialize.load_reward`.
rollout_save_interval: The number of training updates in between
intermediate rollout saves. If the argument is nonpositive, then
don't save intermediate updates.
rollout_save_final: If True, then save rollouts right after training is
finished.
rollout_save_n_timesteps: The minimum number of timesteps saved in every
file. Could be more than `rollout_save_n_timesteps` because
trajectories are saved by episode rather than by transition.
Must set exactly one of `rollout_save_n_timesteps`
and `rollout_save_n_episodes`.
rollout_save_n_episodes: The number of episodes saved in every
file. Must set exactly one of `rollout_save_n_timesteps` and
`rollout_save_n_episodes`.
policy_save_interval: The number of training updates between saves. Has
the same semantics are `rollout_save_interval`.
policy_save_final: If True, then save the policy right after training is
finished.
init_tensorboard: If True, then write tensorboard logs to {log_dir}/sb_tb
and "output/summary/...".
Returns:
The return value of `rollout_stats()` using the final policy.
"""
os.makedirs(log_dir, exist_ok=True)
sacred_util.build_sacred_symlink(log_dir, _run)
sample_until = rollout.make_sample_until(rollout_save_n_timesteps,
rollout_save_n_episodes)
eval_sample_until = rollout.min_episodes(n_episodes_eval)
with networks.make_session():
tf.logging.set_verbosity(tf.logging.INFO)
logger.configure(folder=osp.join(log_dir, "rl"),
format_strs=["tensorboard", "stdout"])
rollout_dir = osp.join(log_dir, "rollouts")
policy_dir = osp.join(log_dir, "policies")
os.makedirs(rollout_dir, exist_ok=True)
os.makedirs(policy_dir, exist_ok=True)
if init_tensorboard:
sb_tensorboard_dir = osp.join(log_dir, "sb_tb")
# Convert sacred's ReadOnlyDict to dict so we can modify on next line.
init_rl_kwargs = dict(init_rl_kwargs)
init_rl_kwargs["tensorboard_log"] = sb_tensorboard_dir
venv = util.make_vec_env(
env_name,
num_vec,
seed=_seed,
parallel=parallel,
log_dir=log_dir,
max_episode_steps=max_episode_steps,
)
log_callbacks = []
with contextlib.ExitStack() as stack:
if reward_type is not None:
reward_fn_ctx = load_reward(reward_type, reward_path, venv)
reward_fn = stack.enter_context(reward_fn_ctx)
venv = RewardVecEnvWrapper(venv, reward_fn)
log_callbacks.append(venv.log_callback)
tf.logging.info(
f"Wrapped env in reward {reward_type} from {reward_path}.")
vec_normalize = None
if normalize:
venv = vec_normalize = VecNormalize(venv, **normalize_kwargs)
policy = util.init_rl(venv, verbose=1, **init_rl_kwargs)
# Make callback to save intermediate artifacts during training.
step = 0
def callback(locals_: dict, _) -> bool:
nonlocal step
step += 1
policy = locals_["self"]
# TODO(adam): make logging frequency configurable
for callback in log_callbacks:
callback(sb_logger)
if rollout_save_interval > 0 and step % rollout_save_interval == 0:
save_path = osp.join(rollout_dir, f"{step}.pkl")
rollout.rollout_and_save(save_path, policy, venv,
sample_until)
if policy_save_interval > 0 and step % policy_save_interval == 0:
output_dir = os.path.join(policy_dir, f"{step:05d}")
serialize.save_stable_model(output_dir, policy,
vec_normalize)
policy.learn(total_timesteps, callback=callback)
# Save final artifacts after training is complete.
if rollout_save_final:
save_path = osp.join(rollout_dir, "final.pkl")
rollout.rollout_and_save(save_path, policy, venv, sample_until)
if policy_save_final:
output_dir = os.path.join(policy_dir, "final")
serialize.save_stable_model(output_dir, policy, vec_normalize)
# Final evaluation of expert policy.
trajs = rollout.generate_trajectories(policy, venv,
eval_sample_until)
stats = rollout.rollout_stats(trajs)
return stats
def wrapper(*args, **kwargs) -> Iterator[T]:
with networks.make_session():
yield fn(*args, **kwargs)
def train(
_run,
_seed: int,
env_name: str,
rollout_path: str,
n_expert_demos: Optional[int],
log_dir: str,
init_trainer_kwargs: dict,
total_timesteps: int,
n_episodes_eval: int,
init_tensorboard: bool,
checkpoint_interval: int,
) -> dict:
"""Train an adversarial-network-based imitation learning algorithm.
Plots (turn on using `plot_interval > 0`):
- Plot discriminator loss during discriminator training steps in blue and
discriminator loss during generator training steps in red.
- Plot the performance of the generator policy versus the performance of
a random policy. Also plot the performance of an expert policy if that is
provided in the arguments.
Checkpoints:
- DiscrimNets are saved to f"{log_dir}/checkpoints/{step}/discrim/",
where step is either the training epoch or "final".
- Generator policies are saved to
f"{log_dir}/checkpoints/{step}/gen_policy/".
Args:
_seed: Random seed.
env_name: The environment to train in.
rollout_path: Path to pickle containing list of Trajectories. Used as
expert demonstrations.
n_expert_demos: The number of expert trajectories to actually use
after loading them from `rollout_path`.
If None, then use all available trajectories.
If `n_expert_demos` is an `int`, then use exactly `n_expert_demos`
trajectories, erroring if there aren't enough trajectories. If there are
surplus trajectories, then use the
first `n_expert_demos` trajectories and drop the rest.
log_dir: Directory to save models and other logging to.
init_trainer_kwargs: Keyword arguments passed to `init_trainer`,
used to initialize the trainer.
total_timesteps: The number of transitions to sample from the environment
during training.
n_episodes_eval: The number of episodes to average over when calculating
the average episode reward of the imitation policy for return.
plot_interval: The number of epochs between each plot. If negative,
then plots are disabled. If zero, then only plot at the end of training.
n_plot_episodes: The number of episodes averaged over when
calculating the average episode reward of a policy for the performance
plots.
extra_episode_data_interval: Usually mean episode rewards are calculated
immediately before every plot. Set this parameter to a nonnegative number
to also add episode reward data points every
`extra_episodes_data_interval` epochs.
show_plots: Figures are always saved to `f"{log_dir}/plots/*.png"`. If
`show_plots` is True, then also show plots as they are created.
init_tensorboard: If True, then write tensorboard logs to `{log_dir}/sb_tb`.
checkpoint_interval: Save the discriminator and generator models every
`checkpoint_interval` epochs and after training is complete. If 0,
then only save weights after training is complete. If <0, then don't
save weights at all.
Returns:
A dictionary with two keys. "imit_stats" gives the return value of
`rollout_stats()` on rollouts test-reward-wrapped
environment, using the final policy (remember that the ground-truth reward
can be recovered from the "monitor_return" key). "expert_stats" gives the
return value of `rollout_stats()` on the expert demonstrations loaded from
`rollout_path`.
"""
total_timesteps = int(total_timesteps)
tf.logging.info("Logging to %s", log_dir)
os.makedirs(log_dir, exist_ok=True)
sacred_util.build_sacred_symlink(log_dir, _run)
# Calculate stats for expert rollouts. Used for plot and return value.
expert_trajs = types.load(rollout_path)
if n_expert_demos is not None:
assert len(expert_trajs) >= n_expert_demos
expert_trajs = expert_trajs[:n_expert_demos]
expert_stats = rollout.rollout_stats(expert_trajs)
with networks.make_session():
if init_tensorboard:
sb_tensorboard_dir = osp.join(log_dir, "sb_tb")
kwargs = init_trainer_kwargs
kwargs["init_rl_kwargs"] = kwargs.get("init_rl_kwargs", {})
kwargs["init_rl_kwargs"]["tensorboard_log"] = sb_tensorboard_dir
trainer = init_trainer(env_name,
expert_trajs,
seed=_seed,
log_dir=log_dir,
**init_trainer_kwargs)
def callback(epoch):
if checkpoint_interval > 0 and epoch % checkpoint_interval == 0:
save(trainer,
os.path.join(log_dir, "checkpoints", f"{epoch:05d}"))
trainer.train(total_timesteps, callback)
# Save final artifacts.
if checkpoint_interval >= 0:
save(trainer, os.path.join(log_dir, "checkpoints", "final"))
# Final evaluation of imitation policy.
results = {}
sample_until_eval = rollout.min_episodes(n_episodes_eval)
trajs = rollout.generate_trajectories(trainer.gen_policy,
trainer.venv_test,
sample_until=sample_until_eval)
results["imit_stats"] = rollout.rollout_stats(trajs)
results["expert_stats"] = expert_stats
return results
def train(
_run,
_seed: int,
algorithm: str,
env_name: str,
num_vec: int,
parallel: bool,
max_episode_steps: Optional[int],
rollout_path: str,
n_expert_demos: Optional[int],
log_dir: str,
total_timesteps: int,
n_episodes_eval: int,
init_tensorboard: bool,
checkpoint_interval: int,
init_rl_kwargs: Mapping,
algorithm_kwargs: Mapping[str, Mapping],
discrim_net_kwargs: Mapping[str, Mapping],
) -> dict:
"""Train an adversarial-network-based imitation learning algorithm.
Checkpoints:
- DiscrimNets are saved to `f"{log_dir}/checkpoints/{step}/discrim/"`,
where step is either the training epoch or "final".
- Generator policies are saved to `f"{log_dir}/checkpoints/{step}/gen_policy/"`.
Args:
_seed: Random seed.
algorithm: A case-insensitive string determining which adversarial imitation
learning algorithm is executed. Either "airl" or "gail".
env_name: The environment to train in.
num_vec: Number of `gym.Env` to vectorize.
parallel: Whether to use "true" parallelism. If True, then use `SubProcVecEnv`.
Otherwise, use `DummyVecEnv` which steps through environments serially.
max_episode_steps: If not None, then a TimeLimit wrapper is applied to each
environment to artificially limit the maximum number of timesteps in an
episode.
rollout_path: Path to pickle containing list of Trajectories. Used as
expert demonstrations.
n_expert_demos: The number of expert trajectories to actually use
after loading them from `rollout_path`.
If None, then use all available trajectories.
If `n_expert_demos` is an `int`, then use exactly `n_expert_demos`
trajectories, erroring if there aren't enough trajectories. If there are
surplus trajectories, then use the first `n_expert_demos` trajectories and
drop the rest.
log_dir: Directory to save models and other logging to.
total_timesteps: The number of transitions to sample from the environment
during training.
n_episodes_eval: The number of episodes to average over when calculating
the average episode reward of the imitation policy for return.
init_tensorboard: If True, then write tensorboard logs to `{log_dir}/sb_tb`.
checkpoint_interval: Save the discriminator and generator models every
`checkpoint_interval` epochs and after training is complete. If 0,
then only save weights after training is complete. If <0, then don't
save weights at all.
init_rl_kwargs: Keyword arguments for `init_rl`, the RL algorithm initialization
utility function.
algorithm_kwargs: Keyword arguments for the `GAIL` or `AIRL` constructor
that can apply to either constructor. Unlike a regular kwargs argument, this
argument can only have the following keys: "shared", "airl", and "gail".
`algorithm_kwargs["airl"]`, if it is provided, is a kwargs `Mapping` passed
to the `AIRL` constructor when `algorithm == "airl"`. Likewise
`algorithm_kwargs["gail"]` is passed to the `GAIL` constructor when
`algorithm == "gail"`. `algorithm_kwargs["shared"]`, if provided, is passed
to both the `AIRL` and `GAIL` constructors. Duplicate keyword argument keys
between `algorithm_kwargs["shared"]` and `algorithm_kwargs["airl"]` (or
"gail") leads to an error.
discrim_net_kwargs: Keyword arguments for the `DiscrimNet` constructor. Unlike a
regular kwargs argument, this argument can only have the following keys:
"shared", "airl", "gail". These keys have the same meaning as they do in
`algorithm_kwargs`.
Returns:
A dictionary with two keys. "imit_stats" gives the return value of
`rollout_stats()` on rollouts test-reward-wrapped environment, using the final
policy (remember that the ground-truth reward can be recovered from the
"monitor_return" key). "expert_stats" gives the return value of
`rollout_stats()` on the expert demonstrations loaded from `rollout_path`.
"""
assert os.path.exists(rollout_path)
total_timesteps = int(total_timesteps)
tf.logging.info("Logging to %s", log_dir)
logger.configure(log_dir, ["tensorboard", "stdout"])
os.makedirs(log_dir, exist_ok=True)
sacred_util.build_sacred_symlink(log_dir, _run)
expert_trajs = types.load(rollout_path)
if n_expert_demos is not None:
assert len(expert_trajs) >= n_expert_demos
expert_trajs = expert_trajs[:n_expert_demos]
expert_transitions = rollout.flatten_trajectories(expert_trajs)
with networks.make_session():
if init_tensorboard:
tensorboard_log = osp.join(log_dir, "sb_tb")
else:
tensorboard_log = None
venv = util.make_vec_env(
env_name,
num_vec,
seed=_seed,
parallel=parallel,
log_dir=log_dir,
max_episode_steps=max_episode_steps,
)
# TODO(shwang): Let's get rid of init_rl later on?
# It's really just a stub function now.
gen_policy = util.init_rl(venv,
verbose=1,
tensorboard_log=tensorboard_log,
**init_rl_kwargs)
# Convert Sacred's ReadOnlyDict to dict so we can modify it.
allowed_keys = {"shared", "gail", "airl"}
assert discrim_net_kwargs.keys() <= allowed_keys
assert algorithm_kwargs.keys() <= allowed_keys
discrim_kwargs_shared = discrim_net_kwargs.get("shared", {})
discrim_kwargs_algo = discrim_net_kwargs.get(algorithm, {})
final_discrim_kwargs = dict(**discrim_kwargs_shared,
**discrim_kwargs_algo)
algorithm_kwargs_shared = algorithm_kwargs.get("shared", {})
algorithm_kwargs_algo = algorithm_kwargs.get(algorithm, {})
final_algorithm_kwargs = dict(
**algorithm_kwargs_shared,
**algorithm_kwargs_algo,
)
if algorithm.lower() == "gail":
algo_cls = adversarial.GAIL
elif algorithm.lower() == "airl":
algo_cls = adversarial.AIRL
else:
raise ValueError(f"Invalid value algorithm={algorithm}.")
trainer = algo_cls(
venv=venv,
expert_data=expert_transitions,
gen_policy=gen_policy,
log_dir=log_dir,
discrim_kwargs=final_discrim_kwargs,
**final_algorithm_kwargs,
)
def callback(epoch):
if checkpoint_interval > 0 and epoch % checkpoint_interval == 0:
save(trainer,
os.path.join(log_dir, "checkpoints", f"{epoch:05d}"))
trainer.train(total_timesteps, callback)
# Save final artifacts.
if checkpoint_interval >= 0:
save(trainer, os.path.join(log_dir, "checkpoints", "final"))
# Final evaluation of imitation policy.
results = {}
sample_until_eval = rollout.min_episodes(n_episodes_eval)
trajs = rollout.generate_trajectories(trainer.gen_policy,
trainer.venv_test,
sample_until=sample_until_eval)
results["expert_stats"] = rollout.rollout_stats(expert_trajs)
results["imit_stats"] = rollout.rollout_stats(trajs)
return results
