def test_train_disc_small_expert_data_warning(tmpdir, _algorithm_cls): logger.configure(tmpdir, ["tensorboard", "stdout"]) venv = util.make_vec_env( "CartPole-v1", n_envs=2, parallel=_parallel, ) gen_algo = util.init_rl(venv, verbose=1) small_data = rollout.generate_transitions(gen_algo, venv, n_timesteps=20) with pytest.raises(ValueError, match="Transitions.*expert_batch_size"): _algorithm_cls( venv=venv, expert_data=small_data, expert_batch_size=21, gen_algo=gen_algo, log_dir=tmpdir, ) with pytest.raises(ValueError, match="expert_batch_size.*positive"): _algorithm_cls( venv=venv, expert_data=small_data, expert_batch_size=-1, gen_algo=gen_algo, log_dir=tmpdir, )
def trainer(_algorithm_cls, _parallel: bool, tmpdir: str, _convert_dataset: bool): logger.configure(tmpdir, ["tensorboard", "stdout"]) trajs = types.load( "tests/data/expert_models/cartpole_0/rollouts/final.pkl") if _convert_dataset: trans = rollout.flatten_trajectories(trajs) expert_data = datasets.TransitionsDictDatasetAdaptor(trans) else: expert_data = rollout.flatten_trajectories(trajs) venv = util.make_vec_env( "CartPole-v1", n_envs=2, parallel=_parallel, log_dir=tmpdir, ) gen_policy = util.init_rl(venv, verbose=1) return _algorithm_cls( venv=venv, expert_data=expert_data, gen_policy=gen_policy, log_dir=tmpdir, )
def test_density_trainer_smoke(): # tests whether density trainer runs, not whether it's good # (it's actually really poor) env_name = "Pendulum-v0" rollout_path = "tests/data/expert_models/pendulum_0/rollouts/final.pkl" rollouts = types.load(rollout_path)[:2] env = util.make_vec_env(env_name, 2) imitation_trainer = util.init_rl(env) density_trainer = DensityTrainer( env, rollouts=rollouts, imitation_trainer=imitation_trainer, density_type=STATE_ACTION_DENSITY, is_stationary=False, kernel="gaussian", ) density_trainer.train_policy(n_timesteps=2) density_trainer.test_policy(n_trajectories=2)
def test_train_disc_small_expert_data_warning(tmpdir, _algorithm_cls): logger.configure(tmpdir, ["tensorboard", "stdout"]) venv = util.make_vec_env( "CartPole-v1", n_envs=2, parallel=_parallel, log_dir=tmpdir, ) gen_algo = util.init_rl(venv, verbose=1) small_data = rollout.generate_transitions(gen_algo, venv, n_timesteps=20) with pytest.warns(RuntimeWarning, match="discriminator batch size"): _algorithm_cls( venv=venv, expert_data=small_data, gen_algo=gen_algo, log_dir=tmpdir, )
def trainer( _algorithm_cls, _parallel: bool, tmpdir: str, _convert_dataset: bool, expert_batch_size: int, expert_transitions: types.Transitions, ): logger.configure(tmpdir, ["tensorboard", "stdout"]) if _convert_dataset: expert_data = th_data.DataLoader( expert_transitions, batch_size=expert_batch_size, collate_fn=types.transitions_collate_fn, shuffle=True, drop_last=True, ) else: expert_data = expert_transitions venv = util.make_vec_env( "CartPole-v1", n_envs=2, parallel=_parallel, log_dir=tmpdir, ) gen_algo = util.init_rl(venv, verbose=1) trainer = _algorithm_cls( venv=venv, expert_data=expert_data, expert_batch_size=expert_batch_size, gen_algo=gen_algo, log_dir=tmpdir, ) try: yield trainer finally: venv.close()
def test_density_trainer(density_type, is_stationary): env_name = "Pendulum-v0" rollout_path = "tests/data/expert_models/pendulum_0/rollouts/final.pkl" rollouts = types.load(rollout_path) env = util.make_vec_env(env_name, 2) imitation_trainer = util.init_rl(env) density_trainer = DensityTrainer( env, rollouts=rollouts, imitation_trainer=imitation_trainer, density_type=density_type, is_stationary=is_stationary, kernel="gaussian", ) novice_stats = density_trainer.test_policy() density_trainer.train_policy(2000) good_stats = density_trainer.test_policy() # Novice is bad assert novice_stats["return_mean"] < -500 # Density is also pretty bad, but shouldn't make things more than 50% worse. # It would be nice to have a less flaky/more meaningful test here. assert good_stats["return_mean"] > 1.5 * novice_stats["return_mean"]
def rand_policy(venv): return util.init_rl(venv)
def init_trainer( env_name: str, expert_trajectories: Sequence[types.Trajectory], *, log_dir: str, seed: int = 0, use_gail: bool = False, num_vec: int = 8, parallel: bool = False, max_episode_steps: Optional[int] = None, scale: bool = True, airl_entropy_weight: float = 1.0, discrim_kwargs: dict = {}, reward_kwargs: dict = {}, trainer_kwargs: dict = {}, init_rl_kwargs: dict = {}, ): """Builds an AdversarialTrainer, ready to be trained on expert demonstrations. Args: env_name: The string id of a gym environment. expert_trajectories: Demonstrations from expert. seed: Random seed. log_dir: Directory for logging output. Will generate a unique sub-directory within this directory for all output. use_gail: If True, then train using GAIL. If False, then train using AIRL. num_vec: The number of vectorized environments. parallel: If True, then use SubprocVecEnv; otherwise, DummyVecEnv. max_episode_steps: If specified, wraps VecEnv in TimeLimit wrapper with this episode length before returning. policy_dir: The directory containing the pickled experts for generating rollouts. scale: If True, then scale input Tensors to the interval [0, 1]. airl_entropy_weight: Only applicable for AIRL. The `entropy_weight` argument of `DiscrimNetAIRL.__init__`. trainer_kwargs: Arguments for the Trainer constructor. reward_kwargs: Arguments for the `*RewardNet` constructor. discrim_kwargs: Arguments for the `DiscrimNet*` constructor. init_rl_kwargs: Keyword arguments passed to `init_rl`, used to initialize the RL algorithm. """ logger.configure(folder=log_dir, format_strs=["tensorboard", "stdout"]) env = util.make_vec_env( env_name, num_vec, seed=seed, parallel=parallel, log_dir=log_dir, max_episode_steps=max_episode_steps, ) gen_policy = util.init_rl(env, verbose=1, **init_rl_kwargs) if use_gail: discrim = discrim_net.DiscrimNetGAIL(env.observation_space, env.action_space, scale=scale, **discrim_kwargs) else: rn = BasicShapedRewardNet(env.observation_space, env.action_space, scale=scale, **reward_kwargs) discrim = discrim_net.DiscrimNetAIRL( rn, entropy_weight=airl_entropy_weight, **discrim_kwargs) expert_demos = rollout.flatten_trajectories(expert_trajectories) trainer = AdversarialTrainer(env, gen_policy, discrim, expert_demos, log_dir=log_dir, **trainer_kwargs) return trainer
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 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, gen_batch_size: 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 round 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` rounds and after training is complete. If 0, then only save weights after training is complete. If <0, then don't save weights at all. gen_batch_size: Batch size for generator updates. Sacred automatically uses this to calculate `n_steps` in `init_rl_kwargs`. In the script body, this is only used in sanity checks. 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`. """ if gen_batch_size % num_vec != 0: raise ValueError( f"num_vec={num_vec} must evenly divide gen_batch_size={gen_batch_size}." ) allowed_keys = {"shared", "gail", "airl"} if not discrim_net_kwargs.keys() <= allowed_keys: raise ValueError( f"Invalid discrim_net_kwargs.keys()={discrim_net_kwargs.keys()}. " f"Allowed keys: {allowed_keys}" ) if not algorithm_kwargs.keys() <= allowed_keys: raise ValueError( f"Invalid discrim_net_kwargs.keys()={algorithm_kwargs.keys()}. " f"Allowed keys: {allowed_keys}" ) if not os.path.exists(rollout_path): raise ValueError(f"File at rollout_path={rollout_path} does not exist.") expert_trajs = types.load(rollout_path) if n_expert_demos is not None: if not len(expert_trajs) >= n_expert_demos: raise ValueError( f"Want to use n_expert_demos={n_expert_demos} trajectories, but only " f"{len(expert_trajs)} are available via {rollout_path}." ) expert_trajs = expert_trajs[:n_expert_demos] expert_transitions = rollout.flatten_trajectories(expert_trajs) total_timesteps = int(total_timesteps) 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) venv = util.make_vec_env( env_name, num_vec, seed=_seed, parallel=parallel, log_dir=log_dir, max_episode_steps=max_episode_steps, ) # if init_tensorboard: # tensorboard_log = osp.join(log_dir, "sb_tb") # else: # tensorboard_log = None gen_algo = util.init_rl( # FIXME(sam): ignoring tensorboard_log is a hack to prevent SB3 from # re-configuring the logger (SB3 issue #109). See init_rl() for details. # TODO(shwang): Let's get rid of init_rl after SB3 issue #109 is fixed? # Besides sidestepping #109, init_rl is just a stub function. venv, **init_rl_kwargs, ) 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_algo=gen_algo, log_dir=log_dir, discrim_kwargs=final_discrim_kwargs, **final_algorithm_kwargs, ) def callback(round_num): if checkpoint_interval > 0 and round_num % checkpoint_interval == 0: save(trainer, os.path.join(log_dir, "checkpoints", f"{round_num: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_algo, trainer.venv_train_norm, sample_until=sample_until_eval ) results["expert_stats"] = rollout.rollout_stats(expert_trajs) results["imit_stats"] = rollout.rollout_stats(trajs) return results