""" Proximal Policy Optimization (PPO) ================================== This file defines the distributed Trainer class for proximal policy optimization. See `ppo_[tf|torch]_policy.py` for the definition of the policy loss. Detailed documentation: https://docs.ray.io/en/master/rllib-algorithms.html#ppo """ import logging from typing import Type from ray.rllib.agents import with_common_config from ray.rllib.agents.ppo.ppo_tf_policy import PPOTFPolicy from ray.rllib.agents.trainer import Trainer from ray.rllib.evaluation.worker_set import WorkerSet from ray.rllib.execution.rollout_ops import ( ParallelRollouts, ConcatBatches, StandardizeFields, SelectExperiences, ) from ray.rllib.execution.train_ops import TrainOneStep, MultiGPUTrainOneStep from ray.rllib.execution.metric_ops import StandardMetricsReporting from ray.rllib.policy.policy import Policy from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID from ray.rllib.utils.annotations import override from ray.rllib.utils.deprecation import DEPRECATED_VALUE from ray.rllib.utils.metrics.learner_info import LEARNER_INFO, LEARNER_STATS_KEY from ray.rllib.utils.typing import TrainerConfigDict from ray.util.iter import LocalIterator logger = logging.getLogger(__name__) # fmt: off # __sphinx_doc_begin__ # Adds the following updates to the (base) `Trainer` config in # rllib/agents/trainer.py (`COMMON_CONFIG` dict). DEFAULT_CONFIG = with_common_config({ # Should use a critic as a baseline (otherwise don't use value baseline; # required for using GAE). "use_critic": True, # If true, use the Generalized Advantage Estimator (GAE) # with a value function, see https://arxiv.org/pdf/1506.02438.pdf. "use_gae": True, # The GAE (lambda) parameter. "lambda": 1.0, # Initial coefficient for KL divergence. "kl_coeff": 0.2, # Size of batches collected from each worker. "rollout_fragment_length": 200, # Number of timesteps collected for each SGD round. This defines the size # of each SGD epoch. "train_batch_size": 4000, # Total SGD batch size across all devices for SGD. This defines the # minibatch size within each epoch. "sgd_minibatch_size": 128, # Whether to shuffle sequences in the batch when training (recommended). "shuffle_sequences": True, # Number of SGD iterations in each outer loop (i.e., number of epochs to # execute per train batch). "num_sgd_iter": 30, # Stepsize of SGD. "lr": 5e-5, # Learning rate schedule. "lr_schedule": None, # Coefficient of the value function loss. IMPORTANT: you must tune this if # you set vf_share_layers=True inside your model's config. "vf_loss_coeff": 1.0, "model": { # Share layers for value function. If you set this to True, it's # important to tune vf_loss_coeff. "vf_share_layers": False, }, # Coefficient of the entropy regularizer. "entropy_coeff": 0.0, # Decay schedule for the entropy regularizer. "entropy_coeff_schedule": None, # PPO clip parameter. "clip_param": 0.3, # Clip param for the value function. Note that this is sensitive to the # scale of the rewards. If your expected V is large, increase this. "vf_clip_param": 10.0, # If specified, clip the global norm of gradients by this amount. "grad_clip": None, # Target value for KL divergence. "kl_target": 0.01, # Whether to rollout "complete_episodes" or "truncate_episodes". "batch_mode": "truncate_episodes", # Which observation filter to apply to the observation. "observation_filter": "NoFilter", # Deprecated keys: # Share layers for value function. If you set this to True, it's important # to tune vf_loss_coeff. # Use config.model.vf_share_layers instead. "vf_share_layers": DEPRECATED_VALUE, }) # __sphinx_doc_end__ # fmt: on class UpdateKL: """Callback to update the KL based on optimization info. This is used inside the execution_plan function. The Policy must define a `update_kl` method for this to work. This is achieved for PPO via a Policy mixin class (which adds the `update_kl` method), defined in ppo_[tf|torch]_policy.py. """ def __init__(self, workers): self.workers = workers def __call__(self, fetches): def update(pi, pi_id): assert LEARNER_STATS_KEY not in fetches, ( "{} should be nested under policy id key".format(LEARNER_STATS_KEY), fetches, ) if pi_id in fetches: kl = fetches[pi_id][LEARNER_STATS_KEY].get("kl") assert kl is not None, (fetches, pi_id) # Make the actual `Policy.update_kl()` call. pi.update_kl(kl) else: logger.warning("No data for {}, not updating kl".format(pi_id)) # Update KL on all trainable policies within the local (trainer) # Worker. self.workers.local_worker().foreach_policy_to_train(update) def warn_about_bad_reward_scales(config, result): if result["policy_reward_mean"]: return result # Punt on handling multiagent case. # Warn about excessively high VF loss. learner_info = result["info"][LEARNER_INFO] if DEFAULT_POLICY_ID in learner_info: scaled_vf_loss = ( config["vf_loss_coeff"] * learner_info[DEFAULT_POLICY_ID][LEARNER_STATS_KEY]["vf_loss"] ) policy_loss = learner_info[DEFAULT_POLICY_ID][LEARNER_STATS_KEY]["policy_loss"] if config.get("model", {}).get("vf_share_layers") and scaled_vf_loss > 100: logger.warning( "The magnitude of your value function loss is extremely large " "({}) compared to the policy loss ({}). This can prevent the " "policy from learning. Consider scaling down the VF loss by " "reducing vf_loss_coeff, or disabling vf_share_layers.".format( scaled_vf_loss, policy_loss ) ) # Warn about bad clipping configs if config["vf_clip_param"] <= 0: rew_scale = float("inf") else: rew_scale = round( abs(result["episode_reward_mean"]) / config["vf_clip_param"], 0 ) if rew_scale > 200: logger.warning( "The magnitude of your environment rewards are more than " "{}x the scale of `vf_clip_param`. ".format(rew_scale) + "This means that it will take more than " "{} iterations for your value ".format(rew_scale) + "function to converge. If this is not intended, consider " "increasing `vf_clip_param`." ) return result class PPOTrainer(Trainer): @classmethod @override(Trainer) def get_default_config(cls) -> TrainerConfigDict: return DEFAULT_CONFIG @override(Trainer) def validate_config(self, config: TrainerConfigDict) -> None: """Validates the Trainer's config dict. Args: config (TrainerConfigDict): The Trainer's config to check. Raises: ValueError: In case something is wrong with the config. """ # Call super's validation method. super().validate_config(config) if isinstance(config["entropy_coeff"], int): config["entropy_coeff"] = float(config["entropy_coeff"]) if config["entropy_coeff"] < 0.0: raise DeprecationWarning("entropy_coeff must be >= 0.0") # SGD minibatch size must be smaller than train_batch_size (b/c # we subsample a batch of `sgd_minibatch_size` from the train-batch for # each `sgd_num_iter`). # Note: Only check this if `train_batch_size` > 0 (DDPPO sets this # to -1 to auto-calculate the actual batch size later). if ( config["train_batch_size"] > 0 and config["sgd_minibatch_size"] > config["train_batch_size"] ): raise ValueError( "`sgd_minibatch_size` ({}) must be <= " "`train_batch_size` ({}).".format( config["sgd_minibatch_size"], config["train_batch_size"] ) ) # Check for mismatches between `train_batch_size` and # `rollout_fragment_length` and auto-adjust `rollout_fragment_length` # if necessary. # Note: Only check this if `train_batch_size` > 0 (DDPPO sets this # to -1 to auto-calculate the actual batch size later). num_workers = config["num_workers"] or 1 calculated_min_rollout_size = ( num_workers * config["num_envs_per_worker"] * config["rollout_fragment_length"] ) if ( config["train_batch_size"] > 0 and config["train_batch_size"] % calculated_min_rollout_size != 0 ): new_rollout_fragment_length = config["train_batch_size"] // ( num_workers * config["num_envs_per_worker"] ) logger.warning( "`train_batch_size` ({}) cannot be achieved with your other " "settings (num_workers={} num_envs_per_worker={} " "rollout_fragment_length={})! Auto-adjusting " "`rollout_fragment_length` to {}.".format( config["train_batch_size"], config["num_workers"], config["num_envs_per_worker"], config["rollout_fragment_length"], new_rollout_fragment_length, ) ) config["rollout_fragment_length"] = new_rollout_fragment_length # Episodes may only be truncated (and passed into PPO's # `postprocessing_fn`), iff generalized advantage estimation is used # (value function estimate at end of truncated episode to estimate # remaining value). if config["batch_mode"] == "truncate_episodes" and not config["use_gae"]: raise ValueError( "Episode truncation is not supported without a value " "function (to estimate the return at the end of the truncated" " trajectory). Consider setting " "batch_mode=complete_episodes." ) # Multi-agent mode and multi-GPU optimizer. if config["multiagent"]["policies"] and not config["simple_optimizer"]: logger.info( "In multi-agent mode, policies will be optimized sequentially" " by the multi-GPU optimizer. Consider setting " "simple_optimizer=True if this doesn't work for you." ) @override(Trainer) def get_default_policy_class(self, config: TrainerConfigDict) -> Type[Policy]: if config["framework"] == "torch": from ray.rllib.agents.ppo.ppo_torch_policy import PPOTorchPolicy return PPOTorchPolicy else: return PPOTFPolicy @staticmethod @override(Trainer) def execution_plan( workers: WorkerSet, config: TrainerConfigDict, **kwargs ) -> LocalIterator[dict]: assert ( len(kwargs) == 0 ), "PPO execution_plan does NOT take any additional parameters" rollouts = ParallelRollouts(workers, mode="bulk_sync") # Collect batches for the trainable policies. rollouts = rollouts.for_each( SelectExperiences(local_worker=workers.local_worker()) ) # Concatenate the SampleBatches into one. rollouts = rollouts.combine( ConcatBatches( min_batch_size=config["train_batch_size"], count_steps_by=config["multiagent"]["count_steps_by"], ) ) # Standardize advantages. rollouts = rollouts.for_each(StandardizeFields(["advantages"])) # Perform one training step on the combined + standardized batch. if config["simple_optimizer"]: train_op = rollouts.for_each( TrainOneStep( workers, num_sgd_iter=config["num_sgd_iter"], sgd_minibatch_size=config["sgd_minibatch_size"], ) ) else: train_op = rollouts.for_each( MultiGPUTrainOneStep( workers=workers, sgd_minibatch_size=config["sgd_minibatch_size"], num_sgd_iter=config["num_sgd_iter"], num_gpus=config["num_gpus"], _fake_gpus=config["_fake_gpus"], ) ) # Update KL after each round of training. train_op = train_op.for_each(lambda t: t[1]).for_each(UpdateKL(workers)) # Warn about bad reward scales and return training metrics. return StandardMetricsReporting(train_op, workers, config).for_each( lambda result: warn_about_bad_reward_scales(config, result) )