""" SlateQ (Reinforcement Learning for Recommendation) ================================================== This file defines the trainer class for the SlateQ algorithm from the `"Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology" `_ paper. See `slateq_torch_policy.py` for the definition of the policy. Currently, only PyTorch is supported. The algorithm is written and tested for Google's RecSim environment (https://github.com/google-research/recsim). """ import logging from typing import List, Type from ray.rllib.agents.slateq.slateq_tf_policy import SlateQTFPolicy from ray.rllib.agents.slateq.slateq_torch_policy import SlateQTorchPolicy from ray.rllib.agents.trainer import Trainer, with_common_config from ray.rllib.evaluation.worker_set import WorkerSet from ray.rllib.execution.concurrency_ops import Concurrently from ray.rllib.execution.metric_ops import StandardMetricsReporting from ray.rllib.execution.replay_ops import Replay, StoreToReplayBuffer from ray.rllib.execution.rollout_ops import ParallelRollouts from ray.rllib.execution.train_ops import ( MultiGPUTrainOneStep, TrainOneStep, UpdateTargetNetwork, ) from ray.rllib.policy.policy import Policy from ray.rllib.utils.annotations import override from ray.rllib.utils.deprecation import DEPRECATED_VALUE from ray.rllib.utils.typing import TrainerConfigDict from ray.util.iter import LocalIterator from ray.rllib.utils.replay_buffers.replay_buffer import validate_buffer_config logger = logging.getLogger(__name__) # fmt: off # __sphinx_doc_begin__ DEFAULT_CONFIG = with_common_config({ # === Model === # Dense-layer setup for each the n (document) candidate Q-network stacks. "fcnet_hiddens_per_candidate": [256, 32], # === Exploration Settings === "exploration_config": { # The Exploration class to use. # Must be SlateEpsilonGreedy or SlateSoftQ to handle the problem that # the action space of the policy is different from the space used inside # the exploration component. # E.g.: action_space=MultiDiscrete([5, 5]) <- slate-size=2, num-docs=5, # but action distribution is Categorical(5*4) -> all possible unique slates. "type": "SlateEpsilonGreedy", "warmup_timesteps": 20000, "epsilon_timesteps": 250000, "final_epsilon": 0.01, }, # Switch to greedy actions in evaluation workers. "evaluation_config": { "explore": False, }, # Minimum env steps to optimize for per train call. This value does # not affect learning, only the length of iterations. "timesteps_per_iteration": 1000, # Update the target network every `target_network_update_freq` steps. "target_network_update_freq": 3200, # Update the target by \tau * policy + (1-\tau) * target_policy. "tau": 1.0, # If True, use huber loss instead of squared loss for critic network # Conventionally, no need to clip gradients if using a huber loss "use_huber": False, # Threshold of the huber loss. "huber_threshold": 1.0, # === Replay buffer === # Size of the replay buffer. Note that if async_updates is set, then # each worker will have a replay buffer of this size. "buffer_size": DEPRECATED_VALUE, "replay_buffer_config": { "type": "MultiAgentReplayBuffer", "capacity": 100000, }, # The number of contiguous environment steps to replay at once. This may # be set to greater than 1 to support recurrent models. "replay_sequence_length": 1, # Whether to LZ4 compress observations "compress_observations": False, # If set, this will fix the ratio of replayed from a buffer and learned on # timesteps to sampled from an environment and stored in the replay buffer # timesteps. Otherwise, the replay will proceed at the native ratio # determined by (train_batch_size / rollout_fragment_length). "training_intensity": None, # === Optimization === # Learning rate for RMSprop optimizer for the q-model. "lr": 0.00025, # Learning rate schedule. # In the format of [[timestep, value], [timestep, value], ...] # A schedule should normally start from timestep 0. "lr_schedule": None, # Learning rate for adam optimizer for the user choice model. "lr_choice_model": 1e-3, # Only relevant for framework=torch. # RMSProp epsilon hyper parameter. "rmsprop_epsilon": 1e-5, # If not None, clip gradients during optimization at this value "grad_clip": None, # How many steps of the model to sample before learning starts. "learning_starts": 20000, # Update the replay buffer with this many samples at once. Note that # this setting applies per-worker if num_workers > 1. "rollout_fragment_length": 4, # Size of a batch sampled from replay buffer for training. Note that # if async_updates is set, then each worker returns gradients for a # batch of this size. "train_batch_size": 32, # === Parallelism === # Number of workers for collecting samples with. This only makes sense # to increase if your environment is particularly slow to sample, or if # you"re using the Async or Ape-X optimizers. "num_workers": 0, # Whether to compute priorities on workers. "worker_side_prioritization": False, # Prevent reporting frequency from going lower than this time span. "min_time_s_per_reporting": 1, # Switch on no-preprocessors for easier Q-model coding. "_disable_preprocessor_api": True, }) # __sphinx_doc_end__ # fmt: on def calculate_round_robin_weights(config: TrainerConfigDict) -> List[float]: """Calculate the round robin weights for the rollout and train steps""" if not config["training_intensity"]: return [1, 1] # e.g., 32 / 4 -> native ratio of 8.0 native_ratio = config["train_batch_size"] / config["rollout_fragment_length"] # Training intensity is specified in terms of # (steps_replayed / steps_sampled), so adjust for the native ratio. weights = [1, config["training_intensity"] / native_ratio] return weights class SlateQTrainer(Trainer): @classmethod @override(Trainer) def get_default_config(cls) -> TrainerConfigDict: return DEFAULT_CONFIG @override(Trainer) def validate_config(self, config: TrainerConfigDict) -> None: super().validate_config(config) validate_buffer_config(config) @override(Trainer) def get_default_policy_class(self, config: TrainerConfigDict) -> Type[Policy]: if config["framework"] == "torch": return SlateQTorchPolicy else: return SlateQTFPolicy @staticmethod @override(Trainer) def execution_plan( workers: WorkerSet, config: TrainerConfigDict, **kwargs ) -> LocalIterator[dict]: assert ( "local_replay_buffer" in kwargs ), "SlateQ execution plan requires a local replay buffer." rollouts = ParallelRollouts(workers, mode="bulk_sync") # We execute the following steps concurrently: # (1) Generate rollouts and store them in our local replay buffer. # Calling next() on store_op drives this. store_op = rollouts.for_each( StoreToReplayBuffer(local_buffer=kwargs["local_replay_buffer"]) ) if config["simple_optimizer"]: train_step_op = TrainOneStep(workers) else: train_step_op = MultiGPUTrainOneStep( workers=workers, sgd_minibatch_size=config["train_batch_size"], num_sgd_iter=1, num_gpus=config["num_gpus"], _fake_gpus=config["_fake_gpus"], ) # (2) Read and train on experiences from the replay buffer. Every batch # returned from the LocalReplay() iterator is passed to TrainOneStep to # take a SGD step. replay_op = ( Replay(local_buffer=kwargs["local_replay_buffer"]) .for_each(train_step_op) .for_each( UpdateTargetNetwork(workers, config["target_network_update_freq"]) ) ) # Alternate deterministically between (1) and (2). Only return the # output of (2) since training metrics are not available until (2) # runs. train_op = Concurrently( [store_op, replay_op], mode="round_robin", output_indexes=[1], round_robin_weights=calculate_round_robin_weights(config), ) return StandardMetricsReporting(train_op, workers, config)