Source code for ray.rllib.algorithms.dqn.dqn

Deep Q-Networks (DQN, Rainbow, Parametric DQN)

This file defines the distributed Algorithm class for the Deep Q-Networks
algorithm. See `dqn_[tf|torch]` for the definition of the policies.

Detailed documentation:
"""  # noqa: E501

import logging
from typing import List, Optional, Type, Callable
import numpy as np

from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.algorithms.dqn.dqn_tf_policy import DQNTFPolicy
from ray.rllib.algorithms.dqn.dqn_torch_policy import DQNTorchPolicy
from ray.rllib.algorithms.simple_q.simple_q import (
from ray.rllib.execution.rollout_ops import (
from ray.rllib.policy.sample_batch import MultiAgentBatch
from ray.rllib.execution.train_ops import (
from ray.rllib.policy.policy import Policy
from ray.rllib.utils.annotations import override
from ray.rllib.utils.replay_buffers.utils import update_priorities_in_replay_buffer
from ray.rllib.utils.typing import ResultDict
from ray.rllib.utils.metrics import (
from ray.rllib.utils.metrics import SYNCH_WORKER_WEIGHTS_TIMER
from ray.rllib.execution.common import (
from ray.rllib.utils.deprecation import DEPRECATED_VALUE
from ray.rllib.utils.replay_buffers.utils import sample_min_n_steps_from_buffer

logger = logging.getLogger(__name__)

[docs]class DQNConfig(SimpleQConfig): r"""Defines a configuration class from which a DQN Algorithm can be built. Example: >>> from ray.rllib.algorithms.dqn.dqn import DQNConfig >>> config = DQNConfig() >>> print(config.replay_buffer_config) # doctest: +SKIP >>> replay_config = config.replay_buffer_config.update( # doctest: +SKIP ... { ... "capacity": 60000, ... "prioritized_replay_alpha": 0.5, ... "prioritized_replay_beta": 0.5, ... "prioritized_replay_eps": 3e-6, ... } ... ) >>> config = doctest: +SKIP >>> config = config.resources(num_gpus=1) # doctest: +SKIP >>> config = config.rollouts(num_rollout_workers=3) # doctest: +SKIP >>> config = config.environment("CartPole-v1") # doctest: +SKIP >>> algo = DQN(config=config) # doctest: +SKIP >>> algo.train() # doctest: +SKIP Example: >>> from ray.rllib.algorithms.dqn.dqn import DQNConfig >>> from ray import air >>> from ray import tune >>> config = DQNConfig() >>> config = # doctest: +SKIP ... num_atoms=tune.grid_search(list(range(1,11)))) >>> config = config.environment(env="CartPole-v1") # doctest: +SKIP >>> tune.Tuner( # doctest: +SKIP ... "DQN", ... run_config=air.RunConfig(stop={"episode_reward_mean":200}), ... param_space=config.to_dict() ... ).fit() Example: >>> from ray.rllib.algorithms.dqn.dqn import DQNConfig >>> config = DQNConfig() >>> print(config.exploration_config) # doctest: +SKIP >>> explore_config = config.exploration_config.update( # doctest: +SKIP ... { ... "initial_epsilon": 1.5, ... "final_epsilon": 0.01, ... "epsilone_timesteps": 5000, ... } ... ) >>>[[1, 1e-3, [500, 5e-3]])\ # doctest: +SKIP ... .exploration(exploration_config=explore_config) Example: >>> from ray.rllib.algorithms.dqn.dqn import DQNConfig >>> config = DQNConfig() >>> print(config.exploration_config) # doctest: +SKIP >>> explore_config = config.exploration_config.update( # doctest: +SKIP ... { ... "type": "softq", ... "temperature": [1.0], ... } ... ) >>>[[1, 1e-3, [500, 5e-3]])\ # doctest: +SKIP ... .exploration(exploration_config=explore_config) """ def __init__(self, algo_class=None): """Initializes a DQNConfig instance.""" super().__init__(algo_class=algo_class or DQN) # DQN specific config settings. # fmt: off # __sphinx_doc_begin__ self.num_atoms = 1 self.v_min = -10.0 self.v_max = 10.0 self.noisy = False self.sigma0 = 0.5 self.dueling = True self.hiddens = [256] self.double_q = True self.n_step = 1 self.before_learn_on_batch = None self.training_intensity = None self.td_error_loss_fn = "huber" self.categorical_distribution_temperature = 1.0 # Changes to SimpleQConfig's default: self.replay_buffer_config = { "type": "MultiAgentPrioritizedReplayBuffer", # Specify prioritized replay by supplying a buffer type that supports # prioritization, for example: MultiAgentPrioritizedReplayBuffer. "prioritized_replay": DEPRECATED_VALUE, # Size of the replay buffer. Note that if async_updates is set, # then each worker will have a replay buffer of this size. "capacity": 50000, "prioritized_replay_alpha": 0.6, # Beta parameter for sampling from prioritized replay buffer. "prioritized_replay_beta": 0.4, # Epsilon to add to the TD errors when updating priorities. "prioritized_replay_eps": 1e-6, # The number of continuous environment steps to replay at once. This may # be set to greater than 1 to support recurrent models. "replay_sequence_length": 1, # Whether to compute priorities on workers. "worker_side_prioritization": False, } # Set to `self.n_step`, if 'auto'. self.rollout_fragment_length = "auto" # fmt: on # __sphinx_doc_end__
[docs] @override(SimpleQConfig) def training( self, *, num_atoms: Optional[int] = NotProvided, v_min: Optional[float] = NotProvided, v_max: Optional[float] = NotProvided, noisy: Optional[bool] = NotProvided, sigma0: Optional[float] = NotProvided, dueling: Optional[bool] = NotProvided, hiddens: Optional[int] = NotProvided, double_q: Optional[bool] = NotProvided, n_step: Optional[int] = NotProvided, before_learn_on_batch: Callable[ [Type[MultiAgentBatch], List[Type[Policy]], Type[int]], Type[MultiAgentBatch], ] = NotProvided, training_intensity: Optional[float] = NotProvided, td_error_loss_fn: Optional[str] = NotProvided, categorical_distribution_temperature: Optional[float] = NotProvided, **kwargs, ) -> "DQNConfig": """Sets the training related configuration. Args: num_atoms: Number of atoms for representing the distribution of return. When this is greater than 1, distributional Q-learning is used. v_min: Minimum value estimation v_max: Maximum value estimation noisy: Whether to use noisy network to aid exploration. This adds parametric noise to the model weights. sigma0: Control the initial parameter noise for noisy nets. dueling: Whether to use dueling DQN. hiddens: Dense-layer setup for each the advantage branch and the value branch double_q: Whether to use double DQN. n_step: N-step for Q-learning. before_learn_on_batch: Callback to run before learning on a multi-agent batch of experiences. training_intensity: The intensity with which to update the model (vs collecting samples from the env). If None, uses "natural" values of: `train_batch_size` / (`rollout_fragment_length` x `num_workers` x `num_envs_per_worker`). If not None, will make sure that the ratio between timesteps inserted into and sampled from the buffer matches the given values. Example: training_intensity=1000.0 train_batch_size=250 rollout_fragment_length=1 num_workers=1 (or 0) num_envs_per_worker=1 -> natural value = 250 / 1 = 250.0 -> will make sure that replay+train op will be executed 4x asoften as rollout+insert op (4 * 250 = 1000). See: rllib/algorithms/dqn/ for further details. replay_buffer_config: Replay buffer config. Examples: { "_enable_replay_buffer_api": True, "type": "MultiAgentReplayBuffer", "capacity": 50000, "replay_sequence_length": 1, } - OR - { "_enable_replay_buffer_api": True, "type": "MultiAgentPrioritizedReplayBuffer", "capacity": 50000, "prioritized_replay_alpha": 0.6, "prioritized_replay_beta": 0.4, "prioritized_replay_eps": 1e-6, "replay_sequence_length": 1, } - Where - prioritized_replay_alpha: Alpha parameter controls the degree of prioritization in the buffer. In other words, when a buffer sample has a higher temporal-difference error, with how much more probability should it drawn to use to update the parametrized Q-network. 0.0 corresponds to uniform probability. Setting much above 1.0 may quickly result as the sampling distribution could become heavily “pointy” with low entropy. prioritized_replay_beta: Beta parameter controls the degree of importance sampling which suppresses the influence of gradient updates from samples that have higher probability of being sampled via alpha parameter and the temporal-difference error. prioritized_replay_eps: Epsilon parameter sets the baseline probability for sampling so that when the temporal-difference error of a sample is zero, there is still a chance of drawing the sample. td_error_loss_fn: "huber" or "mse". loss function for calculating TD error when num_atoms is 1. Note that if num_atoms is > 1, this parameter is simply ignored, and softmax cross entropy loss will be used. categorical_distribution_temperature: Set the temperature parameter used by Categorical action distribution. A valid temperature is in the range of [0, 1]. Note that this mostly affects evaluation since TD error uses argmax for return calculation. Returns: This updated AlgorithmConfig object. """ # Pass kwargs onto super's `training()` method. super().training(**kwargs) if num_atoms is not NotProvided: self.num_atoms = num_atoms if v_min is not NotProvided: self.v_min = v_min if v_max is not NotProvided: self.v_max = v_max if noisy is not NotProvided: self.noisy = noisy if sigma0 is not NotProvided: self.sigma0 = sigma0 if dueling is not NotProvided: self.dueling = dueling if hiddens is not NotProvided: self.hiddens = hiddens if double_q is not NotProvided: self.double_q = double_q if n_step is not NotProvided: self.n_step = n_step if before_learn_on_batch is not NotProvided: self.before_learn_on_batch = before_learn_on_batch if training_intensity is not NotProvided: self.training_intensity = training_intensity if td_error_loss_fn is not NotProvided: self.td_error_loss_fn = td_error_loss_fn if categorical_distribution_temperature is not NotProvided: self.categorical_distribution_temperature = ( categorical_distribution_temperature ) return self
@override(SimpleQConfig) def validate(self) -> None: # Call super's validation method. super().validate() if self.td_error_loss_fn not in ["huber", "mse"]: raise ValueError("`td_error_loss_fn` must be 'huber' or 'mse'!") # Check rollout_fragment_length to be compatible with n_step. if ( not self.in_evaluation and self.rollout_fragment_length != "auto" and self.rollout_fragment_length < self.n_step ): raise ValueError( f"Your `rollout_fragment_length` ({self.rollout_fragment_length}) is " f"smaller than `n_step` ({self.n_step})! " f"Try setting config.rollouts(rollout_fragment_length={self.n_step})." ) if self.exploration_config["type"] == "ParameterNoise": if self.batch_mode != "complete_episodes": raise ValueError( "ParameterNoise Exploration requires `batch_mode` to be " "'complete_episodes'. Try setting `config.rollouts(" "batch_mode='complete_episodes')`." ) if self.noisy: raise ValueError( "ParameterNoise Exploration and `noisy` network cannot be" " used at the same time!" ) @override(AlgorithmConfig) def get_rollout_fragment_length(self, worker_index: int = 0) -> int: if self.rollout_fragment_length == "auto": return self.n_step else: return self.rollout_fragment_length
def calculate_rr_weights(config: AlgorithmConfig) -> List[float]: """Calculate the round robin weights for the rollout and train steps""" if not config["training_intensity"]: return [1, 1] # Calculate the "native ratio" as: # [train-batch-size] / [size of env-rolled-out sampled data] # This is to set freshly rollout-collected data in relation to # the data we pull from the replay buffer (which also contains old # samples). native_ratio = config["train_batch_size"] / ( config.get_rollout_fragment_length() * config["num_envs_per_worker"] # Add one to workers because the local # worker usually collects experiences as well, and we avoid division by zero. * max(config["num_workers"] + 1, 1) ) # Training intensity is specified in terms of # (steps_replayed / steps_sampled), so adjust for the native ratio. sample_and_train_weight = config["training_intensity"] / native_ratio if sample_and_train_weight < 1: return [int(np.round(1 / sample_and_train_weight)), 1] else: return [1, int(np.round(sample_and_train_weight))] class DQN(SimpleQ): @classmethod @override(SimpleQ) def get_default_config(cls) -> AlgorithmConfig: return DQNConfig() @classmethod @override(SimpleQ) def get_default_policy_class( cls, config: AlgorithmConfig ) -> Optional[Type[Policy]]: if config["framework"] == "torch": return DQNTorchPolicy else: return DQNTFPolicy @override(SimpleQ) def training_step(self) -> ResultDict: """DQN training iteration function. Each training iteration, we: - Sample (MultiAgentBatch) from workers. - Store new samples in replay buffer. - Sample training batch (MultiAgentBatch) from replay buffer. - Learn on training batch. - Update remote workers' new policy weights. - Update target network every `target_network_update_freq` sample steps. - Return all collected metrics for the iteration. Returns: The results dict from executing the training iteration. """ train_results = {} # We alternate between storing new samples and sampling and training store_weight, sample_and_train_weight = calculate_rr_weights(self.config) for _ in range(store_weight): # Sample (MultiAgentBatch) from workers. with self._timers[SAMPLE_TIMER]: new_sample_batch = synchronous_parallel_sample( worker_set=self.workers, concat=True ) # Update counters self._counters[NUM_AGENT_STEPS_SAMPLED] += new_sample_batch.agent_steps() self._counters[NUM_ENV_STEPS_SAMPLED] += new_sample_batch.env_steps() # Store new samples in replay buffer. self.local_replay_buffer.add(new_sample_batch) global_vars = { "timestep": self._counters[NUM_ENV_STEPS_SAMPLED], } # Update target network every `target_network_update_freq` sample steps. cur_ts = self._counters[ NUM_AGENT_STEPS_SAMPLED if self.config.count_steps_by == "agent_steps" else NUM_ENV_STEPS_SAMPLED ] if cur_ts > self.config.num_steps_sampled_before_learning_starts: for _ in range(sample_and_train_weight): # Sample training batch (MultiAgentBatch) from replay buffer. train_batch = sample_min_n_steps_from_buffer( self.local_replay_buffer, self.config.train_batch_size, count_by_agent_steps=self.config.count_steps_by == "agent_steps", ) # Postprocess batch before we learn on it post_fn = self.config.get("before_learn_on_batch") or (lambda b, *a: b) train_batch = post_fn(train_batch, self.workers, self.config) # for policy_id, sample_batch in train_batch.policy_batches.items(): # print(len(sample_batch["obs"])) # print(sample_batch.count) # Learn on training batch. # Use simple optimizer (only for multi-agent or tf-eager; all other # cases should use the multi-GPU optimizer, even if only using 1 GPU) if self.config.get("simple_optimizer") is True: train_results = train_one_step(self, train_batch) else: train_results = multi_gpu_train_one_step(self, train_batch) # Update replay buffer priorities. update_priorities_in_replay_buffer( self.local_replay_buffer, self.config, train_batch, train_results, ) last_update = self._counters[LAST_TARGET_UPDATE_TS] if cur_ts - last_update >= self.config.target_network_update_freq: to_update = self.workers.local_worker().get_policies_to_train() self.workers.local_worker().foreach_policy_to_train( lambda p, pid: pid in to_update and p.update_target() ) self._counters[NUM_TARGET_UPDATES] += 1 self._counters[LAST_TARGET_UPDATE_TS] = cur_ts # Update weights and global_vars - after learning on the local worker - # on all remote workers. with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]: self.workers.sync_weights(global_vars=global_vars) # Return all collected metrics for the iteration. return train_results