class_docstr: Implementation of the model-based DreamerV3 RL algorithm described in [1].
class_docstr_tok: ["Implementation","of","the","model-based","DreamerV3","RL","algorithm","described","in","[","1","]","."]
docstr: Returns the actual training ratio of this Algorithm.
  
  The training ratio is copmuted by dividing the total number of steps
  trained thus far (replayed from the buffer) over the total number of actual
  env steps taken thus far.
code: def training_ratio(self) -> float:
      """Returns the actual training ratio of this Algorithm.
  
      The training ratio is copmuted by dividing the total number of steps
      trained thus far (replayed from the buffer) over the total number of actual
      env steps taken thus far.
      """
      return self._counters[NUM_ENV_STEPS_TRAINED] / (
          self._counters[NUM_ENV_STEPS_SAMPLED]
      )
  
imports: import copy
  import gc
  import logging
  import tree
  from typing import Any, Dict, List, Optional, Union
  import gymnasium
  import numpy
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.dreamerv3.dreamerv3_catalog import DreamerV3Catalog
  from ray.rllib.algorithms.dreamerv3.utils import do_symlog_obs
  from ray.rllib.algorithms.dreamerv3.utils.env_runner import DreamerV3EnvRunner
  from ray.rllib.algorithms.dreamerv3.utils.summaries import report_predicted_vs_sampled_obs, report_sampling_and_replay_buffer
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.models.catalog import MODEL_DEFAULTS
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch
  from ray.rllib.utils import deep_update
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.numpy import one_hot
  from ray.rllib.utils.metrics import ALL_MODULES, GARBAGE_COLLECTION_TIMER, LEARN_ON_BATCH_TIMER, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, NUM_GRAD_UPDATES_LIFETIME, NUM_SYNCH_WORKER_WEIGHTS, NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS, SAMPLE_TIMER, SYNCH_WORKER_WEIGHTS_TIMER
  from ray.rllib.utils.replay_buffers.episode_replay_buffer import EpisodeReplayBuffer
  from ray.rllib.utils.typing import LearningRateOrSchedule, ResultDict
label_desc: Use image node_id 5 for calling the DreamerV3 obj's underlying member method code with example usage: obj.training_ratio() and returns: unknown