class_docstr: Defines a configuration class from which an ApexDDPG Trainer can be built.
  
  Example:
      >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig
      >>> config = ApexDDPGConfig().training(lr=0.01).resources(num_gpus=1)
      >>> print(config.to_dict()) # doctest: +SKIP
      >>> # Build a Trainer object from the config and run one training iteration.
      >>> algo = config.build(env="Pendulum-v1")
      >>> algo.train()  # doctest: +SKIP
  
  Example:
      >>> from ray.rllib.algorithms.apex_ddpg.apex_ddpg import ApexDDPGConfig
      >>> from ray import tune
      >>> import ray.air as air
      >>> config = ApexDDPGConfig()
      >>> # Print out some default values.
      >>> print(config.lr) # doctest: +SKIP
      0.0004
      >>> # Update the config object.
      >>> config.training(lr=tune.grid_search([0.001, 0.0001]))
      >>> # Set the config object's env.
      >>> config.environment(env="Pendulum-v1")
      >>> # Use to_dict() to get the old-style python config dict
      >>> # when running with tune.
      >>> tune.Tuner( # doctest: +SKIP
      ...     "APEX_DDPG",
      ...     run_config=air.RunConfig(stop={"episode_reward_mean": 200}),
      ...     param_space=config.to_dict(),
      ... ).fit()
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docstr: Initializes an ApexDDPGConfig instance.
docstr_tok: ["Initializes","an","ApexDDPGConfig","instance","."]
file_path: ray/rllib_contrib/apex_ddpg/src/rllib_apex_ddpg/apex_ddpg/apex_ddpg.py
imports: from typing import Optional
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.apex_dqn.apex_dqn import ApexDQN
  from ray.rllib.algorithms.ddpg.ddpg import DDPG, DDPGConfig
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 1 to create a new ApexDDPGConfig object from inherited base classes: DDPGConfig with example: obj = ApexDDPGConfig(algo_class)