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