class_docstr: Continuous state and action fake environment.
class_docstr_tok: ["Continuous","state","and","action","fake","environment","."]
docstr: Initialize the environment.
  
  Args:
    action_dim: number of action dimensions.
    observation_dim: number of observation dimensions.
    bounded: whether or not the actions are bounded in [-1, 1].
    dtype: dtype of the action and observation spaces.
    reward_dtype: dtype of the reward and discounts.
    **kwargs: additional kwargs passed to the Environment base class.
code: def __init__(
      self,
      *,
      action_dim: int = 1,
      observation_dim: int = 1,
      bounded: bool = False,
      dtype=np.float32,
      reward_dtype=np.float32,
      **kwargs,
  ):
      """Initialize the environment.
  
      Args:
        action_dim: number of action dimensions.
        observation_dim: number of observation dimensions.
        bounded: whether or not the actions are bounded in [-1, 1].
        dtype: dtype of the action and observation spaces.
        reward_dtype: dtype of the reward and discounts.
        **kwargs: additional kwargs passed to the Environment base class.
      """
  
      action_shape = () if action_dim == 0 else (action_dim,)
      observation_shape = (
          () if observation_dim == 0 else (observation_dim,)
      )
  
      observations = specs.Array(observation_shape, dtype)
      rewards = specs.Array((), reward_dtype)
      discounts = specs.BoundedArray((), reward_dtype, 0.0, 1.0)
  
      if bounded:
          actions = specs.BoundedArray(action_shape, dtype, -1.0, 1.0)
      else:
          actions = specs.Array(action_shape, dtype)
  
      super().__init__(
          spec=specs.EnvironmentSpec(
              observations=observations,
              actions=actions,
              rewards=rewards,
              discounts=discounts,
          ),
          **kwargs,
      )
  
imports: import threading
  from typing import List, Mapping, Optional, Sequence, Callable, Iterator
  from acme import core
  from acme import specs
  from acme import types
  from acme import wrappers
  import dm_env
  import numpy
  import reverb
  from rlds import rlds_types
  import tensorflow
  import tree
label_desc: Use image node_id 1 to create a new ContinuousEnvironment object from inherited base classes: Environment with example: obj = ContinuousEnvironment()