when runninng:

from tensorforce.core.parameters import Linear
Linear(unit='episodes', num_steps=100000, initial_value=1.0, final_value=0.05)

I get:

AttributeError Traceback (most recent call last)
Input In [2], in <cell line: 1>()
----> 1 Linear(unit='episodes', num_steps=100000, initial_value=1.0, final_value=0.05)

File ~/Desktop/Semantic-Reasoning-in-Reinforcement-Learning/tensorforce/tensorforce/core/parameters/linear.py:42, in Linear.init(self, unit, num_steps, initial_value, final_value, name, dtype, min_value, max_value)
38 def init(
39 self, *, unit, num_steps, initial_value, final_value, name=None, dtype=None, min_value=None,
40 max_value=None
41 ):
---> 42 super().init(
43 decay='linear', unit=unit, num_steps=num_steps, initial_value=initial_value, name=name,
44 dtype=dtype, min_value=min_value, max_value=max_value, final_value=final_value
45 )

File ~/Desktop/Semantic-Reasoning-in-Reinforcement-Learning/tensorforce/tensorforce/core/parameters/decaying.py:143, in Decaying.init(self, decay, unit, num_steps, initial_value, increasing, inverse, scale, name, dtype, min_value, max_value, **kwargs)
141 elif isinstance(initial_value, float):
142 if dtype != 'float':
--> 143 raise TensorforceError.dtype(
144 name='Decaying', argument='initial_value', dtype=type(initial_value)
145 )
146 else:
147 raise TensorforceError.unexpected()

AttributeError: type object 'TensorforceError' has no attribute 'dtype'

I use tensorforce version 0.6.5