@@ -124,7 +124,7 @@ def __init__(
124124 signal_space : SignalSpace ,
125125 policy_weight : float ,
126126 weight : float ,
127- intrinsic_reward_integration : float ,
127+ intrinsic_signal_factor : float ,
128128 hidden_dim : int = 128 ,
129129 ):
130130 """
@@ -134,7 +134,7 @@ def __init__(
134134:param signal_space: used for scaling the intrinsic reward returned by this module. Can be used to control how
135135the fluctuation scale of the intrinsic signal
136136:param weight: balances the importance between forward and inverse model
137- :param intrinsic_reward_integration : balances the importance between extrinsic and intrinsic signal.
137+ :param intrinsic_signal_factor : balances the importance between extrinsic and intrinsic signal.
138138"""
139139
140140 assert (
@@ -148,7 +148,7 @@ def __init__(
148148 self .policy_weight = policy_weight
149149 self .reward_scale = signal_space .span
150150 self .weight = weight
151- self .intrinsic_signal_integration = intrinsic_reward_integration
151+ self .intrinsic_signal_factor = intrinsic_signal_factor
152152
153153 self .encoder = nn .Sequential (
154154 nn .Linear (observation_space .shape [0 ], hidden_dim ),
@@ -233,8 +233,8 @@ def sample(
233233 writer .scalar ("icm/signal" , intrinsic_signal .mean ().item ())
234234
235235 return (
236- 1.0 - self .intrinsic_signal_integration
237- ) * signals + self .intrinsic_signal_integration * intrinsic_signal
236+ 1.0 - self .intrinsic_signal_factor
237+ ) * signals + self .intrinsic_signal_factor * intrinsic_signal
238238
239239 def loss (
240240 self ,
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