log reward clipping removed

src/gfn/gym/box.py

@@ -20,7 +20,6 @@ def __init__(
         R2: float = 2.0,
         epsilon: float = 1e-4,
         device_str: Literal["cpu", "cuda"] = "cpu",
-        log_reward_clip: float = -100.0,
     ):
         assert 0 < delta <= 1, "delta must be in (0, 1]"
         self.delta = delta
@@ -31,7 +30,7 @@ def __init__(
         self.R1 = R1
         self.R2 = R2
 
-        super().__init__(s0=s0, log_reward_clip=log_reward_clip)
+        super().__init__(s0=s0)
 
     def make_States_class(self) -> type[States]:
         env = self

src/gfn/gym/discrete_ebm.py

@@ -48,7 +48,6 @@ def __init__(
         alpha: float = 1.0,
         device_str: Literal["cpu", "cuda"] = "cpu",
         preprocessor_name: Literal["Identity", "Enum"] = "Identity",
-        log_reward_clip: float = -100.0,
     ):
         """Discrete EBM environment.
 
@@ -60,7 +59,6 @@ def __init__(
             device_str: "cpu" or "cuda". Defaults to "cpu".
             preprocessor_name: "KHot" or "OneHot" or "Identity".
                 Defaults to "KHot".
-            log_reward_clip: Minimum log reward allowable (namely, for log(0)).
         """
         self.ndim = ndim
 
@@ -94,7 +92,6 @@ def __init__(
             sf=sf,
             device_str=device_str,
             preprocessor=preprocessor,
-            log_reward_clip=log_reward_clip,
         )
 
     def make_States_class(self) -> type[DiscreteStates]:
@@ -195,7 +192,7 @@ def log_reward(self, final_states: DiscreteStates) -> TT["batch_shape"]:
         canonical = 2 * raw_states - 1
         log_reward = -self.alpha * self.energy(canonical)
 
-        return log_reward.clip(self.log_reward_clip)
+        return log_reward
 
     def get_states_indices(self, states: DiscreteStates) -> TT["batch_shape"]:
         """The chosen encoding is the following: -1 -> 0, 0 -> 1, 1 -> 2, then we convert to base 3"""

src/gfn/gym/hypergrid.py

@@ -25,7 +25,6 @@ def __init__(
         reward_cos: bool = False,
         device_str: Literal["cpu", "cuda"] = "cpu",
         preprocessor_name: Literal["KHot", "OneHot", "Identity", "Enum"] = "KHot",
-        log_reward_clip: float = -100.0,
     ):
         """HyperGrid environment from the GFlowNets paper.
         The states are represented as 1-d tensors of length `ndim` with values in
@@ -42,7 +41,6 @@ def __init__(
             reward_cos (bool, optional): Which version of the reward to use. Defaults to False.
             device_str (str, optional): "cpu" or "cuda". Defaults to "cpu".
             preprocessor_name (str, optional): "KHot" or "OneHot" or "Identity". Defaults to "KHot".
-            log_reward_clip: Minimum log reward allowable (namely, for log(0)).
         """
         self.ndim = ndim
         self.height = height
@@ -82,7 +80,6 @@ def __init__(
             sf=sf,
             device_str=device_str,
             preprocessor=preprocessor,
-            log_reward_clip=log_reward_clip,
         )
 
     def make_States_class(self) -> type[DiscreteStates]: