Remove unused hyperparams

Author: cswinter

hyper_params.py

@@ -52,12 +52,6 @@ def __init__(self):
         self.fp16 = False              # Whether to use half-precision floating point
         self.zero_init_vf = True       # Set all initial weights for value function head to zero
         self.small_init_pi = False     # Set initial weights for policy head to small values and biases to zero
-        self.spatial_attn = False      # Use parametric spatial attention
-        self.item_item_spatial_attn = False  # Use parametric spatial attention on item<->item self attention layer
-        self.spatial_attn_lr_multiplier = 1.0   # Increased learning rate for parametric spatial attention parameters
-        self.spatial_attn_scale = 10000.0
-        self.spatial_attn_init_scale = 0.1
-        self.item_item_spatial_attn_vf = False
 
         self.resume_from = ''       # Filepath to saved policy
 

main.py

@@ -457,16 +457,6 @@ def train(hps: HyperParams, out_dir: str) -> None:
                 metrics[f'build_{action}'] = count
             for action, fraction in normalize(buildmean).items():
                 metrics[f'frac_{action}'] = fraction
-            if hps.spatial_attn:
-                for i in range(hps.nhead):
-                    metrics[f'gattn.mean[{i}]'] = policy.gattn.mean[i]
-                    metrics[f'gattn.logvariance[{i}]'] = policy.gattn.logvariance[i]
-                    metrics[f'gattn.weight[{i}]'] = policy.gattn.weight[i]
-            if hps.item_item_spatial_attn:
-                for i in range(hps.nhead):
-                    metrics[f'gattn_ii.mean[{i}]'] = policy.gattn_ii.mean[i]
-                    metrics[f'gattn_ii.logvariance[{i}]'] = policy.gattn_ii.logvariance[i]
-                    metrics[f'gattn_ii.weight[{i}]'] = policy.gattn_ii.weight[i]
 
             metrics.update(adr.metrics())
             total_norm = 0.0