added the option for a deterministic run

tools.py

@@ -6,7 +6,7 @@ import json
 import pathlib
 import re
 import time
-import uuid
+import random
 
 import numpy as np
 
@@ -321,7 +321,7 @@ def from_generator(generator, batch_size):
 
 
 def sample_episodes(episodes, length, seed=0):
-    random = np.random.RandomState(seed)
+    np_random = np.random.RandomState(seed)
     while True:
         size = 0
         ret = None
@@ -330,15 +330,17 @@ def sample_episodes(episodes, length, seed=0):
         )
         p = p / np.sum(p)
         while size < length:
-            episode = random.choice(list(episodes.values()), p=p)
+            episode = np_random.choice(list(episodes.values()), p=p)
             total = len(next(iter(episode.values())))
             # make sure at least one transition included
             if total < 2:
                 continue
             if not ret:
-                index = int(random.randint(0, total - 1))
+                index = int(np_random.randint(0, total - 1))
                 ret = {
-                    k: v[index : min(index + length, total)] for k, v in episode.items()
+                    k: v[index : min(index + length, total)]
+                    for k, v in episode.items()
+                    if "log_" not in k
                 }
                 if "is_first" in ret:
                     ret["is_first"][0] = True
@@ -351,6 +353,7 @@ def sample_episodes(episodes, length, seed=0):
                         ret[k], v[index : min(index + possible, total)], axis=0
                     )
                     for k, v in episode.items()
+                    if "log_" not in k
                 }
                 if "is_first" in ret:
                     ret["is_first"][size] = True
@@ -980,3 +983,17 @@ def tensorstats(tensor, prefix=None):
     if prefix:
         metrics = {f"{prefix}_{k}": v for k, v in metrics.items()}
     return metrics
+
+
+def set_seed_everywhere(seed):
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+def enable_deterministic_run():
+    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
+    torch.backends.cudnn.benchmark = False
+    torch.use_deterministic_algorithms(True)