put running episode into replay buffer

envs/wrappers.py

@@ -1,13 +1,18 @@
 import gym
 import numpy as np
+import uuid
 
 
 class CollectDataset:
-    def __init__(self, env, callbacks=None, precision=32):
+    def __init__(
+        self, env, mode, train_eps, eval_eps=dict(), callbacks=None, precision=32
+    ):
         self._env = env
         self._callbacks = callbacks or ()
         self._precision = precision
         self._episode = None
+        self._cache = dict(train=train_eps, eval=eval_eps)[mode]
+        self._temp_name = str(uuid.uuid4())
 
     def __getattr__(self, name):
         return getattr(self._env, name)
@@ -23,7 +28,11 @@ class CollectDataset:
         transition["reward"] = reward
         transition["discount"] = info.get("discount", np.array(1 - float(done)))
         self._episode.append(transition)
+        self.add_to_cache(transition)
         if done:
+            # detele transitions before whole episode is stored
+            del self._cache[self._temp_name]
+            self._temp_name = str(uuid.uuid4())
             for key, value in self._episode[1].items():
                 if key not in self._episode[0]:
                     self._episode[0][key] = 0 * value
@@ -43,8 +52,23 @@ class CollectDataset:
         transition["reward"] = 0.0
         transition["discount"] = 1.0
         self._episode = [transition]
+        self.add_to_cache(transition)
         return obs
 
+    def add_to_cache(self, transition):
+        if self._temp_name not in self._cache:
+            self._cache[self._temp_name] = dict()
+            for key, val in transition.items():
+                self._cache[self._temp_name][key] = [self._convert(val)]
+        else:
+            for key, val in transition.items():
+                if key not in self._cache[self._temp_name]:
+                    # fill missing data(action)
+                    self._cache[self._temp_name][key] = [self._convert(0 * val)]
+                    self._cache[self._temp_name][key].append(self._convert(val))
+                else:
+                    self._cache[self._temp_name][key].append(self._convert(val))
+
     def _convert(self, value):
         value = np.array(value)
         if np.issubdtype(value.dtype, np.floating):