pytorch with gpu

aicrowd.json

@@ -4,5 +4,5 @@
   "authors": ["minerl_rainbow_baseline"],
   "tags": "RL",
   "description": "Test Model for MineRL Challenge",
-  "gpu": false
+  "gpu": true
 }

requirements.txt

@@ -4,3 +4,5 @@ coloredlogs
 matplotlib
 opencv-python
 pyro4
+torch
+torchvision

test.py

@@ -57,12 +57,12 @@ class MineRLAgentBase(abc.ABC):
     """
     To compete in the competition, you are required to implement a
     SUBCLASS to this class.
-    
+
     YOUR SUBMISSION WILL FAIL IF:
         * Rename this class
-        * You do not implement a subclass to this class 
+        * You do not implement a subclass to this class
 
-    This class enables the evaluator to run your agent in parallel, 
+    This class enables the evaluator to run your agent in parallel,
     so you should load your model only once in the 'load_agent' method.
     """
 
@@ -83,9 +83,9 @@ class MineRLAgentBase(abc.ABC):
         You should just implement the standard environment interaction loop here:
             obs  = env.reset()
             while not done:
-                env.step(self.agent.act(obs)) 
+                env.step(self.agent.act(obs))
                 ...
-        
+
         NOTE: This method will be called in PARALLEL during evaluation.
             So, only store state in LOCAL variables.
             For example, if using an LSTM, don't store the hidden state in the class
@@ -103,7 +103,7 @@ class MineRLAgentBase(abc.ABC):
 
 class MineRLMatrixAgent(MineRLAgentBase):
     """
-    An example random agent. 
+    An example random agent.
     Note, you MUST subclass MineRLAgentBase.
     """
 
@@ -128,6 +128,11 @@ class MineRLMatrixAgent(MineRLAgentBase):
         Args:
             single_episode_env (Episode): The episode on which to run the agent.
         """
+        import torch
+        device = torch.device('cuda:0')
+        x = torch.randn(64, 1000, device=device, dtype=torch.float)
+        assert torch.cuda.is_available()
+
         obs = single_episode_env.reset()
         done = False
         while not done:
@@ -145,9 +150,9 @@ class MineRLRandomAgent(MineRLAgentBase):
         while not done:
             random_act = single_episode_env.action_space.sample()
             single_episode_env.step(random_act)
-        
+
 #####################################################################
-# IMPORTANT: SET THIS VARIABLE WITH THE AGENT CLASS YOU ARE USING   # 
+# IMPORTANT: SET THIS VARIABLE WITH THE AGENT CLASS YOU ARE USING   #
 ######################################################################
 AGENT_TO_TEST = MineRLMatrixAgent # MineRLMatrixAgent, MineRLRandomAgent, YourAgentHere
 
@@ -177,7 +182,7 @@ def main():
             except EpisodeDone:
                 print("[{}] Episode complete".format(i))
                 pass
-    
+
     evaluator_threads = [threading.Thread(target=evaluate, args=(i, envs[i])) for i in range(EVALUATION_THREAD_COUNT)]
     for thread in evaluator_threads:
         thread.start()
@@ -188,5 +193,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-    
-