Fix a problem with the recent version of PyTorch

README.md

@@ -1,8 +1,5 @@
 # pytorch-a3c
 
-NEED TO USE V-0.1.9 (or lower) OF PYTORCH, AND NOT V-0.1.10 BECAUSE OF THIS ISSUE:
-https://discuss.pytorch.org/t/problem-on-variable-grad-data/957/7
-
 This is a PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from ["Asynchronous Methods for Deep Reinforcement Learning"](https://arxiv.org/pdf/1602.01783v1.pdf).
 
 This implementation is inspired by [Universe Starter Agent](https://github.com/openai/universe-starter-agent).
@@ -14,7 +11,7 @@ Contributions are very welcome. If you know how to make this code better, don't
 
 ## Usage
 ```
-python main.py --env-name "PongDeterministic-v3" --num-processes 16
+OMP_NUM_THREADS=1 python main.py --env-name "PongDeterministic-v3" --num-processes 16
 ```
 
 This code runs evaluation in a separate thread in addition to 16 processes.

main.py

@@ -38,7 +38,6 @@
     args = parser.parse_args()
 
     torch.manual_seed(args.seed)
-    torch.set_num_threads(1)
 
     env = create_atari_env(args.env_name)
     shared_model = ActorCritic(

model.py

@@ -59,6 +59,16 @@ def __init__(self, num_inputs, action_space):
         self.lstm.bias_hh.data.fill_(0)
 
         self.train()
+        self.__dummy_backprob()
+
+    def __dummy_backprob(self):
+        # See: https://discuss.pytorch.org/t/problem-on-variable-grad-data/957/7
+        # An ugly hack until there is a better solution.
+        inputs = Variable(torch.randn(1, 1, 42, 42))
+        hx, cx = Variable(torch.randn(1, 256)), Variable(torch.randn(1, 256))
+        outputs = self((inputs, (hx, cx)))
+        loss = (outputs[0].mean() + outputs[1].mean()) * 0.0
+        loss.backward()
 
     def forward(self, inputs):
         inputs, (hx, cx) = inputs