Reformat clean_rl_example.py

Author: Ivan-267

examples/clean_rl_example.py

@@ -167,7 +167,8 @@ def get_action_and_value(self, x, action=None):
 
     # env setup
 
-    envs = env = CleanRLGodotEnv(env_path=args.env_path, show_window=args.viz, speedup=args.speedup, seed=args.seed, n_parallel=args.n_parallel)
+    envs = env = CleanRLGodotEnv(env_path=args.env_path, show_window=args.viz, speedup=args.speedup, seed=args.seed,
+                                 n_parallel=args.n_parallel)
     args.num_envs = envs.num_envs
     args.batch_size = int(args.num_envs * args.num_steps)
     args.minibatch_size = int(args.batch_size // args.num_minibatches)
@@ -333,6 +334,7 @@ def get_action_and_value(self, x, action=None):
 
         agent.eval().to("cpu")
 
+
         class OnnxPolicy(torch.nn.Module):
             def __init__(self, actor_mean):
                 super().__init__()
@@ -342,6 +344,7 @@ def forward(self, obs, state_ins):
                 action_mean = self.actor_mean(obs)
                 return action_mean, state_ins
 
+
         onnx_policy = OnnxPolicy(agent.actor_mean)
         dummy_input = torch.unsqueeze(torch.tensor(envs.single_observation_space.sample()), 0)
 
@@ -352,9 +355,9 @@ def forward(self, obs, state_ins):
             opset_version=15,
             input_names=["obs", "state_ins"],
             output_names=["output", "state_outs"],
-            dynamic_axes={'obs' : {0 : 'batch_size'},
-                          'state_ins' : {0 : 'batch_size'},    # variable length axes
-                          'output' : {0 : 'batch_size'},
-                          'state_outs' : {0 : 'batch_size'}}
+            dynamic_axes={'obs': {0: 'batch_size'},
+                          'state_ins': {0: 'batch_size'},  # variable length axes
+                          'output': {0: 'batch_size'},
+                          'state_outs': {0: 'batch_size'}}
 
-        )
+        )