[bug-fix] Add clipping to PyTorch policy, fix initialization (#4649) (#4662)

ml-agents/mlagents/trainers/policy/torch_policy.py

             conditional_sigma=self.condition_sigma_on_obs,
             tanh_squash=tanh_squash,
         )
+        self._clip_action = not tanh_squash
         # Save the m_size needed for export
         self._export_m_size = self.m_size
         # m_size needed for training is determined by network, not trainer settings
             action, log_probs, entropy, memories = self.sample_actions(
                 vec_obs, vis_obs, masks=masks, memories=memories
             )
-        run_out["action"] = ModelUtils.to_numpy(action)
+
+        if self._clip_action and self.use_continuous_act:
+            clipped_action = torch.clamp(action, -3, 3) / 3
+        else:
+            clipped_action = action
+        run_out["action"] = ModelUtils.to_numpy(clipped_action)
         # Todo - make pre_action difference
         run_out["log_probs"] = ModelUtils.to_numpy(log_probs)
         run_out["entropy"] = ModelUtils.to_numpy(entropy)

ml-agents/mlagents/trainers/ppo/optimizer_torch.py

         vec_obs = [ModelUtils.list_to_tensor(batch["vector_obs"])]
         act_masks = ModelUtils.list_to_tensor(batch["action_mask"])
         if self.policy.use_continuous_act:
-            actions = ModelUtils.list_to_tensor(batch["actions"]).unsqueeze(-1)
+            actions = ModelUtils.list_to_tensor(batch["actions_pre"]).unsqueeze(-1)
         else:
             actions = ModelUtils.list_to_tensor(batch["actions"], dtype=torch.long)
 

ml-agents/mlagents/trainers/tests/torch/test_reward_providers/test_curiosity.py

     buffer = create_agent_buffer(behavior_spec, 5)
     curiosity_rp.update(buffer)
     reward_old = curiosity_rp.evaluate(buffer)[0]
-    for _ in range(10):
+    for _ in range(20):
         curiosity_rp.update(buffer)
         reward_new = curiosity_rp.evaluate(buffer)[0]
     assert reward_new < reward_old

ml-agents/mlagents/trainers/tests/torch/test_simple_rl.py

         PPO_TORCH_CONFIG,
         hyperparameters=new_hyperparams,
         network_settings=new_network_settings,
-        max_steps=5000,
+        max_steps=6000,
     )
     check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
 

ml-agents/mlagents/trainers/tests/torch/test_utils.py

         action_list, dist_list
     )
     assert log_probs.shape == (1, 2, 2)
-    assert entropies.shape == (1, 2, 2)
+    assert entropies.shape == (1, 1, 2)
     assert all_probs is None
 
     for log_prob in log_probs.flatten():

ml-agents/mlagents/trainers/torch/distributions.py

         return torch.exp(log_prob)
 
     def entropy(self):
-        return 0.5 * torch.log(2 * math.pi * math.e * self.std + EPSILON)
+        return torch.mean(
+            0.5 * torch.log(2 * math.pi * math.e * self.std + EPSILON),
+            dim=1,
+            keepdim=True,
+        )  # Use equivalent behavior to TF
 
 
 class TanhGaussianDistInstance(GaussianDistInstance):
             hidden_size,
             num_outputs,
             kernel_init=Initialization.KaimingHeNormal,
-            kernel_gain=0.1,
+            kernel_gain=0.2,
             bias_init=Initialization.Zero,
         )
         self.tanh_squash = tanh_squash
                 num_outputs,
                 kernel_init=Initialization.KaimingHeNormal,
-                kernel_gain=0.1,
+                kernel_gain=0.2,
                 bias_init=Initialization.Zero,
             )
         else:

ml-agents/mlagents/trainers/torch/encoders.py

                 self.final_flat,
                 self.h_size,
                 kernel_init=Initialization.KaimingHeNormal,
-                kernel_gain=1.0,
+                kernel_gain=1.41,  # Use ReLU gain
             ),
             nn.LeakyReLU(),
         )
                 self.final_flat,
                 self.h_size,
                 kernel_init=Initialization.KaimingHeNormal,
-                kernel_gain=1.0,
+                kernel_gain=1.41,  # Use ReLU gain
             ),
             nn.LeakyReLU(),
         )
                 self.final_flat,
                 self.h_size,
                 kernel_init=Initialization.KaimingHeNormal,
-                kernel_gain=1.0,
+                kernel_gain=1.41,  # Use ReLU gain
             ),
             nn.LeakyReLU(),
         )
             n_channels[-1] * height * width,
             output_size,
             kernel_init=Initialization.KaimingHeNormal,
-            kernel_gain=1.0,
+            kernel_gain=1.41,  # Use ReLU gain
         )
         self.sequential = nn.Sequential(*layers)
 

ml-agents/mlagents/trainers/torch/layers.py

     :param output_size: The size of the output tensor
     :param kernel_init: The Initialization to use for the weights of the layer
     :param kernel_gain: The multiplier for the weights of the kernel. Note that in
-    TensorFlow, calling variance_scaling with scale 0.01 is equivalent to calling
-    KaimingHeNormal with kernel_gain of 0.1
+    TensorFlow, the gain is square-rooted. Therefore calling  with scale 0.01 is equivalent to calling
+        KaimingHeNormal with kernel_gain of 0.1
-    _init_methods[kernel_init](layer.weight.data)
+    if (
+        kernel_init == Initialization.KaimingHeNormal
+        or kernel_init == Initialization.KaimingHeUniform
+    ):
+        _init_methods[kernel_init](layer.weight.data, nonlinearity="linear")
+    else:
+        _init_methods[kernel_init](layer.weight.data)
     layer.weight.data *= kernel_gain
     _init_methods[bias_init](layer.bias.data)
     return layer

ml-agents/mlagents/trainers/torch/networks.py

             self.distribution = MultiCategoricalDistribution(
                 self.encoding_size, self.action_spec.discrete_branches
             )
+        # During training, clipping is done in TorchPolicy, but we need to clip before ONNX
+        # export as well.
+        self._clip_action_on_export = not tanh_squash
 
     @property
     def memory_size(self) -> int:
         if self.action_spec.is_continuous():
             action_list = self.sample_action(dists)
             action_out = torch.stack(action_list, dim=-1)
+            if self._clip_action_on_export:
+                action_out = torch.clamp(action_out, -3, 3) / 3
         else:
             action_out = torch.cat([dist.all_log_prob() for dist in dists], dim=1)
         return (