From d523c8cc0ac0214963eee5323f717c67c647e540 Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Fri, 7 Aug 2020 16:14:21 -0700
Subject: [PATCH 01/17] Running LSTM for SAC

---
 .../mlagents/trainers/sac/optimizer_torch.py  | 100 +++++++++++++++---
 1 file changed, 85 insertions(+), 15 deletions(-)

diff --git a/ml-agents/mlagents/trainers/sac/optimizer_torch.py b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
index 9c3ced80a7..84df073fc0 100644
--- a/ml-agents/mlagents/trainers/sac/optimizer_torch.py
+++ b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
@@ -1,7 +1,8 @@
 import numpy as np
-from typing import Dict, List, Mapping, cast, Tuple
+from typing import Dict, List, Mapping, cast, Tuple, Optional
 import torch
 from torch import nn
+import attr
 
 from mlagents_envs.logging_util import get_logger
 from mlagents_envs.base_env import ActionType
@@ -56,10 +57,24 @@ def forward(
             self,
             vec_inputs: List[torch.Tensor],
             vis_inputs: List[torch.Tensor],
-            actions: torch.Tensor = None,
+            actions: Optional[torch.Tensor] = None,
+            memories: Optional[torch.Tensor] = None,
+            sequence_length: int = 1,
         ) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]:
-            q1_out, _ = self.q1_network(vec_inputs, vis_inputs, actions=actions)
-            q2_out, _ = self.q2_network(vec_inputs, vis_inputs, actions=actions)
+            q1_out, _ = self.q1_network(
+                vec_inputs,
+                vis_inputs,
+                actions=actions,
+                memories=memories,
+                sequence_length=sequence_length,
+            )
+            q2_out, _ = self.q2_network(
+                vec_inputs,
+                vis_inputs,
+                actions=actions,
+                memories=memories,
+                sequence_length=sequence_length,
+            )
             return q1_out, q2_out
 
     def __init__(self, policy: TorchPolicy, trainer_params: TrainerSettings):
@@ -87,17 +102,28 @@ def __init__(self, policy: TorchPolicy, trainer_params: TrainerSettings):
             for name in self.stream_names
         }
 
+        # Critics should have 1/2 of the memory of the policy
+        critic_memory = policy_network_settings.memory
+        if critic_memory is not None:
+            critic_memory = attr.evolve(
+                critic_memory, memory_size=critic_memory.memory_size // 2
+            )
+        value_network_settings = attr.evolve(
+            policy_network_settings, memory=critic_memory
+        )
+
         self.value_network = TorchSACOptimizer.PolicyValueNetwork(
             self.stream_names,
             self.policy.behavior_spec.observation_shapes,
-            policy_network_settings,
+            value_network_settings,
             self.policy.behavior_spec.action_type,
             self.act_size,
         )
+
         self.target_network = ValueNetwork(
             self.stream_names,
             self.policy.behavior_spec.observation_shapes,
-            policy_network_settings,
+            value_network_settings,
         )
         self.soft_update(self.policy.actor_critic.critic, self.target_network, 1.0)
 
@@ -232,7 +258,6 @@ def sac_value_loss(
                     v_backup = min_policy_qs[name] - torch.sum(
                         _ent_coef * log_probs, dim=1
                     )
-                # print(log_probs, v_backup, _ent_coef, loss_masks)
                 value_loss = 0.5 * torch.mean(
                     loss_masks * torch.nn.functional.mse_loss(values[name], v_backup)
                 )
@@ -369,12 +394,30 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
         else:
             actions = ModelUtils.list_to_tensor(batch["actions"], dtype=torch.long)
 
-        memories = [
+        memories_list = [
             ModelUtils.list_to_tensor(batch["memory"][i])
             for i in range(0, len(batch["memory"]), self.policy.sequence_length)
         ]
-        if len(memories) > 0:
-            memories = torch.stack(memories).unsqueeze(0)
+        # LSTM shouldn't have sequence length <1, but stop it from going out of the index if true.
+        offset = 1 if self.policy.sequence_length > 1 else 0
+        next_memories_list = [
+            ModelUtils.list_to_tensor(
+                batch["memory"][i][: self.policy.m_size // 2]
+            )  # only pass value part of memory to target network
+            for i in range(offset, len(batch["memory"]), self.policy.sequence_length)
+        ]
+
+        if len(memories_list) > 0:
+            memories = torch.stack(memories_list).unsqueeze(0)
+            next_memories = torch.stack(next_memories_list).unsqueeze(0)
+        else:
+            memories = None
+            next_memories = None
+        # Q network memories are 0'ed out, since we don't have them during inference.
+        q_memories = torch.zeros(
+            (memories.shape[0], memories.shape[1], memories.shape[2] // 2)
+        )
+
         vis_obs: List[torch.Tensor] = []
         next_vis_obs: List[torch.Tensor] = []
         if self.policy.use_vis_obs:
@@ -415,18 +458,45 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
         )
         if self.policy.use_continuous_act:
             squeezed_actions = actions.squeeze(-1)
-            q1p_out, q2p_out = self.value_network(vec_obs, vis_obs, sampled_actions)
-            q1_out, q2_out = self.value_network(vec_obs, vis_obs, squeezed_actions)
+            q1p_out, q2p_out = self.value_network(
+                vec_obs,
+                vis_obs,
+                sampled_actions,
+                memories=q_memories,
+                sequence_length=self.policy.sequence_length,
+            )
+            q1_out, q2_out = self.value_network(
+                vec_obs,
+                vis_obs,
+                squeezed_actions,
+                memories=q_memories,
+                sequence_length=self.policy.sequence_length,
+            )
             q1_stream, q2_stream = q1_out, q2_out
         else:
             with torch.no_grad():
-                q1p_out, q2p_out = self.value_network(vec_obs, vis_obs)
-            q1_out, q2_out = self.value_network(vec_obs, vis_obs)
+                q1p_out, q2p_out = self.value_network(
+                    vec_obs,
+                    vis_obs,
+                    memories=q_memories,
+                    sequence_length=self.policy.sequence_length,
+                )
+            q1_out, q2_out = self.value_network(
+                vec_obs,
+                vis_obs,
+                memories=q_memories,
+                sequence_length=self.policy.sequence_length,
+            )
             q1_stream = self._condense_q_streams(q1_out, actions)
             q2_stream = self._condense_q_streams(q2_out, actions)
 
         with torch.no_grad():
-            target_values, _ = self.target_network(next_vec_obs, next_vis_obs)
+            target_values, _ = self.target_network(
+                next_vec_obs,
+                next_vis_obs,
+                memories=next_memories,
+                sequence_length=self.policy.sequence_length,
+            )
         masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.int32)
         use_discrete = not self.policy.use_continuous_act
         dones = ModelUtils.list_to_tensor(batch["done"])

From f2873b296fe60fefb86545405c6076e26d10af5e Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Mon, 10 Aug 2020 10:38:19 -0700
Subject: [PATCH 02/17] Use correct half of memories

---
 ml-agents/mlagents/trainers/sac/optimizer_torch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ml-agents/mlagents/trainers/sac/optimizer_torch.py b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
index 84df073fc0..3ba92e6c1b 100644
--- a/ml-agents/mlagents/trainers/sac/optimizer_torch.py
+++ b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
@@ -402,7 +402,7 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
         offset = 1 if self.policy.sequence_length > 1 else 0
         next_memories_list = [
             ModelUtils.list_to_tensor(
-                batch["memory"][i][: self.policy.m_size // 2]
+                batch["memory"][i][self.policy.m_size // 2 :]
             )  # only pass value part of memory to target network
             for i in range(offset, len(batch["memory"]), self.policy.sequence_length)
         ]

From b97b1e535af6006bd864249693c78460bc721877 Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Mon, 10 Aug 2020 17:40:47 -0700
Subject: [PATCH 03/17] Fix policy memory storinig

---
 ml-agents/mlagents/trainers/policy/torch_policy.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ml-agents/mlagents/trainers/policy/torch_policy.py b/ml-agents/mlagents/trainers/policy/torch_policy.py
index d6dd822646..9d935d4676 100644
--- a/ml-agents/mlagents/trainers/policy/torch_policy.py
+++ b/ml-agents/mlagents/trainers/policy/torch_policy.py
@@ -186,7 +186,7 @@ def evaluate(
         run_out["value"] = np.mean(list(run_out["value_heads"].values()), 0)
         run_out["learning_rate"] = 0.0
         if self.use_recurrent:
-            run_out["memories"] = memories.detach().cpu().numpy()
+            run_out["memory_out"] = memories.detach().cpu().numpy().squeeze(0)
         return run_out
 
     def get_action(

From cd509ddbbdc3e4025040105f9d521034a37fdf3a Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Mon, 10 Aug 2020 18:12:27 -0700
Subject: [PATCH 04/17] Fix SeparateActorCritic and add test

---
 ml-agents/mlagents/trainers/tests/torch/test_networks.py | 6 +++++-
 ml-agents/mlagents/trainers/torch/networks.py            | 2 +-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/ml-agents/mlagents/trainers/tests/torch/test_networks.py b/ml-agents/mlagents/trainers/tests/torch/test_networks.py
index ff5209b676..19030aeafc 100644
--- a/ml-agents/mlagents/trainers/tests/torch/test_networks.py
+++ b/ml-agents/mlagents/trainers/tests/torch/test_networks.py
@@ -203,7 +203,11 @@ def test_actor_critic(ac_type, lstm):
             assert value_out[stream].shape == (1,)
 
     # Test get_dist_and_value
-    dists, value_out, _ = actor.get_dist_and_value([sample_obs], [], memories=memories)
+    dists, value_out, mem_out = actor.get_dist_and_value(
+        [sample_obs], [], memories=memories
+    )
+    if mem_out is not None:
+        assert mem_out.shape == memories.shape
     for dist in dists:
         assert isinstance(dist, GaussianDistInstance)
     for stream in stream_names:
diff --git a/ml-agents/mlagents/trainers/torch/networks.py b/ml-agents/mlagents/trainers/torch/networks.py
index 15f0d92e2d..aff61ce0f6 100644
--- a/ml-agents/mlagents/trainers/torch/networks.py
+++ b/ml-agents/mlagents/trainers/torch/networks.py
@@ -463,7 +463,7 @@ def get_dist_and_value(
             vec_inputs, vis_inputs, memories=critic_mem, sequence_length=sequence_length
         )
         if self.use_lstm:
-            mem_out = torch.cat([actor_mem_outs, critic_mem_outs], dim=1)
+            mem_out = torch.cat([actor_mem_outs, critic_mem_outs], dim=-1)
         else:
             mem_out = None
         return dists, value_outputs, mem_out

From 07bb4c0818887dab74e75236f51f6ca1533bde80 Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Tue, 11 Aug 2020 19:15:30 -0700
Subject: [PATCH 05/17] Use loss masks in PPO.

---
 .../mlagents/trainers/ppo/optimizer_torch.py  | 44 +++++++++++++------
 1 file changed, 31 insertions(+), 13 deletions(-)

diff --git a/ml-agents/mlagents/trainers/ppo/optimizer_torch.py b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
index 9bbb7b51c8..13fdc12e01 100644
--- a/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
+++ b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
@@ -61,12 +61,15 @@ def ppo_value_loss(
         old_values: Dict[str, torch.Tensor],
         returns: Dict[str, torch.Tensor],
         epsilon: float,
+        loss_masks: torch.Tensor,
     ) -> torch.Tensor:
         """
-        Creates training-specific Tensorflow ops for PPO models.
-        :param returns:
-        :param old_values:
-        :param values:
+        Evaluates value loss for PPO.
+        :param values: Value output of the current network.
+        :param old_values: Value stored with experiences in buffer.
+        :param returns: Computed returns.
+        :param epsilon: Clipping value for value estimate.
+        :param loss_mask: Mask for losses. Used with LSTM to ignore 0'ed out experiences.
         """
         value_losses = []
         for name, head in values.items():
@@ -77,18 +80,25 @@ def ppo_value_loss(
             )
             v_opt_a = (returns_tensor - head) ** 2
             v_opt_b = (returns_tensor - clipped_value_estimate) ** 2
-            value_loss = torch.mean(torch.max(v_opt_a, v_opt_b))
+            masked_loss = torch.max(v_opt_a, v_opt_b) * loss_masks
+            value_loss = torch.mean(masked_loss)
             value_losses.append(value_loss)
         value_loss = torch.mean(torch.stack(value_losses))
         return value_loss
 
-    def ppo_policy_loss(self, advantages, log_probs, old_log_probs, masks):
+    def ppo_policy_loss(
+        self,
+        advantages: torch.Tensor,
+        log_probs: torch.Tensor,
+        old_log_probs: torch.Tensor,
+        loss_masks: torch.Tensor,
+    ) -> torch.Tensor:
         """
-        Creates training-specific Tensorflow ops for PPO models.
-        :param masks:
-        :param advantages:
+        Evaluate PPO policy loss.
+        :param advantages: Computed advantages.
         :param log_probs: Current policy probabilities
         :param old_log_probs: Past policy probabilities
+        :param loss_masks: Mask for losses. Used with LSTM to ignore 0'ed out experiences.
         """
         advantage = advantages.unsqueeze(-1)
 
@@ -99,7 +109,8 @@ def ppo_policy_loss(self, advantages, log_probs, old_log_probs, masks):
         p_opt_b = (
             torch.clamp(r_theta, 1.0 - decay_epsilon, 1.0 + decay_epsilon) * advantage
         )
-        policy_loss = -torch.mean(torch.min(p_opt_a, p_opt_b))
+        masked_loss = torch.min(p_opt_a, p_opt_b) * loss_masks
+        policy_loss = -torch.mean(masked_loss)
         return policy_loss
 
     @timed
@@ -153,14 +164,21 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
             memories=memories,
             seq_len=self.policy.sequence_length,
         )
-        value_loss = self.ppo_value_loss(values, old_values, returns, decay_eps)
+        loss_masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.int32)
+        value_loss = self.ppo_value_loss(
+            values, old_values, returns, decay_eps, loss_masks
+        )
         policy_loss = self.ppo_policy_loss(
             ModelUtils.list_to_tensor(batch["advantages"]),
             log_probs,
             ModelUtils.list_to_tensor(batch["action_probs"]),
-            ModelUtils.list_to_tensor(batch["masks"], dtype=torch.int32),
+            loss_masks,
+        )
+        loss = (
+            policy_loss
+            + 0.5 * value_loss
+            - decay_bet * torch.mean(entropy * loss_masks)
         )
-        loss = policy_loss + 0.5 * value_loss - decay_bet * torch.mean(entropy)
 
         # Set optimizer learning rate
         ModelUtils.update_learning_rate(self.optimizer, decay_lr)

From 0a3c795cee4197003d5c7f5e96b03925e14f9896 Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Tue, 11 Aug 2020 19:46:34 -0700
Subject: [PATCH 06/17] Proper shape of masks

---
 ml-agents/mlagents/trainers/ppo/optimizer_torch.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ml-agents/mlagents/trainers/ppo/optimizer_torch.py b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
index 13fdc12e01..1232429f36 100644
--- a/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
+++ b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
@@ -109,7 +109,7 @@ def ppo_policy_loss(
         p_opt_b = (
             torch.clamp(r_theta, 1.0 - decay_epsilon, 1.0 + decay_epsilon) * advantage
         )
-        masked_loss = torch.min(p_opt_a, p_opt_b) * loss_masks
+        masked_loss = torch.min(p_opt_a, p_opt_b).flatten() * loss_masks
         policy_loss = -torch.mean(masked_loss)
         return policy_loss
 
@@ -164,7 +164,7 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
             memories=memories,
             seq_len=self.policy.sequence_length,
         )
-        loss_masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.int32)
+        loss_masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.float32)
         value_loss = self.ppo_value_loss(
             values, old_values, returns, decay_eps, loss_masks
         )
@@ -177,7 +177,7 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
         loss = (
             policy_loss
             + 0.5 * value_loss
-            - decay_bet * torch.mean(entropy * loss_masks)
+            - decay_bet * torch.mean(entropy.flatten() * loss_masks)
         )
 
         # Set optimizer learning rate

From 2337d15c09e2cdd91ec5d0c08eb63a2ba5d5e297 Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Tue, 11 Aug 2020 20:04:48 -0700
Subject: [PATCH 07/17] Proper mask mean for PPO

---
 ml-agents/mlagents/trainers/ppo/optimizer_torch.py | 14 +++++++-------
 ml-agents/mlagents/trainers/torch/utils.py         | 10 ++++++++++
 2 files changed, 17 insertions(+), 7 deletions(-)

diff --git a/ml-agents/mlagents/trainers/ppo/optimizer_torch.py b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
index 1232429f36..b330bce0fb 100644
--- a/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
+++ b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
@@ -80,8 +80,7 @@ def ppo_value_loss(
             )
             v_opt_a = (returns_tensor - head) ** 2
             v_opt_b = (returns_tensor - clipped_value_estimate) ** 2
-            masked_loss = torch.max(v_opt_a, v_opt_b) * loss_masks
-            value_loss = torch.mean(masked_loss)
+            value_loss = ModelUtils.masked_mean(torch.max(v_opt_a, v_opt_b), loss_masks)
             value_losses.append(value_loss)
         value_loss = torch.mean(torch.stack(value_losses))
         return value_loss
@@ -109,8 +108,9 @@ def ppo_policy_loss(
         p_opt_b = (
             torch.clamp(r_theta, 1.0 - decay_epsilon, 1.0 + decay_epsilon) * advantage
         )
-        masked_loss = torch.min(p_opt_a, p_opt_b).flatten() * loss_masks
-        policy_loss = -torch.mean(masked_loss)
+        policy_loss = -1 * ModelUtils.masked_mean(
+            torch.min(p_opt_a, p_opt_b).flatten(), loss_masks
+        )
         return policy_loss
 
     @timed
@@ -138,7 +138,7 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
         if self.policy.use_continuous_act:
             actions = ModelUtils.list_to_tensor(batch["actions"]).unsqueeze(-1)
         else:
-            actions = ModelUtils.list_to_tensor(batch["actions"], dtype=torch.long)
+            actions = ModelUtils.list_to_tensor(batch["actions"], dtype=torch.bool)
 
         memories = [
             ModelUtils.list_to_tensor(batch["memory"][i])
@@ -164,7 +164,7 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
             memories=memories,
             seq_len=self.policy.sequence_length,
         )
-        loss_masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.float32)
+        loss_masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.bool)
         value_loss = self.ppo_value_loss(
             values, old_values, returns, decay_eps, loss_masks
         )
@@ -177,7 +177,7 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
         loss = (
             policy_loss
             + 0.5 * value_loss
-            - decay_bet * torch.mean(entropy.flatten() * loss_masks)
+            - decay_bet * ModelUtils.masked_mean(entropy.flatten(), loss_masks)
         )
 
         # Set optimizer learning rate
diff --git a/ml-agents/mlagents/trainers/torch/utils.py b/ml-agents/mlagents/trainers/torch/utils.py
index baa99c98ef..ba6b7d57a0 100644
--- a/ml-agents/mlagents/trainers/torch/utils.py
+++ b/ml-agents/mlagents/trainers/torch/utils.py
@@ -284,3 +284,13 @@ def get_probs_and_entropy(
         else:
             all_probs = torch.cat(all_probs_list, dim=-1)
         return log_probs, entropies, all_probs
+
+    @staticmethod
+    def masked_mean(tensor: torch.Tensor, masks: torch.Tensor) -> torch.Tensor:
+        """
+        Returns the mean of the tensor but ignoring the values specified by masks.
+        Used for masking out loss functions.
+        :param tensor: Tensor which needs mean computation.
+        :param masks: Boolean tensor of masks with same dimension as tensor.
+        """
+        return (tensor * masks).sum() / masks.float().sum()

From 1f69102f45dd5bfd7a0c8e63fdee701af7a25632 Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Tue, 11 Aug 2020 20:17:47 -0700
Subject: [PATCH 08/17] Fix dtype for actions

---
 ml-agents/mlagents/trainers/ppo/optimizer_torch.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ml-agents/mlagents/trainers/ppo/optimizer_torch.py b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
index b330bce0fb..e162166481 100644
--- a/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
+++ b/ml-agents/mlagents/trainers/ppo/optimizer_torch.py
@@ -138,7 +138,7 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
         if self.policy.use_continuous_act:
             actions = ModelUtils.list_to_tensor(batch["actions"]).unsqueeze(-1)
         else:
-            actions = ModelUtils.list_to_tensor(batch["actions"], dtype=torch.bool)
+            actions = ModelUtils.list_to_tensor(batch["actions"], dtype=torch.long)
 
         memories = [
             ModelUtils.list_to_tensor(batch["memory"][i])

From c0a77f76b6f9b1c6a3187b9c56e49a7228605e7b Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Wed, 12 Aug 2020 14:13:17 -0700
Subject: [PATCH 09/17] Proper initialization and SAC masking

---
 .../mlagents/trainers/sac/optimizer_torch.py  | 27 +++++++++----------
 ml-agents/mlagents/trainers/torch/layers.py   | 24 +++++++++++++++++
 ml-agents/mlagents/trainers/torch/networks.py | 13 +++++----
 3 files changed, 43 insertions(+), 21 deletions(-)

diff --git a/ml-agents/mlagents/trainers/sac/optimizer_torch.py b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
index 3ba92e6c1b..6d110f99f5 100644
--- a/ml-agents/mlagents/trainers/sac/optimizer_torch.py
+++ b/ml-agents/mlagents/trainers/sac/optimizer_torch.py
@@ -194,11 +194,11 @@ def sac_q_loss(
                     * self.gammas[i]
                     * target_values[name]
                 )
-            _q1_loss = 0.5 * torch.mean(
-                loss_masks * torch.nn.functional.mse_loss(q_backup, q1_stream)
+            _q1_loss = 0.5 * ModelUtils.masked_mean(
+                torch.nn.functional.mse_loss(q_backup, q1_stream), loss_masks
             )
-            _q2_loss = 0.5 * torch.mean(
-                loss_masks * torch.nn.functional.mse_loss(q_backup, q2_stream)
+            _q2_loss = 0.5 * ModelUtils.masked_mean(
+                torch.nn.functional.mse_loss(q_backup, q2_stream), loss_masks
             )
 
             q1_losses.append(_q1_loss)
@@ -258,8 +258,8 @@ def sac_value_loss(
                     v_backup = min_policy_qs[name] - torch.sum(
                         _ent_coef * log_probs, dim=1
                     )
-                value_loss = 0.5 * torch.mean(
-                    loss_masks * torch.nn.functional.mse_loss(values[name], v_backup)
+                value_loss = 0.5 * ModelUtils.masked_mean(
+                    torch.nn.functional.mse_loss(values[name], v_backup), loss_masks
                 )
                 value_losses.append(value_loss)
         else:
@@ -278,9 +278,9 @@ def sac_value_loss(
                     v_backup = min_policy_qs[name] - torch.mean(
                         branched_ent_bonus, axis=0
                     )
-                value_loss = 0.5 * torch.mean(
-                    loss_masks
-                    * torch.nn.functional.mse_loss(values[name], v_backup.squeeze())
+                value_loss = 0.5 * ModelUtils.masked_mean(
+                    torch.nn.functional.mse_loss(values[name], v_backup.squeeze()),
+                    loss_masks,
                 )
                 value_losses.append(value_loss)
         value_loss = torch.mean(torch.stack(value_losses))
@@ -300,7 +300,7 @@ def sac_policy_loss(
         if not discrete:
             mean_q1 = mean_q1.unsqueeze(1)
             batch_policy_loss = torch.mean(_ent_coef * log_probs - mean_q1, dim=1)
-            policy_loss = torch.mean(loss_masks * batch_policy_loss)
+            policy_loss = ModelUtils.masked_mean(batch_policy_loss, loss_masks)
         else:
             action_probs = log_probs.exp()
             branched_per_action_ent = ModelUtils.break_into_branches(
@@ -347,9 +347,8 @@ def sac_entropy_loss(
                 target_current_diff = torch.squeeze(
                     target_current_diff_branched, axis=2
                 )
-            entropy_loss = -torch.mean(
-                loss_masks
-                * torch.mean(self._log_ent_coef * target_current_diff, axis=1)
+            entropy_loss = -1 * ModelUtils.masked_mean(
+                torch.mean(self._log_ent_coef * target_current_diff, axis=1), loss_masks
             )
 
         return entropy_loss
@@ -497,7 +496,7 @@ def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
                 memories=next_memories,
                 sequence_length=self.policy.sequence_length,
             )
-        masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.int32)
+        masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.bool)
         use_discrete = not self.policy.use_continuous_act
         dones = ModelUtils.list_to_tensor(batch["done"])
 
diff --git a/ml-agents/mlagents/trainers/torch/layers.py b/ml-agents/mlagents/trainers/torch/layers.py
index 8dbb1cbcb4..d1c68887df 100644
--- a/ml-agents/mlagents/trainers/torch/layers.py
+++ b/ml-agents/mlagents/trainers/torch/layers.py
@@ -46,3 +46,27 @@ def linear_layer(
     layer.weight.data *= kernel_gain
     _init_methods[bias_init](layer.bias.data)
     return layer
+
+
+def lstm_layer(
+    input_size: int,
+    hidden_size: int,
+    num_layers: int = 1,
+    batch_first: bool = True,
+    forget_bias: float = 1.0,
+    kernel_init: Initialization = Initialization.XavierGlorotUniform,
+    bias_init: Initialization = Initialization.Zero,
+) -> torch.nn.Module:
+    """
+    Creates a torch.nn.LSTM and initializes its weights and biases. Provides a
+    forget_bias offset like is done in TensorFlow.
+    """
+    lstm = torch.nn.LSTM(input_size, hidden_size, num_layers, batch_first=batch_first)
+    # Add forget_bias to forget gate bias
+    for name, param in lstm.named_parameters():
+        if "weight" in name:
+            _init_methods[kernel_init](param.data)
+        elif "bias" in name:
+            _init_methods[bias_init](param.data)
+            param.data[hidden_size : 2 * hidden_size].add_(forget_bias)
+    return lstm
diff --git a/ml-agents/mlagents/trainers/torch/networks.py b/ml-agents/mlagents/trainers/torch/networks.py
index aff61ce0f6..b60cfbe543 100644
--- a/ml-agents/mlagents/trainers/torch/networks.py
+++ b/ml-agents/mlagents/trainers/torch/networks.py
@@ -14,6 +14,7 @@
 from mlagents.trainers.settings import NetworkSettings
 from mlagents.trainers.torch.utils import ModelUtils
 from mlagents.trainers.torch.decoders import ValueHeads
+from mlagents.trainers.torch.layers import lstm_layer
 
 ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
 EncoderFunction = Callable[
@@ -50,7 +51,7 @@ def __init__(
         )
 
         if self.use_lstm:
-            self.lstm = nn.LSTM(self.h_size, self.m_size // 2, 1)
+            self.lstm = lstm_layer(self.h_size, self.m_size // 2, batch_first=True)
         else:
             self.lstm = None
 
@@ -101,13 +102,11 @@ def forward(
             raise Exception("No valid inputs to network.")
 
         if self.use_lstm:
-            encoding = encoding.view([sequence_length, -1, self.h_size])
+            # Resize to (batch, sequence length, encoding size)
+            encoding = encoding.reshape([-1, sequence_length, self.h_size])
             memories = torch.split(memories, self.m_size // 2, dim=-1)
-            encoding, memories = self.lstm(
-                encoding.contiguous(),
-                (memories[0].contiguous(), memories[1].contiguous()),
-            )
-            encoding = encoding.view([-1, self.m_size // 2])
+            encoding, memories = self.lstm(encoding, (memories[0], memories[1]))
+            encoding = encoding.reshape([-1, self.m_size // 2])
             memories = torch.cat(memories, dim=-1)
         return encoding, memories
 

From f404834ea551eed1ec649af72591ce59ba1cf8d8 Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Wed, 12 Aug 2020 15:53:13 -0700
Subject: [PATCH 10/17] Experimental amrl layer

---
 ml-agents/mlagents/trainers/torch/layers.py   | 39 +++++++++++++++++++
 ml-agents/mlagents/trainers/torch/networks.py |  6 +--
 2 files changed, 42 insertions(+), 3 deletions(-)

diff --git a/ml-agents/mlagents/trainers/torch/layers.py b/ml-agents/mlagents/trainers/torch/layers.py
index d1c68887df..4a8ed07374 100644
--- a/ml-agents/mlagents/trainers/torch/layers.py
+++ b/ml-agents/mlagents/trainers/torch/layers.py
@@ -70,3 +70,42 @@ def lstm_layer(
             _init_methods[bias_init](param.data)
             param.data[hidden_size : 2 * hidden_size].add_(forget_bias)
     return lstm
+
+
+class AMRLMax(torch.nn.Module):
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        num_layers: int = 1,
+        batch_first: bool = True,
+        forget_bias: float = 1.0,
+        kernel_init: Initialization = Initialization.XavierGlorotUniform,
+        bias_init: Initialization = Initialization.Zero,
+    ):
+        super().__init__()
+        self.lstm = lstm_layer(
+            input_size,
+            hidden_size,
+            num_layers,
+            batch_first,
+            forget_bias,
+            kernel_init,
+            bias_init,
+        )
+        self.hidden_size = hidden_size
+
+    def forward(self, input_tensor, h0_c0):
+        hidden = h0_c0
+        all_out = []
+        m = None
+        for t in range(input_tensor.shape[1]):
+            out, hidden = self.lstm(input_tensor[:, t : t + 1, :], hidden)
+            h_half, other_half = torch.split(out, self.hidden_size // 2, dim=-1)
+            if m is None:
+                m = h_half
+            else:
+                m = torch.max(m, h_half)
+            out = torch.cat([m, other_half])
+            all_out.append(out)
+        return torch.cat(all_out, dim=1), hidden
diff --git a/ml-agents/mlagents/trainers/torch/networks.py b/ml-agents/mlagents/trainers/torch/networks.py
index b60cfbe543..264cef52a7 100644
--- a/ml-agents/mlagents/trainers/torch/networks.py
+++ b/ml-agents/mlagents/trainers/torch/networks.py
@@ -14,7 +14,7 @@
 from mlagents.trainers.settings import NetworkSettings
 from mlagents.trainers.torch.utils import ModelUtils
 from mlagents.trainers.torch.decoders import ValueHeads
-from mlagents.trainers.torch.layers import lstm_layer
+from mlagents.trainers.torch.layers import AMRLMax
 
 ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
 EncoderFunction = Callable[
@@ -51,9 +51,9 @@ def __init__(
         )
 
         if self.use_lstm:
-            self.lstm = lstm_layer(self.h_size, self.m_size // 2, batch_first=True)
+            self.lstm = AMRLMax(self.h_size, self.m_size // 2, batch_first=True)
         else:
-            self.lstm = None
+            self.lstm = None  # type: ignore
 
     def update_normalization(self, vec_inputs: List[torch.Tensor]) -> None:
         for vec_input, vec_enc in zip(vec_inputs, self.vector_encoders):

From beab310ec471d2aa06b534a38e6f04965b76e3a4 Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Wed, 12 Aug 2020 16:13:59 -0700
Subject: [PATCH 11/17] Add extra FF layer

---
 ml-agents/mlagents/trainers/torch/layers.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/ml-agents/mlagents/trainers/torch/layers.py b/ml-agents/mlagents/trainers/torch/layers.py
index 4a8ed07374..bb94a28edd 100644
--- a/ml-agents/mlagents/trainers/torch/layers.py
+++ b/ml-agents/mlagents/trainers/torch/layers.py
@@ -82,6 +82,7 @@ def __init__(
         forget_bias: float = 1.0,
         kernel_init: Initialization = Initialization.XavierGlorotUniform,
         bias_init: Initialization = Initialization.Zero,
+        num_post_layers: int = 1,
     ):
         super().__init__()
         self.lstm = lstm_layer(
@@ -94,6 +95,18 @@ def __init__(
             bias_init,
         )
         self.hidden_size = hidden_size
+        self.layers = []
+        for _ in range(num_post_layers):
+            self.layers.append(
+                linear_layer(
+                    input_size,
+                    hidden_size,
+                    kernel_init=Initialization.KaimingHeNormal,
+                    kernel_gain=1.0,
+                )
+            )
+            self.layers.append(Swish())
+        self.seq_layers = torch.nn.Sequential(*self.layers)
 
     def forward(self, input_tensor, h0_c0):
         hidden = h0_c0
@@ -108,4 +121,5 @@ def forward(self, input_tensor, h0_c0):
                 m = torch.max(m, h_half)
             out = torch.cat([m, other_half])
             all_out.append(out)
-        return torch.cat(all_out, dim=1), hidden
+        full_out = self.seq_layers(torch.cat(all_out, dim=1))
+        return full_out, hidden

From 6fece65403d59d23a4f220966a61d4c9b3cdd14c Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Wed, 12 Aug 2020 16:58:30 -0700
Subject: [PATCH 12/17] Faster implementation

---
 ml-agents/mlagents/trainers/torch/layers.py | 25 ++++++++++++---------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/ml-agents/mlagents/trainers/torch/layers.py b/ml-agents/mlagents/trainers/torch/layers.py
index bb94a28edd..ce56347c29 100644
--- a/ml-agents/mlagents/trainers/torch/layers.py
+++ b/ml-agents/mlagents/trainers/torch/layers.py
@@ -99,7 +99,7 @@ def __init__(
         for _ in range(num_post_layers):
             self.layers.append(
                 linear_layer(
-                    input_size,
+                    hidden_size,
                     hidden_size,
                     kernel_init=Initialization.KaimingHeNormal,
                     kernel_gain=1.0,
@@ -110,16 +110,19 @@ def __init__(
 
     def forward(self, input_tensor, h0_c0):
         hidden = h0_c0
-        all_out = []
+        all_c = []
         m = None
-        for t in range(input_tensor.shape[1]):
-            out, hidden = self.lstm(input_tensor[:, t : t + 1, :], hidden)
-            h_half, other_half = torch.split(out, self.hidden_size // 2, dim=-1)
+        lstm_out, hidden = self.lstm(input_tensor, hidden)
+        h_half, other_half = torch.split(lstm_out, self.hidden_size // 2, dim=-1)
+        for t in range(h_half.shape[1]):
+            h_half_subt = h_half[:, t : t + 1, :]
             if m is None:
-                m = h_half
+                m = h_half_subt
             else:
-                m = torch.max(m, h_half)
-            out = torch.cat([m, other_half])
-            all_out.append(out)
-        full_out = self.seq_layers(torch.cat(all_out, dim=1))
-        return full_out, hidden
+                m = torch.max(m, h_half_subt)
+            all_c.append(m)
+        concat_c = torch.cat(all_c, dim=1)
+        concat_out = torch.cat([concat_c, other_half], dim=-1)
+        full_out = self.seq_layers(concat_out.reshape([-1, self.hidden_size]))
+        full_out = full_out.reshape([-1, input_tensor.shape[1], self.hidden_size])
+        return concat_out, hidden

From eac1dc96dbebb443e5dba1e6846eb8a24568eee8 Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Wed, 12 Aug 2020 17:11:56 -0700
Subject: [PATCH 13/17] Add comment

---
 ml-agents/mlagents/trainers/torch/layers.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/ml-agents/mlagents/trainers/torch/layers.py b/ml-agents/mlagents/trainers/torch/layers.py
index ce56347c29..58c549f0d5 100644
--- a/ml-agents/mlagents/trainers/torch/layers.py
+++ b/ml-agents/mlagents/trainers/torch/layers.py
@@ -73,6 +73,11 @@ def lstm_layer(
 
 
 class AMRLMax(torch.nn.Module):
+    """
+    Implements Aggregation for LSTM as described here:
+    https://www.microsoft.com/en-us/research/publication/amrl-aggregated-memory-for-reinforcement-learning/
+    """
+
     def __init__(
         self,
         input_size: int,

From d2e31aadf26145b01a4419b8aea9a7f19c9a174f Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Wed, 12 Aug 2020 18:16:44 -0700
Subject: [PATCH 14/17] Passthrough max

---
 ml-agents/mlagents/trainers/torch/layers.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/ml-agents/mlagents/trainers/torch/layers.py b/ml-agents/mlagents/trainers/torch/layers.py
index 58c549f0d5..eba616cd19 100644
--- a/ml-agents/mlagents/trainers/torch/layers.py
+++ b/ml-agents/mlagents/trainers/torch/layers.py
@@ -124,10 +124,19 @@ def forward(self, input_tensor, h0_c0):
             if m is None:
                 m = h_half_subt
             else:
-                m = torch.max(m, h_half_subt)
+                m = AMRLMax.PassthroughMax.apply(m, h_half_subt)
             all_c.append(m)
         concat_c = torch.cat(all_c, dim=1)
         concat_out = torch.cat([concat_c, other_half], dim=-1)
         full_out = self.seq_layers(concat_out.reshape([-1, self.hidden_size]))
         full_out = full_out.reshape([-1, input_tensor.shape[1], self.hidden_size])
         return concat_out, hidden
+
+    class PassthroughMax(torch.autograd.Function):
+        @staticmethod
+        def forward(ctx, tensor1, tensor2):
+            return torch.max(tensor1, tensor2)
+
+        @staticmethod
+        def backward(ctx, grad_output):
+            return grad_output.clone(), grad_output.clone()

From bf485a2006adae9595b42c8be461b2b71d7c614c Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Fri, 14 Aug 2020 11:59:31 -0700
Subject: [PATCH 15/17] Memory size abstraction and fixes

---
 .../mlagents/trainers/policy/torch_policy.py  |  1 +
 ml-agents/mlagents/trainers/torch/layers.py   | 26 ++++++++++++-------
 ml-agents/mlagents/trainers/torch/networks.py | 26 ++++++++++++++-----
 3 files changed, 38 insertions(+), 15 deletions(-)

diff --git a/ml-agents/mlagents/trainers/policy/torch_policy.py b/ml-agents/mlagents/trainers/policy/torch_policy.py
index 9d935d4676..4d645e1fba 100644
--- a/ml-agents/mlagents/trainers/policy/torch_policy.py
+++ b/ml-agents/mlagents/trainers/policy/torch_policy.py
@@ -83,6 +83,7 @@ def __init__(
             conditional_sigma=self.condition_sigma_on_obs,
             tanh_squash=tanh_squash,
         )
+        self.m_size = self.actor_critic.memory_size
 
         self.actor_critic.to(TestingConfiguration.device)
 
diff --git a/ml-agents/mlagents/trainers/torch/layers.py b/ml-agents/mlagents/trainers/torch/layers.py
index b1303338fb..2d353db7df 100644
--- a/ml-agents/mlagents/trainers/torch/layers.py
+++ b/ml-agents/mlagents/trainers/torch/layers.py
@@ -125,24 +125,32 @@ def __init__(
             self.layers.append(Swish())
         self.seq_layers = torch.nn.Sequential(*self.layers)
 
-    def forward(self, input_tensor, h0_c0):
-        hidden = h0_c0
+    @property
+    def memory_size(self) -> int:
+        return self.hidden_size // 2 + 2 * self.hidden_size
+
+    def forward(self, input_tensor, memories):
+        # memories is 1/2 * hidden_size (accumulant) + hidden_size/2 (h0) + hidden_size/2 (c0)
+        acc, h0, c0 = torch.split(
+            memories,
+            [self.hidden_size // 2, self.hidden_size, self.hidden_size],
+            dim=-1,
+        )
+        hidden = (h0, c0)
         all_c = []
-        m = None
-        lstm_out, hidden = self.lstm(input_tensor, hidden)
+        m = acc.permute([1, 0, 2])
+        lstm_out, (h0_out, c0_out) = self.lstm(input_tensor, hidden)
         h_half, other_half = torch.split(lstm_out, self.hidden_size // 2, dim=-1)
         for t in range(h_half.shape[1]):
             h_half_subt = h_half[:, t : t + 1, :]
-            if m is None:
-                m = h_half_subt
-            else:
-                m = AMRLMax.PassthroughMax.apply(m, h_half_subt)
+            m = AMRLMax.PassthroughMax.apply(m, h_half_subt)
             all_c.append(m)
         concat_c = torch.cat(all_c, dim=1)
         concat_out = torch.cat([concat_c, other_half], dim=-1)
         full_out = self.seq_layers(concat_out.reshape([-1, self.hidden_size]))
         full_out = full_out.reshape([-1, input_tensor.shape[1], self.hidden_size])
-        return concat_out, hidden
+        output_mem = torch.cat([m.permute([1, 0, 2]), h0_out, c0_out], dim=-1)
+        return concat_out, output_mem
 
     class PassthroughMax(torch.autograd.Function):
         @staticmethod
diff --git a/ml-agents/mlagents/trainers/torch/networks.py b/ml-agents/mlagents/trainers/torch/networks.py
index 591568aece..87ff8c84cc 100644
--- a/ml-agents/mlagents/trainers/torch/networks.py
+++ b/ml-agents/mlagents/trainers/torch/networks.py
@@ -14,7 +14,7 @@
 from mlagents.trainers.settings import NetworkSettings
 from mlagents.trainers.torch.utils import ModelUtils
 from mlagents.trainers.torch.decoders import ValueHeads
-from mlagents.trainers.torch.layers import lstm_layer
+from mlagents.trainers.torch.layers import AMRLMax
 
 ActivationFunction = Callable[[torch.Tensor], torch.Tensor]
 EncoderFunction = Callable[
@@ -51,7 +51,7 @@ def __init__(
         )
 
         if self.use_lstm:
-            self.lstm = lstm_layer(self.h_size, self.m_size // 2, batch_first=True)
+            self.lstm = AMRLMax(self.h_size, self.m_size // 2, batch_first=True)
         else:
             self.lstm = None  # type: ignore
 
@@ -104,10 +104,10 @@ def forward(
         if self.use_lstm:
             # Resize to (batch, sequence length, encoding size)
             encoding = encoding.reshape([-1, sequence_length, self.h_size])
-            memories = torch.split(memories, self.m_size // 2, dim=-1)
+            # memories = torch.split(memories, self.m_size // 2, dim=-1)
             encoding, memories = self.lstm(encoding, memories)
             encoding = encoding.reshape([-1, self.m_size // 2])
-            memories = torch.cat(memories, dim=-1)
+            # memories = torch.cat(memories, dim=-1)
         return encoding, memories
 
 
@@ -257,7 +257,7 @@ def __init__(
         self.act_type = act_type
         self.act_size = act_size
         self.version_number = torch.nn.Parameter(torch.Tensor([2.0]))
-        self.memory_size = torch.nn.Parameter(torch.Tensor([0]))
+        self.memory_size_param = torch.nn.Parameter(torch.Tensor([0]))
         self.is_continuous_int = torch.nn.Parameter(
             torch.Tensor([int(act_type == ActionType.CONTINUOUS)])
         )
@@ -279,6 +279,13 @@ def __init__(
                 self.encoding_size, act_size
             )
 
+    @property
+    def memory_size(self) -> int:
+        if self.network_body.lstm is not None:
+            return self.network_body.lstm.memory_size
+        else:
+            return 0
+
     def update_normalization(self, vector_obs: List[torch.Tensor]) -> None:
         self.network_body.update_normalization(vector_obs)
 
@@ -327,7 +334,7 @@ def forward(
             sampled_actions,
             dists[0].pdf(sampled_actions),
             self.version_number,
-            self.memory_size,
+            self.memory_size_param,
             self.is_continuous_int,
             self.act_size_vector,
         )
@@ -425,6 +432,13 @@ def __init__(
             stream_names, observation_shapes, use_network_settings
         )
 
+    @property
+    def memory_size(self) -> int:
+        if self.network_body.lstm is not None:
+            return 2 * self.network_body.lstm.memory_size
+        else:
+            return 0
+
     def critic_pass(
         self,
         vec_inputs: List[torch.Tensor],

From bd90e29c7b402f6a838d52d5b8baf645bc1bd16b Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Fri, 14 Aug 2020 14:25:28 -0700
Subject: [PATCH 16/17] Fix SeparateActorCritic

---
 ml-agents/mlagents/trainers/torch/networks.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ml-agents/mlagents/trainers/torch/networks.py b/ml-agents/mlagents/trainers/torch/networks.py
index 87ff8c84cc..8d73912667 100644
--- a/ml-agents/mlagents/trainers/torch/networks.py
+++ b/ml-agents/mlagents/trainers/torch/networks.py
@@ -449,7 +449,7 @@ def critic_pass(
         actor_mem, critic_mem = None, None
         if self.use_lstm:
             # Use only the back half of memories for critic
-            actor_mem, critic_mem = torch.split(memories, self.half_mem_size, -1)
+            actor_mem, critic_mem = torch.split(memories, self.memory_size, -1)
         value_outputs, critic_mem_out = self.critic(
             vec_inputs, vis_inputs, memories=critic_mem, sequence_length=sequence_length
         )
@@ -470,7 +470,7 @@ def get_dist_and_value(
     ) -> Tuple[List[DistInstance], Dict[str, torch.Tensor], torch.Tensor]:
         if self.use_lstm:
             # Use only the back half of memories for critic and actor
-            actor_mem, critic_mem = torch.split(memories, self.half_mem_size, dim=-1)
+            actor_mem, critic_mem = torch.split(memories, self.memory_size, dim=-1)
         else:
             critic_mem = None
             actor_mem = None

From 7f4ea51b226759c3c620bc1aa2772fef6ead2b5a Mon Sep 17 00:00:00 2001
From: Ervin Teng <ervin@unity3d.com>
Date: Fri, 14 Aug 2020 15:00:17 -0700
Subject: [PATCH 17/17] Fix SeparateActorCritic

---
 ml-agents/mlagents/trainers/torch/networks.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ml-agents/mlagents/trainers/torch/networks.py b/ml-agents/mlagents/trainers/torch/networks.py
index 8d73912667..f606aafd81 100644
--- a/ml-agents/mlagents/trainers/torch/networks.py
+++ b/ml-agents/mlagents/trainers/torch/networks.py
@@ -449,7 +449,7 @@ def critic_pass(
         actor_mem, critic_mem = None, None
         if self.use_lstm:
             # Use only the back half of memories for critic
-            actor_mem, critic_mem = torch.split(memories, self.memory_size, -1)
+            actor_mem, critic_mem = torch.split(memories, self.memory_size // 2, -1)
         value_outputs, critic_mem_out = self.critic(
             vec_inputs, vis_inputs, memories=critic_mem, sequence_length=sequence_length
         )
@@ -470,7 +470,7 @@ def get_dist_and_value(
     ) -> Tuple[List[DistInstance], Dict[str, torch.Tensor], torch.Tensor]:
         if self.use_lstm:
             # Use only the back half of memories for critic and actor
-            actor_mem, critic_mem = torch.split(memories, self.memory_size, dim=-1)
+            actor_mem, critic_mem = torch.split(memories, self.memory_size // 2, dim=-1)
         else:
             critic_mem = None
             actor_mem = None