[add-fire] Add LSTM to SAC, LSTM fixes and initializations (#4324)

Author: Ervin T

ml-agents/mlagents/trainers/optimizer/torch_optimizer.py

@@ -1,16 +1,15 @@
 from typing import Dict, Optional, Tuple, List
 import torch
 import numpy as np
-from mlagents_envs.base_env import DecisionSteps
 
 from mlagents.trainers.buffer import AgentBuffer
+from mlagents.trainers.trajectory import SplitObservations
 from mlagents.trainers.torch.components.bc.module import BCModule
 from mlagents.trainers.torch.components.reward_providers import create_reward_provider
 
 from mlagents.trainers.policy.torch_policy import TorchPolicy
 from mlagents.trainers.optimizer import Optimizer
 from mlagents.trainers.settings import TrainerSettings
-from mlagents.trainers.trajectory import SplitObservations
 from mlagents.trainers.torch.utils import ModelUtils
 
 
@@ -50,35 +49,6 @@ def create_reward_signals(self, reward_signal_configs):
                 reward_signal, self.policy.behavior_spec, settings
             )
 
-    def get_value_estimates(
-        self, decision_requests: DecisionSteps, idx: int, done: bool
-    ) -> Dict[str, float]:
-        """
-        Generates value estimates for bootstrapping.
-        :param decision_requests:
-        :param idx: Index in BrainInfo of agent.
-        :param done: Whether or not this is the last element of the episode,
-        in which case the value estimate will be 0.
-        :return: The value estimate dictionary with key being the name of the reward signal
-        and the value the corresponding value estimate.
-        """
-        vec_vis_obs = SplitObservations.from_observations(decision_requests.obs)
-
-        value_estimates = self.policy.actor_critic.critic_pass(
-            np.expand_dims(vec_vis_obs.vector_observations[idx], 0),
-            np.expand_dims(vec_vis_obs.visual_observations[idx], 0),
-        )
-
-        value_estimates = {k: float(v) for k, v in value_estimates.items()}
-
-        # If we're done, reassign all of the value estimates that need terminal states.
-        if done:
-            for k in value_estimates:
-                if not self.reward_signals[k].ignore_done:
-                    value_estimates[k] = 0.0
-
-        return value_estimates
-
     def get_trajectory_value_estimates(
         self, batch: AgentBuffer, next_obs: List[np.ndarray], done: bool
     ) -> Tuple[Dict[str, np.ndarray], Dict[str, float]]:
@@ -93,18 +63,23 @@ def get_trajectory_value_estimates(
         else:
             visual_obs = []
 
-        memory = torch.zeros([1, len(vector_obs[0]), self.policy.m_size])
+        memory = torch.zeros([1, 1, self.policy.m_size])
 
-        next_obs = np.concatenate(next_obs, axis=-1)
-        next_obs = [ModelUtils.list_to_tensor(next_obs).unsqueeze(0)]
-        next_memory = torch.zeros([1, 1, self.policy.m_size])
+        vec_vis_obs = SplitObservations.from_observations(next_obs)
+        next_vec_obs = [
+            ModelUtils.list_to_tensor(vec_vis_obs.vector_observations).unsqueeze(0)
+        ]
+        next_vis_obs = [
+            ModelUtils.list_to_tensor(_vis_ob).unsqueeze(0)
+            for _vis_ob in vec_vis_obs.visual_observations
+        ]
 
-        value_estimates = self.policy.actor_critic.critic_pass(
-            vector_obs, visual_obs, memory
+        value_estimates, next_memory = self.policy.actor_critic.critic_pass(
+            vector_obs, visual_obs, memory, sequence_length=batch.num_experiences
         )
 
-        next_value_estimate = self.policy.actor_critic.critic_pass(
-            next_obs, next_obs, next_memory
+        next_value_estimate, _ = self.policy.actor_critic.critic_pass(
+            next_vec_obs, next_vis_obs, next_memory, sequence_length=1
         )
 
         for name, estimate in value_estimates.items():

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(

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 +80,24 @@ 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))
+            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
 
-    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 +108,9 @@ 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))
+        policy_loss = -1 * ModelUtils.masked_mean(
+            torch.min(p_opt_a, p_opt_b).flatten(), loss_masks
+        )
         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.bool)
+        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 * ModelUtils.masked_mean(entropy.flatten(), 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)

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)
 
@@ -168,11 +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)
@@ -232,9 +258,8 @@ 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)
+                value_loss = 0.5 * ModelUtils.masked_mean(
+                    torch.nn.functional.mse_loss(values[name], v_backup), loss_masks
                 )
                 value_losses.append(value_loss)
         else:
@@ -253,9 +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))
@@ -275,7 +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(
@@ -322,9 +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
@@ -369,12 +393,28 @@ 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_like(next_memories)
+
         vis_obs: List[torch.Tensor] = []
         next_vis_obs: List[torch.Tensor] = []
         if self.policy.use_vis_obs:
@@ -415,19 +455,46 @@ 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)
-        masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.int32)
+            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.bool)
         use_discrete = not self.policy.use_continuous_act
         dones = ModelUtils.list_to_tensor(batch["done"])