Add gradient penalty

alf/algorithms/algorithm.py

@@ -2132,7 +2132,10 @@ def _hybrid_update(self, experience, batch_info, offline_experience,
         else:
             loss_info = offline_loss_info
 
-        params = self._backward_and_gradient_update(loss_info.loss * weight)
+        params, gns = self._backward_and_gradient_update(
+            loss_info.loss * weight)
+
+        loss_info = loss_info._replace(gns=gns)
 
         if self._RL_train:
             # for now, there is no need to do a hybrid after update

alf/algorithms/smodice_algorithm.py

@@ -46,7 +46,8 @@
 SmoCriticInfo = namedtuple("SmoCriticInfo",
                            ["values", "initial_v_values", "is_first"])
 
-SmoLossInfo = namedtuple("SmoLossInfo", ["actor"], default_value=())
+SmoLossInfo = namedtuple(
+    "SmoLossInfo", ["actor", "grad_penalty"], default_value=())
 
 
 @alf.configurable
@@ -77,7 +78,8 @@ def __init__(self,
                  value_optimizer=None,
                  discriminator_optimizer=None,
                  gamma: float = 0.99,
-                 f="chi",
+                 f: str = "chi",
+                 gradient_penalty_weight: float = 1,
                  env=None,
                  config: TrainerConfig = None,
                  checkpoint=None,
@@ -104,7 +106,8 @@ def __init__(self,
             value_optimizer (torch.optim.optimizer): The optimizer for value network.
             discriminator_optimizer (torch.optim.optimizer): The optimizer for discriminator.
             gamma (float): the discount factor.
-            f (str): the function form for f-divergence. Currently support 'chi' and 'kl'
+            f: the function form for f-divergence. Currently support 'chi' and 'kl'
+            gradient_penalty_weight: the weight for discriminator gradient penalty
             env (Environment): The environment to interact with. ``env`` is a
                 batched environment, which means that it runs multiple simulations
                 simultateously. ``env` only needs to be provided to the root
@@ -155,6 +158,7 @@ def __init__(self,
         self._actor_network = actor_network
         self._value_network = value_network
         self._discriminator_net = discriminator_net
+        self._gradient_penalty_weight = gradient_penalty_weight
 
         assert actor_optimizer is not None
         if actor_optimizer is not None and actor_network is not None:
@@ -236,18 +240,44 @@ def _discriminator_train_step(self, inputs: TimeStep, state, rollout_info,
         """
         observation = inputs.observation
         action = rollout_info.action
-        expert_logits, _ = self._discriminator_net((observation, action),
-                                                   state)
+
+        discriminator_inputs = (observation, action)
 
         if is_expert:
+            # turn on input gradient for gradient penalty in the case of expert data
+            for e in discriminator_inputs:
+                e.requires_grad = True
+
+        expert_logits, _ = self._discriminator_net(discriminator_inputs, state)
+
+        if is_expert:
+            grads = torch.autograd.grad(
+                outputs=expert_logits,
+                inputs=discriminator_inputs,
+                grad_outputs=torch.ones_like(expert_logits),
+                create_graph=True,
+                retain_graph=True,
+                only_inputs=True)
+
+            grad_pen = 0
+            for g in grads:
+                grad_pen += self._gradient_penalty_weight * (
+                    g.norm(2, dim=1) - 1).pow(2)
+
             label = torch.ones(expert_logits.size())
+            # turn on input gradient for gradient penalty in the case of expert data
+            for e in discriminator_inputs:
+                e.requires_grad = True
         else:
             label = torch.zeros(expert_logits.size())
+            grad_pen = ()
 
         expert_loss = F.binary_cross_entropy_with_logits(
             expert_logits, label, reduction='none')
 
-        return LossInfo(loss=expert_loss, extra=SmoLossInfo(actor=expert_loss))
+        return LossInfo(
+            loss=expert_loss if grad_pen == () else expert_loss + grad_pen,
+            extra=SmoLossInfo(actor=expert_loss, grad_penalty=grad_pen))
 
     def value_train_step(self, inputs: TimeStep, state, rollout_info):
         observation = inputs.observation
@@ -285,7 +315,7 @@ def train_step(self,
                 alf.summary.scalar("imitation_loss_online",
                                    actor_loss.loss.mean())
                 alf.summary.scalar("discriminator_loss_online",
-                                   expert_disc_loss.loss.mean())
+                                   expert_disc_loss.extra.actor.mean())
 
         # use predicted reward
         reward = self.predict_reward(inputs, rollout_info)
@@ -305,7 +335,6 @@ def train_step_offline(self,
                            state,
                            rollout_info,
                            pre_train=False):
-
         action_dist, new_state = self._predict_action(
             inputs.observation, state=state.actor)
 
@@ -324,7 +353,8 @@ def train_step_offline(self,
                                    actor_loss.loss.mean())
                 alf.summary.scalar("discriminator_loss_offline",
                                    expert_disc_loss.loss.mean())
-
+                alf.summary.scalar("grad_penalty",
+                                   expert_disc_loss.extra.grad_penalty.mean())
         # use predicted reward
         reward = self.predict_reward(inputs, rollout_info)
 

alf/examples/data_collection_carla_conf.py

@@ -27,7 +27,7 @@
 # This is an example config file for data collection in CARLA.
 
 # the desired replay buffer size for collection
-# 100 is just an example. Should set it to he actual desired size.
+# 100 is just an example. Should set it to the actual desired size.
 replay_buffer_length = 100
 
 # the desired environment for data collection

alf/examples/smodice_bipedal_walker_conf.py

@@ -30,7 +30,7 @@
 
 offline_buffer_length = None
 offline_buffer_dir = [
-    "./hybrid_rl/replay_buffer_data/pendulum_replay_buffer_from_sac_10k"
+    "/home/haichaozhang/data/DATA/sac_bipedal_baseline/train/algorithm/ckpt-80000-replay_buffer"
 ]
 
 alf.config('Agent', rl_algorithm_cls=SmodiceAlgorithm, optimizer=None)
@@ -67,4 +67,11 @@
     # add weight decay to the v_net following smodice paper
     value_optimizer=alf.optimizers.Adam(lr=lr, weight_decay=1e-4),
     discriminator_optimizer=alf.optimizers.Adam(lr=lr),
+    gradient_penalty_weight=0.1,
 )
+
+# training config
+alf.config(
+    "TrainerConfig",
+    offline_buffer_dir=offline_buffer_dir,
+    offline_buffer_length=offline_buffer_length)

alf/examples/smodice_pendulum_conf.py

@@ -76,6 +76,7 @@
     # add weight decay to the v_net following smodice paper
     value_optimizer=alf.optimizers.Adam(lr=lr, weight_decay=1e-4),
     discriminator_optimizer=alf.optimizers.Adam(lr=lr),
+    gradient_penalty_weight=0.1,
 )
 
 num_iterations = 1000000
@@ -91,6 +92,7 @@
     rl_train_after_update_steps=0,  # joint training
     mini_batch_size=256,
     mini_batch_length=2,
+    unroll_length=1,
     offline_buffer_dir=offline_buffer_dir,
     offline_buffer_length=offline_buffer_length,
     num_checkpoints=1,