[RLlib] Torch LR schedule not working. Fix and added test case. (#12396)

rllib/agents/ppo/tests/test_ppo.py

@@ -3,6 +3,7 @@
 import unittest
 
 import ray
+from ray.rllib.agents.callbacks import DefaultCallbacks
 import ray.rllib.agents.ppo as ppo
 from ray.rllib.agents.ppo.ppo_tf_policy import postprocess_ppo_gae as \
     postprocess_ppo_gae_tf, ppo_surrogate_loss as ppo_surrogate_loss_tf
@@ -36,6 +37,30 @@
 }
 
 
+class MyCallbacks(DefaultCallbacks):
+    @staticmethod
+    def _check_lr_torch(policy, policy_id):
+        for j, opt in enumerate(policy._optimizers):
+            for p in opt.param_groups:
+                assert p["lr"] == policy.cur_lr, "LR scheduling error!"
+
+    @staticmethod
+    def _check_lr_tf(policy, policy_id):
+        lr = policy.cur_lr
+        sess = policy.get_session()
+        if sess:
+            lr = sess.run(lr)
+            optim_lr = sess.run(policy._optimizer._lr)
+        else:
+            lr = lr.numpy()
+            optim_lr = policy._optimizer.lr.numpy()
+        assert lr == optim_lr, "LR scheduling error!"
+
+    def on_train_result(self, *, trainer, result: dict, **kwargs):
+        trainer.workers.foreach_policy(self._check_lr_torch if trainer.config[
+            "framework"] == "torch" else self._check_lr_tf)
+
+
 class TestPPO(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
@@ -45,9 +70,12 @@ def setUpClass(cls):
     def tearDownClass(cls):
         ray.shutdown()
 
-    def test_ppo_compilation(self):
+    def test_ppo_compilation_and_lr_schedule(self):
         """Test whether a PPOTrainer can be built with all frameworks."""
         config = copy.deepcopy(ppo.DEFAULT_CONFIG)
+        # for checking lr-schedule correctness
+        config["callbacks"] = MyCallbacks
+
         config["num_workers"] = 1
         config["num_sgd_iter"] = 2
         # Settings in case we use an LSTM.

rllib/policy/torch_policy.py

@@ -612,15 +612,11 @@ def __init__(self, lr, lr_schedule):
 
     @override(Policy)
     def on_global_var_update(self, global_vars):
-        super(LearningRateSchedule, self).on_global_var_update(global_vars)
+        super().on_global_var_update(global_vars)
         self.cur_lr = self.lr_schedule.value(global_vars["timestep"])
-
-    @override(TorchPolicy)
-    def optimizer(self):
         for opt in self._optimizers:
             for p in opt.param_groups:
                 p["lr"] = self.cur_lr
-        return self._optimizers
 
 
 @DeveloperAPI