[RLlib] Issue 12233 shared tf layers example not really shared (only works for tf1.x, not tf2.x). (#12399)

rllib/BUILD

@@ -1899,7 +1899,7 @@ py_test(
     tags = ["examples", "examples_M"],
     size = "medium",
     srcs = ["examples/multi_agent_cartpole.py"],
-    args = ["--as-test", "--torch", "--stop-reward=70.0", "--num-cpus=4"]
+    args = ["--as-test", "--framework=torch", "--stop-reward=70.0", "--num-cpus=4"]
 )
 
 py_test(

rllib/examples/models/shared_weights_model.py

@@ -10,10 +10,53 @@ from ray.rllib.utils.framework import try_import_tf, try_import_torch
 tf1, tf, tfv = try_import_tf()
 torch, nn = try_import_torch()
 
+TF2_GLOBAL_SHARED_LAYER = None
+if tf:
+    # The global, shared layer to be used by both models.
+    TF2_GLOBAL_SHARED_LAYER = tf.keras.layers.Dense(
+        units=64, activation=tf.nn.relu, name="fc1")
+
+
+class TF2SharedWeightsModel(TFModelV2):
+    """Example of weight sharing between two different TFModelV2s.
+
+    NOTE: This will only work for tf2.x. When running with config.framework=tf,
+    use SharedWeightsModel1 and SharedWeightsModel2 below, instead!
+
+    The shared (single) layer is simply defined outside of the two Models,
+    then used by both Models in their forward pass.
+    """
+
+    def __init__(self, observation_space, action_space, num_outputs,
+                 model_config, name):
+        super().__init__(observation_space, action_space, num_outputs,
+                         model_config, name)
+
+        inputs = tf.keras.layers.Input(observation_space.shape)
+        last_layer = TF2_GLOBAL_SHARED_LAYER(inputs)
+        output = tf.keras.layers.Dense(
+            units=num_outputs, activation=None, name="fc_out")(last_layer)
+        vf = tf.keras.layers.Dense(
+            units=1, activation=None, name="value_out")(last_layer)
+        self.base_model = tf.keras.models.Model(inputs, [output, vf])
+        self.register_variables(self.base_model.variables)
+
+    @override(ModelV2)
+    def forward(self, input_dict, state, seq_lens):
+        out, self._value_out = self.base_model(input_dict["obs"])
+        return out, []
+
+    @override(ModelV2)
+    def value_function(self):
+        return tf.reshape(self._value_out, [-1])
+
 
 class SharedWeightsModel1(TFModelV2):
     """Example of weight sharing between two different TFModelV2s.
 
+    NOTE: This will only work for tf1 (static graph). When running with
+    config.framework=tf2, use TF2SharedWeightsModel, instead!
+
     Here, we share the variables defined in the 'shared' variable scope
     by entering it explicitly with tf1.AUTO_REUSE. This creates the
     variables for the 'fc1' layer in a global scope called 'shared'
@@ -85,6 +128,7 @@ class SharedWeightsModel2(TFModelV2):
 
 TORCH_GLOBAL_SHARED_LAYER = None
 if torch:
+    # The global, shared layer to be used by both models.
     TORCH_GLOBAL_SHARED_LAYER = SlimFC(
         64,
         64,

rllib/examples/multi_agent_cartpole.py

@@ -18,7 +18,8 @@ import ray
 from ray import tune
 from ray.rllib.examples.env.multi_agent import MultiAgentCartPole
 from ray.rllib.examples.models.shared_weights_model import \
-    SharedWeightsModel1, SharedWeightsModel2, TorchSharedWeightsModel
+    SharedWeightsModel1, SharedWeightsModel2, TF2SharedWeightsModel, \
+    TorchSharedWeightsModel
 from ray.rllib.models import ModelCatalog
 from ray.rllib.utils.framework import try_import_tf
 from ray.rllib.utils.test_utils import check_learning_achieved
@@ -35,7 +36,8 @@ parser.add_argument("--stop-timesteps", type=int, default=100000)
 parser.add_argument("--simple", action="store_true")
 parser.add_argument("--num-cpus", type=int, default=0)
 parser.add_argument("--as-test", action="store_true")
-parser.add_argument("--torch", action="store_true")
+parser.add_argument(
+    "--framework", choices=["tf2", "tf", "tfe", "torch"], default="tf")
 
 if __name__ == "__main__":
     args = parser.parse_args()
@@ -43,8 +45,13 @@ if __name__ == "__main__":
     ray.init(num_cpus=args.num_cpus or None)
 
     # Register the models to use.
-    mod1 = TorchSharedWeightsModel if args.torch else SharedWeightsModel1
-    mod2 = TorchSharedWeightsModel if args.torch else SharedWeightsModel2
+    if args.framework == "torch":
+        mod1 = mod2 = TorchSharedWeightsModel
+    elif args.framework in ["tfe", "tf2"]:
+        mod1 = mod2 = TF2SharedWeightsModel
+    else:
+        mod1 = SharedWeightsModel1
+        mod2 = SharedWeightsModel2
     ModelCatalog.register_custom_model("model1", mod1)
     ModelCatalog.register_custom_model("model2", mod2)
 
@@ -83,7 +90,7 @@ if __name__ == "__main__":
             "policies": policies,
             "policy_mapping_fn": (lambda agent_id: random.choice(policy_ids)),
         },
-        "framework": "torch" if args.torch else "tf",
+        "framework": args.framework,
     }
     stop = {
         "episode_reward_mean": args.stop_reward,