chore: import upstream snapshot with attribution

rllib/core/testing/bc_algorithm.py

@@ -0,0 +1,40 @@
+"""Contains example implementation of a custom algorithm.
+
+Note: It doesn't include any real use-case functionality; it only serves as an example
+to test the algorithm construction and customization.
+"""
+
+from ray.rllib.algorithms import Algorithm, AlgorithmConfig
+from ray.rllib.core.rl_module.rl_module import RLModuleSpec
+from ray.rllib.core.testing.torch.bc_learner import BCTorchLearner
+from ray.rllib.core.testing.torch.bc_module import DiscreteBCTorchModule
+from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
+from ray.rllib.utils.annotations import override
+from ray.rllib.utils.typing import ResultDict
+
+
+class BCConfigTest(AlgorithmConfig):
+    def __init__(self, algo_class=None):
+        super().__init__(algo_class=algo_class or BCAlgorithmTest)
+
+    def get_default_rl_module_spec(self):
+        if self.framework_str == "torch":
+            return RLModuleSpec(module_class=DiscreteBCTorchModule)
+
+    def get_default_learner_class(self):
+        if self.framework_str == "torch":
+            return BCTorchLearner
+
+
+class BCAlgorithmTest(Algorithm):
+    @classmethod
+    def get_default_policy_class(cls, config: AlgorithmConfig):
+        if config.framework_str == "torch":
+            return TorchPolicyV2
+        else:
+            raise ValueError("Unknown framework: {}".format(config.framework_str))
+
+    @override(Algorithm)
+    def training_step(self) -> ResultDict:
+        # do nothing.
+        return {}