[tune] Support lambda functions in hyperparameters / tune rllib multiagent support (#2568)

* update

* func

* Update registry.py

* revert

python/ray/rllib/examples/multiagent_cartpole.py

@@ -17,10 +17,10 @@ import gym
 import random
 
 import ray
-from ray.rllib.agents.pg.pg import PGAgent
+from ray import tune
 from ray.rllib.agents.pg.pg_policy_graph import PGPolicyGraph
 from ray.rllib.test.test_multi_agent_env import MultiCartpole
-from ray.tune.logger import pretty_print
+from ray.tune import run_experiments
 from ray.tune.registry import register_env
 
 parser = argparse.ArgumentParser()
@@ -53,16 +53,19 @@ if __name__ == "__main__":
     }
     policy_ids = list(policy_graphs.keys())
 
-    agent = PGAgent(
-        env="multi_cartpole",
-        config={
-            "multiagent": {
-                "policy_graphs": policy_graphs,
-                "policy_mapping_fn": (
-                    lambda agent_id: random.choice(policy_ids)),
+    run_experiments({
+        "test": {
+            "run": "PG",
+            "env": "multi_cartpole",
+            "stop": {
+                "training_iteration": args.num_iters
             },
-        })
-
-    for i in range(args.num_iters):
-        print("== Iteration", i, "==")
-        print(pretty_print(agent.train()))
+            "config": {
+                "multiagent": {
+                    "policy_graphs": policy_graphs,
+                    "policy_mapping_fn": tune.function(
+                        lambda agent_id: random.choice(policy_ids)),
+                },
+            },
+        }
+    })

python/ray/tune/__init__.py

@@ -7,9 +7,9 @@ from ray.tune.tune import run_experiments
 from ray.tune.experiment import Experiment
 from ray.tune.registry import register_env, register_trainable
 from ray.tune.trainable import Trainable
-from ray.tune.suggest import grid_search
+from ray.tune.suggest import grid_search, function
 
 __all__ = [
     "Trainable", "TuneError", "grid_search", "register_env",
-    "register_trainable", "run_experiments", "Experiment"
+    "register_trainable", "run_experiments", "Experiment", "function"
 ]

python/ray/tune/suggest/__init__.py

@@ -2,9 +2,9 @@ from ray.tune.suggest.search import SearchAlgorithm
 from ray.tune.suggest.basic_variant import BasicVariantGenerator
 from ray.tune.suggest.suggestion import SuggestionAlgorithm
 from ray.tune.suggest.hyperopt import HyperOptSearch
-from ray.tune.suggest.variant_generator import grid_search
+from ray.tune.suggest.variant_generator import grid_search, function
 
 __all__ = [
     "SearchAlgorithm", "BasicVariantGenerator", "HyperOptSearch",
-    "SuggestionAlgorithm", "grid_search"
+    "SuggestionAlgorithm", "grid_search", "function"
 ]

python/ray/tune/suggest/variant_generator.py

@@ -51,6 +51,16 @@ def grid_search(values):
     return {"grid_search": values}
 
 
+class function(object):
+    """Wraps `func` to make sure it is not expanded during resolution."""
+
+    def __init__(self, func):
+        self.func = func
+
+    def __call__(self, *args, **kwargs):
+        return self.func(*args, **kwargs)
+
+
 _STANDARD_IMPORTS = {
     "random": random,
     "np": numpy,