[rllib] Add multi-agent examples for hand-coded policy, centralized VF (#4554)

ci/jenkins_tests/run_rllib_tests.sh

@@ -380,6 +380,9 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     /ray/ci/suppress_output python /ray/python/ray/rllib/examples/custom_loss.py --iters=2
 
+docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
+    /ray/ci/suppress_output python /ray/python/ray/rllib/examples/policy_evaluator_custom_workflow.py
+
 docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
     /ray/ci/suppress_output python /ray/python/ray/rllib/examples/custom_metrics_and_callbacks.py --num-iters=2
 

doc/source/rllib-examples.rst

@@ -22,6 +22,8 @@ Training Workflows
    Example of how to adjust the configuration of an environment over time.
 - `Custom metrics <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/custom_metrics_and_callbacks.py>`__:
    Example of how to output custom training metrics to TensorBoard.
+- `Using policy evaluators directly for control over the whole training workflow <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/policy_evaluator_custom_workflow.py>`__:
+   Example of how to use RLlib's lower-level building blocks to implement a fully customized training workflow.
 
 Custom Envs and Models
 ----------------------
@@ -49,12 +51,16 @@ Multi-Agent and Hierarchical
 
 - `Two-step game <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/twostep_game.py>`__:
    Example of the two-step game from the `QMIX paper <https://arxiv.org/pdf/1803.11485.pdf>`__.
+- `Hand-coded policy <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/multiagent_custom_policy.py>`__:
+   Example of running a custom hand-coded policy alongside trainable policies.
 - `Weight sharing between policies <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/multiagent_cartpole.py>`__:
    Example of how to define weight-sharing layers between two different policies.
 - `Multiple trainers <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/multiagent_two_trainers.py>`__:
    Example of alternating training between two DQN and PPO trainers.
 - `Hierarchical training <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/hierarchical_training.py>`__:
    Example of hierarchical training using the multi-agent API.
+- `PPO with centralized value function <https://github.com/ray-project/ray/pull/3642/files>`__:
+   Example of customizing PPO to include a centralized value function, including a runnable script that demonstrates cooperative CartPole.
 
 Community Examples
 ------------------

doc/source/rllib-training.rst

@@ -178,6 +178,8 @@ Custom Training Workflows
 
 In the `basic training example <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/custom_env.py>`__, Tune will call ``train()`` on your trainer once per iteration and report the new training results. Sometimes, it is desirable to have full control over training, but still run inside Tune. Tune supports `custom trainable functions <tune-usage.html#training-api>`__ that can be used to implement `custom training workflows (example) <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/custom_train_fn.py>`__.
 
+For even finer-grained control over training, you can use RLlib's lower-level `building blocks <rllib-concepts.html>`__ directly to implement `fully customized training workflows <https://github.com/ray-project/ray/blob/master/python/ray/rllib/examples/policy_evaluator_custom_workflow.py>`__.
+
 Accessing Policy State
 ~~~~~~~~~~~~~~~~~~~~~~
 It is common to need to access a trainer's internal state, e.g., to set or get internal weights. In RLlib trainer state is replicated across multiple *policy evaluators* (Ray actors) in the cluster. However, you can easily get and update this state between calls to ``train()`` via ``trainer.optimizer.foreach_evaluator()`` or ``trainer.optimizer.foreach_evaluator_with_index()``. These functions take a lambda function that is applied with the evaluator as an arg. You can also return values from these functions and those will be returned as a list.

python/ray/rllib/evaluation/interface.py

@@ -49,7 +49,9 @@ class EvaluatorInterface(object):
             >>> ev.learn_on_batch(samples)
         """
 
-        return self.compute_apply(samples)
+        grads, info = self.compute_gradients(samples)
+        self.apply_gradients(grads)
+        return info
 
     @DeveloperAPI
     def compute_gradients(self, samples):
@@ -113,14 +115,6 @@ class EvaluatorInterface(object):
 
         raise NotImplementedError
 
-    @DeveloperAPI
-    def compute_apply(self, samples):
-        """Deprecated: override learn_on_batch instead."""
-
-        grads, info = self.compute_gradients(samples)
-        self.apply_gradients(grads)
-        return info
-
     @DeveloperAPI
     def get_host(self):
         """Returns the hostname of the process running this evaluator."""

python/ray/rllib/evaluation/metrics.py

@@ -41,7 +41,8 @@ def get_learner_stats(grad_info):
 
 
 @DeveloperAPI
-def collect_metrics(local_evaluator, remote_evaluators=[],
+def collect_metrics(local_evaluator=None,
+                    remote_evaluators=[],
                     timeout_seconds=180):
     """Gathers episode metrics from PolicyEvaluator instances."""
 
@@ -52,7 +53,7 @@ def collect_metrics(local_evaluator, remote_evaluators=[],
 
 
 @DeveloperAPI
-def collect_episodes(local_evaluator,
+def collect_episodes(local_evaluator=None,
                      remote_evaluators=[],
                      timeout_seconds=180):
     """Gathers new episodes metrics tuples from the given evaluators."""
@@ -69,7 +70,8 @@ def collect_episodes(local_evaluator,
             "this timeout with `collect_metrics_timeout`.")
 
     metric_lists = ray.get(collected)
-    metric_lists.append(local_evaluator.get_metrics())
+    if local_evaluator:
+        metric_lists.append(local_evaluator.get_metrics())
     episodes = []
     for metrics in metric_lists:
         episodes.extend(metrics)

python/ray/rllib/evaluation/policy_evaluator.py

@@ -564,21 +564,21 @@ class PolicyEvaluator(EvaluatorInterface):
                     summarize(samples)))
         if isinstance(samples, MultiAgentBatch):
             info_out = {}
+            to_fetch = {}
             if self.tf_sess is not None:
                 builder = TFRunBuilder(self.tf_sess, "learn_on_batch")
-                for pid, batch in samples.policy_batches.items():
-                    if pid not in self.policies_to_train:
-                        continue
-                    info_out[pid], _ = (
-                        self.policy_map[pid]._build_learn_on_batch(
-                            builder, batch))
-                info_out = {k: builder.get(v) for k, v in info_out.items()}
             else:
-                for pid, batch in samples.policy_batches.items():
+                builder = None
-                    if pid not in self.policies_to_train:
+            for pid, batch in samples.policy_batches.items():
-                        continue
+                if pid not in self.policies_to_train:
-                    info_out[pid], _ = (
+                    continue
-                        self.policy_map[pid].learn_on_batch(batch))
+                policy = self.policy_map[pid]
+                if builder and hasattr(policy, "_build_learn_on_batch"):
+                    to_fetch[pid], _ = policy._build_learn_on_batch(
+                        builder, batch)
+                else:
+                    info_out[pid], _ = policy.learn_on_batch(batch)
+            info_out.update({k: builder.get(v) for k, v in to_fetch.items()})
         else:
             info_out, _ = (
                 self.policy_map[DEFAULT_POLICY_ID].learn_on_batch(samples))

python/ray/rllib/evaluation/policy_graph.py

@@ -170,7 +170,9 @@ class PolicyGraph(object):
             >>> ev.learn_on_batch(samples)
         """
 
-        return self.compute_apply(samples)
+        grads, grad_info = self.compute_gradients(samples)
+        apply_info = self.apply_gradients(grads)
+        return grad_info, apply_info
 
     @DeveloperAPI
     def compute_gradients(self, postprocessed_batch):
@@ -195,14 +197,6 @@ class PolicyGraph(object):
         """
         raise NotImplementedError
 
-    @DeveloperAPI
-    def compute_apply(self, samples):
-        """Deprecated: override learn_on_batch instead."""
-
-        grads, grad_info = self.compute_gradients(samples)
-        apply_info = self.apply_gradients(grads)
-        return grad_info, apply_info
-
     @DeveloperAPI
     def get_weights(self):
         """Returns model weights.

python/ray/rllib/examples/multiagent_custom_policy.py

@@ -0,0 +1,76 @@
+"""Example of running a custom hand-coded policy alongside trainable policies.
+
+This example has two policies:
+    (1) a simple PG policy
+    (2) a hand-coded policy that acts at random in the env (doesn't learn)
+
+In the console output, you can see the PG policy does much better than random:
+Result for PG_multi_cartpole_0:
+  ...
+  policy_reward_mean:
+    pg_policy: 185.23
+    random: 21.255
+  ...
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import gym
+
+import ray
+from ray import tune
+from ray.rllib.evaluation import PolicyGraph
+from ray.rllib.tests.test_multi_agent_env import MultiCartpole
+from ray.tune.registry import register_env
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--num-iters", type=int, default=20)
+
+
+class RandomPolicy(PolicyGraph):
+    """Hand-coded policy that returns random actions."""
+
+    def compute_actions(self,
+                        obs_batch,
+                        state_batches,
+                        prev_action_batch=None,
+                        prev_reward_batch=None,
+                        info_batch=None,
+                        episodes=None,
+                        **kwargs):
+        """Compute actions on a batch of observations."""
+        return [self.action_space.sample() for _ in obs_batch], [], {}
+
+    def learn_on_batch(self, samples):
+        """No learning."""
+        return {}, {}
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    ray.init()
+
+    # Simple environment with 4 independent cartpole entities
+    register_env("multi_cartpole", lambda _: MultiCartpole(4))
+    single_env = gym.make("CartPole-v0")
+    obs_space = single_env.observation_space
+    act_space = single_env.action_space
+
+    tune.run(
+        "PG",
+        stop={"training_iteration": args.num_iters},
+        config={
+            "env": "multi_cartpole",
+            "multiagent": {
+                "policy_graphs": {
+                    "pg_policy": (None, obs_space, act_space, {}),
+                    "random": (RandomPolicy, obs_space, act_space, {}),
+                },
+                "policy_mapping_fn": tune.function(
+                    lambda agent_id: ["pg_policy", "random"][agent_id % 2]),
+            },
+        },
+    )

python/ray/rllib/examples/policy_evaluator_custom_workflow.py

@@ -0,0 +1,117 @@
+"""Example of using policy evaluator classes directly to implement training.
+
+Instead of using the built-in Trainer classes provided by RLlib, here we define
+a custom PolicyGraph class and manually coordinate distributed sample
+collection and policy optimization.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import gym
+
+import ray
+from ray import tune
+from ray.rllib.evaluation import PolicyGraph, PolicyEvaluator, SampleBatch
+from ray.rllib.evaluation.metrics import collect_metrics
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--gpu", action="store_true")
+parser.add_argument("--num-iters", type=int, default=20)
+parser.add_argument("--num-workers", type=int, default=2)
+
+
+class CustomPolicy(PolicyGraph):
+    """Example of a custom policy graph written from scratch.
+
+    You might find it more convenient to extend TF/TorchPolicyGraph instead
+    for a real policy.
+    """
+
+    def __init__(self, observation_space, action_space, config):
+        PolicyGraph.__init__(self, observation_space, action_space, config)
+        # example parameter
+        self.w = 1.0
+
+    def compute_actions(self,
+                        obs_batch,
+                        state_batches,
+                        prev_action_batch=None,
+                        prev_reward_batch=None,
+                        info_batch=None,
+                        episodes=None,
+                        **kwargs):
+        # return random actions
+        return [self.action_space.sample() for _ in obs_batch], [], {}
+
+    def learn_on_batch(self, samples):
+        # implement your learning code here
+        return {}, {}
+
+    def update_some_value(self, w):
+        # can also call other methods on policies
+        self.w = w
+
+    def get_weights(self):
+        return {"w": self.w}
+
+    def set_weights(self, weights):
+        self.w = weights["w"]
+
+
+def training_workflow(config, reporter):
+    # Setup policy and policy evaluation actors
+    env = gym.make("CartPole-v0")
+    policy = CustomPolicy(env.observation_space, env.action_space, {})
+    workers = [
+        PolicyEvaluator.as_remote().remote(lambda c: gym.make("CartPole-v0"),
+                                           CustomPolicy)
+        for _ in range(config["num_workers"])
+    ]
+
+    for _ in range(config["num_iters"]):
+        # Broadcast weights to the policy evaluation workers
+        weights = ray.put({"default_policy": policy.get_weights()})
+        for w in workers:
+            w.set_weights.remote(weights)
+
+        # Gather a batch of samples
+        T1 = SampleBatch.concat_samples(
+            ray.get([w.sample.remote() for w in workers]))
+
+        # Update the remote policy replicas and gather another batch of samples
+        new_value = policy.w * 2.0
+        for w in workers:
+            w.for_policy.remote(lambda p: p.update_some_value(new_value))
+
+        # Gather another batch of samples
+        T2 = SampleBatch.concat_samples(
+            ray.get([w.sample.remote() for w in workers]))
+
+        # Improve the policy using the T1 batch
+        policy.learn_on_batch(T1)
+
+        # Do some arbitrary updates based on the T2 batch
+        policy.update_some_value(sum(T2["rewards"]))
+
+        reporter(**collect_metrics(remote_evaluators=workers))
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    ray.init()
+
+    tune.run(
+        training_workflow,
+        resources_per_trial={
+            "gpu": 1 if args.gpu else 0,
+            "cpu": 1,
+            "extra_cpu": args.num_workers,
+        },
+        config={
+            "num_workers": args.num_workers,
+            "num_iters": args.num_iters,
+        },
+    )