[rllib] more user-friendly Optimizer signature + compute_apply (ray-p…

…roject#2335)

* Move signature of optimizers

* fix

* expose compute_apply for policy_graphs

* dictionaries and such

* test for multiagent

python/ray/rllib/agents/a3c/a3c.py

@@ -94,8 +94,8 @@ def _init(self):
             self.env_creator, policy_cls, self.config["num_workers"],
             {"num_gpus": 1 if self.config["use_gpu_for_workers"] else 0})
         self.optimizer = AsyncGradientsOptimizer(
-            self.config["optimizer"], self.local_evaluator,
-            self.remote_evaluators)
+            self.local_evaluator, self.remote_evaluators,
+            self.config["optimizer"])
 
     def _train(self):
         self.optimizer.step()

python/ray/rllib/agents/bc/bc.py

@@ -72,8 +72,8 @@ def _init(self):
             remote_cls.remote(self.env_creator, self.config, self.logdir)
             for _ in range(self.config["num_workers"])]
         self.optimizer = AsyncGradientsOptimizer(
-            self.config["optimizer"], self.local_evaluator,
-            self.remote_evaluators)
+            self.local_evaluator, self.remote_evaluators,
+            self.config["optimizer"])
 
     def _train(self):
         self.optimizer.step()

python/ray/rllib/agents/dqn/dqn.py

@@ -136,8 +136,8 @@ def _init(self):
             {"num_cpus": self.config["num_cpus_per_worker"],
              "num_gpus": self.config["num_gpus_per_worker"]})
         self.optimizer = getattr(optimizers, self.config["optimizer_class"])(
-            self.config["optimizer"], self.local_evaluator,
-            self.remote_evaluators)
+            self.local_evaluator, self.remote_evaluators,
+            self.config["optimizer"])
 
         self.last_target_update_ts = 0
         self.num_target_updates = 0

python/ray/rllib/agents/pg/pg.py

@@ -45,8 +45,8 @@ def _init(self):
         self.remote_evaluators = self.make_remote_evaluators(
             self.env_creator, PGPolicyGraph, self.config["num_workers"], {})
         self.optimizer = SyncSamplesOptimizer(
-            self.config["optimizer"], self.local_evaluator,
-            self.remote_evaluators)
+            self.local_evaluator, self.remote_evaluators,
+            self.config["optimizer"])
 
     def _train(self):
         self.optimizer.step()

python/ray/rllib/agents/ppo/ppo.py

@@ -74,11 +74,11 @@ def _init(self):
             {"num_cpus": self.config["num_cpus_per_worker"],
              "num_gpus": self.config["num_gpus_per_worker"]})
         self.optimizer = LocalMultiGPUOptimizer(
+            self.local_evaluator, self.remote_evaluators,
             {"sgd_batch_size": self.config["sgd_batchsize"],
              "sgd_stepsize": self.config["sgd_stepsize"],
              "num_sgd_iter": self.config["num_sgd_iter"],
-             "timesteps_per_batch": self.config["timesteps_per_batch"]},
-            self.local_evaluator, self.remote_evaluators)
+             "timesteps_per_batch": self.config["timesteps_per_batch"]})
 
     def _train(self):
         def postprocess_samples(batch):

python/ray/rllib/evaluation/policy_graph.py

@@ -106,6 +106,22 @@ def apply_gradients(self, gradients):
         """
         raise NotImplementedError
 
+    def compute_apply(self, samples):
+        """Fused compute gradients and apply gradients call.
+
+        Returns:
+            grad_info: dictionary of extra metadata from compute_gradients().
+            apply_info: dictionary of extra metadata from apply_gradients().
+
+        Examples:
+            >>> batch = ev.sample()
+            >>> ev.compute_apply(samples)
+        """
+
+        grads, grad_info = self.compute_gradients(samples)
+        apply_info = self.apply_gradients(grads)
+        return grad_info, apply_info
+
     def get_weights(self):
         """Returns model weights.
 

python/ray/rllib/optimizers/policy_optimizer.py

@@ -31,7 +31,7 @@ class PolicyOptimizer(object):
             evaluators created by this optimizer.
     """
 
-    def __init__(self, config, local_evaluator, remote_evaluators):
+    def __init__(self, local_evaluator, remote_evaluators=None, config=None):
         """Create an optimizer instance.
 
         Args:
@@ -41,10 +41,10 @@ def __init__(self, config, local_evaluator, remote_evaluators):
                 evaluators instances. If empty, the optimizer should fall back
                 to using only the local evaluator.
         """
-        self.config = config
         self.local_evaluator = local_evaluator
-        self.remote_evaluators = remote_evaluators
-        self._init(**config)
+        self.remote_evaluators = remote_evaluators or []
+        self.config = config or {}
+        self._init(**self.config)
 
         # Counters that should be updated by sub-classes
         self.num_steps_trained = 0

python/ray/rllib/optimizers/sync_samples_optimizer.py

@@ -40,8 +40,7 @@ def step(self):
                 samples = self.local_evaluator.sample()
 
         with self.grad_timer:
-            grad, _ = self.local_evaluator.compute_gradients(samples)
-            self.local_evaluator.apply_gradients(grad)
+            self.local_evaluator.compute_apply(samples)
             self.grad_timer.push_units_processed(samples.count)
 
         self.num_steps_sampled += samples.count

python/ray/rllib/test/test_multi_agent_env.py

@@ -296,7 +296,7 @@ def _testWithOptimizer(self, optimizer_cls):
                 batch_steps=50)]
         else:
             remote_evs = []
-        optimizer = optimizer_cls({}, ev, remote_evs)
+        optimizer = optimizer_cls(ev, remote_evs, {})
         for i in range(200):
             ev.foreach_policy(
                 lambda p, _: p.set_epsilon(max(0.02, 1 - i * .02))
@@ -338,7 +338,7 @@ def testTrainMultiCartpoleManyPolicies(self):
             policy_graph=policies,
             policy_mapping_fn=lambda agent_id: random.choice(policy_ids),
             batch_steps=100)
-        optimizer = SyncSamplesOptimizer({}, ev, [])
+        optimizer = SyncSamplesOptimizer(ev, [], {})
         for i in range(100):
             optimizer.step()
             result = collect_metrics(ev)

python/ray/rllib/test/test_optimizers.py

@@ -21,9 +21,8 @@ def testBasic(self):
         local = _MockEvaluator()
         remotes = ray.remote(_MockEvaluator)
         remote_evaluators = [remotes.remote() for i in range(5)]
-        test_optimizer = AsyncGradientsOptimizer({
-            "grads_per_step": 10
-        }, local, remote_evaluators)
+        test_optimizer = AsyncGradientsOptimizer(
+            local, remote_evaluators, {"grads_per_step": 10})
         test_optimizer.step()
         self.assertTrue(all(local.get_weights() == 0))