[rllib] Add magic methods for rollouts (#2024)

python/ray/rllib/optimizers/sample_batch.py

@@ -36,8 +36,8 @@ class SampleBatch(object):
     @staticmethod
     def concat_samples(samples):
         out = {}
-        for k in samples[0].data.keys():
-            out[k] = np.concatenate([s.data[k] for s in samples])
+        for k in samples[0].keys():
+            out[k] = np.concatenate([s[k] for s in samples])
         return SampleBatch(out)
 
     def concat(self, other):
@@ -50,10 +50,10 @@ class SampleBatch(object):
             {"a": [1, 2, 3, 4, 5]}
         """
 
-        assert self.data.keys() == other.data.keys(), "must have same columns"
+        assert self.keys() == other.keys(), "must have same columns"
         out = {}
-        for k in self.data.keys():
-            out[k] = np.concatenate([self.data[k], other.data[k]])
+        for k in self.keys():
+            out[k] = np.concatenate([self[k], other[k]])
         return SampleBatch(out)
 
     def rows(self):
@@ -70,7 +70,7 @@ class SampleBatch(object):
 
         for i in range(self.count):
             row = {}
-            for k in self.data.keys():
+            for k in self.keys():
                 row[k] = self[k][i]
             yield row
 
@@ -85,19 +85,34 @@ class SampleBatch(object):
 
         out = []
         for k in keys:
-            out.append(self.data[k])
+            out.append(self[k])
         return out
 
     def shuffle(self):
         permutation = np.random.permutation(self.count)
-        for key, val in self.data.items():
-            self.data[key] = val[permutation]
+        for key, val in self.items():
+            self[key] = val[permutation]
 
     def __getitem__(self, key):
         return self.data[key]
 
+    def __setitem__(self, key, item):
+        self.data[key] = item
+
     def __str__(self):
         return "SampleBatch({})".format(str(self.data))
 
     def __repr__(self):
         return "SampleBatch({})".format(str(self.data))
+
+    def keys(self):
+        return self.data.keys()
+
+    def items(self):
+        return self.data.items()
+
+    def __iter__(self):
+        return self.data.__iter__()
+
+    def __contains__(self, x):
+        return x in self.data

python/ray/rllib/test/test_optimizers.py

@@ -12,7 +12,6 @@ from ray.rllib.optimizers import AsyncOptimizer, SampleBatch
 
 
 class AsyncOptimizerTest(unittest.TestCase):
-
     def tearDown(self):
         ray.worker.cleanup()
 
@@ -21,8 +20,9 @@ class AsyncOptimizerTest(unittest.TestCase):
         local = _MockEvaluator()
         remotes = ray.remote(_MockEvaluator)
         remote_evaluators = [remotes.remote() for i in range(5)]
-        test_optimizer = AsyncOptimizer(
-            {"grads_per_step": 10}, local, remote_evaluators)
+        test_optimizer = AsyncOptimizer({
+            "grads_per_step": 10
+        }, local, remote_evaluators)
         test_optimizer.step()
         self.assertTrue(all(local.get_weights() == 0))
 
@@ -33,11 +33,11 @@ class SampleBatchTest(unittest.TestCase):
         b2 = SampleBatch({"a": np.array([1]), "b": np.array([4])})
         b3 = SampleBatch({"a": np.array([1]), "b": np.array([5])})
         b12 = b1.concat(b2)
-        self.assertEqual(b12.data["a"].tolist(), [1, 2, 3, 1])
-        self.assertEqual(b12.data["b"].tolist(), [4, 5, 6, 4])
+        self.assertEqual(b12["a"].tolist(), [1, 2, 3, 1])
+        self.assertEqual(b12["b"].tolist(), [4, 5, 6, 4])
         b = SampleBatch.concat_samples([b1, b2, b3])
-        self.assertEqual(b.data["a"].tolist(), [1, 2, 3, 1, 1])
-        self.assertEqual(b.data["b"].tolist(), [4, 5, 6, 4, 5])
+        self.assertEqual(b["a"].tolist(), [1, 2, 3, 1, 1])
+        self.assertEqual(b["b"].tolist(), [4, 5, 6, 4, 5])
 
 
 if __name__ == '__main__':

python/ray/rllib/utils/process_rollout.py

@@ -26,22 +26,22 @@ def process_rollout(rollout, reward_filter, gamma, lambda_=1.0, use_gae=True):
             processed rewards."""
 
     traj = {}
-    trajsize = len(rollout.data["actions"])
-    for key in rollout.data:
-        traj[key] = np.stack(rollout.data[key])
+    trajsize = len(rollout["actions"])
+    for key in rollout:
+        traj[key] = np.stack(rollout[key])
 
     if use_gae:
-        assert "vf_preds" in rollout.data, "Values not found!"
-        vpred_t = np.stack(
-            rollout.data["vf_preds"] + [np.array(rollout.last_r)]).squeeze()
+        assert "vf_preds" in rollout, "Values not found!"
+        vpred_t = np.stack(rollout["vf_preds"] +
+                           [np.array(rollout.last_r)]).squeeze()
         delta_t = traj["rewards"] + gamma * vpred_t[1:] - vpred_t[:-1]
         # This formula for the advantage comes
         # "Generalized Advantage Estimation": https://arxiv.org/abs/1506.02438
         traj["advantages"] = discount(delta_t, gamma * lambda_)
         traj["value_targets"] = traj["advantages"] + traj["vf_preds"]
     else:
-        rewards_plus_v = np.stack(
-            rollout.data["rewards"] + [np.array(rollout.last_r)]).squeeze()
+        rewards_plus_v = np.stack(rollout["rewards"] +
+                                  [np.array(rollout.last_r)]).squeeze()
         traj["advantages"] = discount(rewards_plus_v, gamma)[:-1]
 
     for i in range(traj["advantages"].shape[0]):

python/ray/rllib/utils/sampler.py

@@ -56,9 +56,30 @@ class PartialRollout(object):
             terminal (bool): if rollout has terminated."""
         return self.data["dones"][-1]
 
+    def __getitem__(self, key):
+        return self.data[key]
 
-CompletedRollout = namedtuple(
-    "CompletedRollout", ["episode_length", "episode_reward"])
+    def __setitem__(self, key, item):
+        self.data[key] = item
+
+    def keys(self):
+        return self.data.keys()
+
+    def items(self):
+        return self.data.items()
+
+    def __iter__(self):
+        return self.data.__iter__()
+
+    def __next__(self):
+        return self.data.__next__()
+
+    def __contains__(self, x):
+        return x in self.data
+
+
+CompletedRollout = namedtuple("CompletedRollout",
+                              ["episode_length", "episode_reward"])
 
 
 class SyncSampler(object):
@@ -71,16 +92,15 @@ class SyncSampler(object):
     thread."""
     async = False
 
-    def __init__(self, env, policy, obs_filter,
-                 num_local_steps, horizon=None):
+    def __init__(self, env, policy, obs_filter, num_local_steps, horizon=None):
         self.num_local_steps = num_local_steps
         self.horizon = horizon
         self.env = env
         self.policy = policy
         self._obs_filter = obs_filter
-        self.rollout_provider = _env_runner(
-            self.env, self.policy, self.num_local_steps, self.horizon,
-            self._obs_filter)
+        self.rollout_provider = _env_runner(self.env, self.policy,
+                                            self.num_local_steps, self.horizon,
+                                            self._obs_filter)
         self.metrics_queue = queue.Queue()
 
     def get_data(self):
@@ -108,10 +128,10 @@ class AsyncSampler(threading.Thread):
     accumulate and the gradient can be calculated on up to 5 batches."""
     async = True
 
-    def __init__(self, env, policy, obs_filter,
-                 num_local_steps, horizon=None):
-        assert getattr(obs_filter, "is_concurrent", False), (
-            "Observation Filter must support concurrent updates.")
+    def __init__(self, env, policy, obs_filter, num_local_steps, horizon=None):
+        assert getattr(
+            obs_filter, "is_concurrent",
+            False), ("Observation Filter must support concurrent updates.")
         threading.Thread.__init__(self)
         self.queue = queue.Queue(5)
         self.metrics_queue = queue.Queue()
@@ -132,9 +152,9 @@ class AsyncSampler(threading.Thread):
             raise e
 
     def _run(self):
-        rollout_provider = _env_runner(
-            self.env, self.policy, self.num_local_steps,
-            self.horizon, self._obs_filter)
+        rollout_provider = _env_runner(self.env, self.policy,
+                                       self.num_local_steps, self.horizon,
+                                       self._obs_filter)
         while True:
             # The timeout variable exists because apparently, if one worker
             # dies, the other workers won't die with it, unless the timeout is
@@ -232,13 +252,14 @@ def _env_runner(env, policy, num_local_steps, horizon, obs_filter):
                 action = np.concatenate(action, axis=0).flatten()
 
             # Collect the experience.
-            rollout.add(obs=last_observation,
-                        actions=action,
-                        rewards=reward,
-                        dones=terminal,
-                        features=last_features,
-                        new_obs=observation,
-                        **pi_info)
+            rollout.add(
+                obs=last_observation,
+                actions=action,
+                rewards=reward,
+                dones=terminal,
+                features=last_features,
+                new_obs=observation,
+                **pi_info)
 
             last_observation = observation
             last_features = features
@@ -247,8 +268,8 @@ def _env_runner(env, policy, num_local_steps, horizon, obs_filter):
                 terminal_end = True
                 yield CompletedRollout(length, rewards)
 
-                if (length >= horizon or
-                        not env.metadata.get("semantics.autoreset")):
+                if (length >= horizon
+                        or not env.metadata.get("semantics.autoreset")):
                     last_observation = obs_filter(env.reset())
                     if hasattr(policy, "get_initial_features"):
                         last_features = policy.get_initial_features()