diff --git a/doc/source/rllib-algorithms.rst b/doc/source/rllib-algorithms.rst
index 6ee5aef50..d7042636e 100644
--- a/doc/source/rllib-algorithms.rst
+++ b/doc/source/rllib-algorithms.rst
@@ -197,7 +197,7 @@ Proximal Policy Optimization (PPO)
 `[paper] <https://arxiv.org/abs/1707.06347>`__ `[implementation] <https://github.com/ray-project/ray/blob/master/python/ray/rllib/agents/ppo/ppo.py>`__
 PPO's clipped objective supports multiple SGD passes over the same batch of experiences. RLlib's multi-GPU optimizer pins that data in GPU memory to avoid unnecessary transfers from host memory, substantially improving performance over a naive implementation. RLlib's PPO scales out using multiple workers for experience collection, and also with multiple GPUs for SGD.
 
-Tuned examples: `Humanoid-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/humanoid-ppo-gae.yaml>`__, `Hopper-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/hopper-ppo.yaml>`__, `Pendulum-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pendulum-ppo.yaml>`__, `PongDeterministic-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-ppo.yaml>`__, `Walker2d-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/walker2d-ppo.yaml>`__, `{BeamRider,Breakout,Qbert,SpaceInvaders}NoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-ppo.yaml>`__
+Tuned examples: `Humanoid-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/humanoid-ppo-gae.yaml>`__, `Hopper-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/hopper-ppo.yaml>`__, `Pendulum-v0 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pendulum-ppo.yaml>`__, `PongDeterministic-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/pong-ppo.yaml>`__, `Walker2d-v1 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/walker2d-ppo.yaml>`__, `HalfCheetah-v2 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/halfcheetah-ppo.yaml>`__, `{BeamRider,Breakout,Qbert,SpaceInvaders}NoFrameskip-v4 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/tuned_examples/atari-ppo.yaml>`__
 
 
 **Atari results**: `more details <https://github.com/ray-project/rl-experiments>`__
@@ -212,7 +212,13 @@ SpaceInvaders  671             944             ~800
 =============  ==============  ==============  ==================
 
 
-**Scalability:**
+**Scalability:** `more details <https://github.com/ray-project/rl-experiments>`__
+
+=============  =========================  =============================
+MuJoCo env     RLlib PPO 16-workers @ 1h  Fan et al PPO 16-workers @ 1h
+=============  =========================  =============================
+HalfCheetah    9664                       ~7700 
+=============  =========================  =============================
 
 .. figure:: ppo.png
    :width: 500px
diff --git a/python/ray/rllib/agents/ppo/ppo.py b/python/ray/rllib/agents/ppo/ppo.py
index 3e892e9de..965dea53c 100644
--- a/python/ray/rllib/agents/ppo/ppo.py
+++ b/python/ray/rllib/agents/ppo/ppo.py
@@ -44,6 +44,8 @@ DEFAULT_CONFIG = with_common_config({
     # Clip param for the value function. Note that this is sensitive to the
     # scale of the rewards. If your expected V is large, increase this.
     "vf_clip_param": 10.0,
+    # If specified, clip the global norm of gradients by this amount
+    "grad_clip": None,
     # Target value for KL divergence
     "kl_target": 0.01,
     # Whether to rollout "complete_episodes" or "truncate_episodes"
diff --git a/python/ray/rllib/agents/ppo/ppo_policy_graph.py b/python/ray/rllib/agents/ppo/ppo_policy_graph.py
index 80ec01ea5..d3ba03555 100644
--- a/python/ray/rllib/agents/ppo/ppo_policy_graph.py
+++ b/python/ray/rllib/agents/ppo/ppo_policy_graph.py
@@ -289,8 +289,17 @@ class PPOPolicyGraph(LearningRateSchedule, TFPolicyGraph):
 
     @override(TFPolicyGraph)
     def gradients(self, optimizer):
-        return optimizer.compute_gradients(
-            self._loss, colocate_gradients_with_ops=True)
+        if self.config["grad_clip"] is not None:
+            self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
+                                              tf.get_variable_scope().name)
+            grads = tf.gradients(self._loss, self.var_list)
+            self.grads, _ = tf.clip_by_global_norm(grads,
+                                                   self.config["grad_clip"])
+            clipped_grads = list(zip(self.grads, self.var_list))
+            return clipped_grads
+        else:
+            return optimizer.compute_gradients(
+                self._loss, colocate_gradients_with_ops=True)
 
     @override(PolicyGraph)
     def get_initial_state(self):
diff --git a/python/ray/rllib/tuned_examples/halfcheetah-ppo.yaml b/python/ray/rllib/tuned_examples/halfcheetah-ppo.yaml
new file mode 100644
index 000000000..d154e7c29
--- /dev/null
+++ b/python/ray/rllib/tuned_examples/halfcheetah-ppo.yaml
@@ -0,0 +1,22 @@
+halfcheetah-ppo:
+    env: HalfCheetah-v2
+    run: PPO
+    stop:
+        episode_reward_mean: 9800
+        time_total_s: 10800           
+    config:
+        gamma: 0.99
+        lambda: 0.95
+        kl_coeff: 1.0
+        num_sgd_iter: 32
+        lr: .0003
+        vf_loss_coeff: 0.5
+        clip_param: 0.2
+        sgd_minibatch_size: 4096
+        train_batch_size: 65536
+        num_workers: 16
+        num_gpus: 1
+        grad_clip: 0.5
+        num_envs_per_worker: 
+            grid_search: [16, 32]
+        batch_mode: truncate_episodes
diff --git a/python/ray/rllib/tuned_examples/pong-appo.yaml b/python/ray/rllib/tuned_examples/pong-appo.yaml
index 94e48e44d..cfcf19d1e 100644
--- a/python/ray/rllib/tuned_examples/pong-appo.yaml
+++ b/python/ray/rllib/tuned_examples/pong-appo.yaml
@@ -7,7 +7,7 @@ pong-appo:
     config:
         sample_batch_size: 50
         train_batch_size: 750
-        num_workers: 47
+        num_workers: 32
         broadcast_interval: 1
         max_sample_requests_in_flight_per_worker: 1
         num_data_loader_buffers: 1