diff --git a/rllib/agents/marwil/marwil.py b/rllib/agents/marwil/marwil.py
index c4f88fdb8..d123b3ef5 100644
--- a/rllib/agents/marwil/marwil.py
+++ b/rllib/agents/marwil/marwil.py
@@ -21,6 +21,8 @@ DEFAULT_CONFIG = with_common_config({
     "beta": 1.0,
     # Balancing value estimation loss and policy optimization loss.
     "vf_coeff": 1.0,
+    # If specified, clip the global norm of gradients by this amount.
+    "grad_clip": None,
     # Whether to calculate cumulative rewards.
     "postprocess_inputs": True,
     # Whether to rollout "complete_episodes" or "truncate_episodes".
diff --git a/rllib/agents/marwil/marwil_tf_policy.py b/rllib/agents/marwil/marwil_tf_policy.py
index 44352be4f..211f9467e 100644
--- a/rllib/agents/marwil/marwil_tf_policy.py
+++ b/rllib/agents/marwil/marwil_tf_policy.py
@@ -1,6 +1,7 @@
 import logging
 
 import ray
+from ray.rllib.agents.ppo.ppo_tf_policy import compute_and_clip_gradients
 from ray.rllib.policy.sample_batch import SampleBatch
 from ray.rllib.evaluation.postprocessing import compute_advantages, \
     Postprocessing
@@ -133,7 +134,7 @@ class MARWILLoss:
 
                 # Exponentially weighted advantages.
                 c = tf.math.sqrt(policy._moving_average_sqd_adv_norm)
-                exp_advs = tf.math.exp(beta * (adv / c))
+                exp_advs = tf.math.exp(beta * (adv / (1e-8 + c)))
             # Static graph.
             else:
                 update_adv_norm = tf1.assign_add(
@@ -200,4 +201,5 @@ MARWILTFPolicy = build_tf_policy(
     stats_fn=stats,
     postprocess_fn=postprocess_advantages,
     before_loss_init=setup_mixins,
+    gradients_fn=compute_and_clip_gradients,
     mixins=[ValueNetworkMixin])
diff --git a/rllib/agents/marwil/marwil_torch_policy.py b/rllib/agents/marwil/marwil_torch_policy.py
index ef3558378..14ae943ec 100644
--- a/rllib/agents/marwil/marwil_torch_policy.py
+++ b/rllib/agents/marwil/marwil_torch_policy.py
@@ -4,7 +4,7 @@ from ray.rllib.evaluation.postprocessing import Postprocessing
 from ray.rllib.policy.policy_template import build_policy_class
 from ray.rllib.policy.sample_batch import SampleBatch
 from ray.rllib.utils.framework import try_import_torch
-from ray.rllib.utils.torch_ops import explained_variance
+from ray.rllib.utils.torch_ops import apply_grad_clipping, explained_variance
 
 torch, _ = try_import_torch()
 
@@ -98,5 +98,6 @@ MARWILTorchPolicy = build_policy_class(
     get_default_config=lambda: ray.rllib.agents.marwil.marwil.DEFAULT_CONFIG,
     stats_fn=stats,
     postprocess_fn=postprocess_advantages,
+    extra_grad_process_fn=apply_grad_clipping,
     before_loss_init=setup_mixins,
     mixins=[ValueNetworkMixin])
diff --git a/rllib/agents/ppo/ppo_tf_policy.py b/rllib/agents/ppo/ppo_tf_policy.py
index 57874ba29..5991da84e 100644
--- a/rllib/agents/ppo/ppo_tf_policy.py
+++ b/rllib/agents/ppo/ppo_tf_policy.py
@@ -182,9 +182,15 @@ def compute_and_clip_gradients(policy: Policy, optimizer: LocalOptimizer,
 
     # Clip by global norm, if necessary.
     if policy.config["grad_clip"] is not None:
+        # Defuse inf gradients (due to super large losses).
         grads = [g for (g, v) in grads_and_vars]
-        policy.grads, _ = tf.clip_by_global_norm(grads,
-                                                 policy.config["grad_clip"])
+        grads, _ = tf.clip_by_global_norm(grads, policy.config["grad_clip"])
+        # If the global_norm is inf -> All grads will be NaN. Stabilize this
+        # here by setting them to 0.0. This will simply ignore destructive loss
+        # calculations.
+        policy.grads = [
+            tf.where(tf.math.is_nan(g), tf.zeros_like(g), g) for g in grads
+        ]
         clipped_grads_and_vars = list(zip(policy.grads, variables))
         return clipped_grads_and_vars
     else: