diff --git a/rltorch/network/ESNetwork.py b/rltorch/network/ESNetwork.py
index be0970e..7d6d8d0 100644
--- a/rltorch/network/ESNetwork.py
+++ b/rltorch/network/ESNetwork.py
@@ -16,6 +16,7 @@ class ESNetwork(Network):
         self.population_size = population_size
         self.fitness = fitness_fn
         self.sigma = sigma
+        assert self.sigma > 0
 
     # We're not going to be calculating gradients in the traditional way
     # So there's no need to waste computation time keeping track
diff --git a/rltorch/network/ESNetworkMP.py b/rltorch/network/ESNetworkMP.py
new file mode 100644
index 0000000..ec954c2
--- /dev/null
+++ b/rltorch/network/ESNetworkMP.py
@@ -0,0 +1,89 @@
+import numpy as np
+import torch
+from .Network import Network
+from copy import deepcopy
+import torch.multiprocessing as mp
+import functools
+
+class fn_copy:
+    def __init__(self, fn, args):
+        self.fn = fn
+        self.args = args
+    def __call__(self, x):
+        return self.fn(x, *(self.args))
+
+# [TODO] Should we torch.no_grad the __call__?
+# What if we want to sometimes do gradient descent as well?
+class ESNetworkMP(Network):
+    """
+    Network that functions from the paper Evolutionary Strategies (https://arxiv.org/abs/1703.03864)
+    fitness_fun := model, *args -> fitness_value (float)
+    We wish to find a model that maximizes the fitness function
+    """
+    def __init__(self, model, optimizer, population_size, fitness_fn, config, sigma = 0.05, device = None, logger = None, name = ""):
+        super(ESNetworkMP, self).__init__(model, optimizer, config, device, logger, name)
+        self.population_size = population_size
+        self.fitness = fitness_fn
+        self.sigma = sigma
+        assert self.sigma > 0
+        self.pool = mp.Pool(processes=3) #[TODO] Probably should make number of processes a config variable
+
+    # We're not going to be calculating gradients in the traditional way
+    # So there's no need to waste computation time keeping track
+    def __call__(self, *args):
+        with torch.no_grad():
+            result = self.model(*args)
+        return result
+
+
+    def _generate_noise_dicts(self):
+        model_dict = self.model.state_dict()
+        white_noise_dict = {}
+        noise_dict = {}
+        for key in model_dict.keys():
+            white_noise_dict[key] = torch.randn(self.population_size, *model_dict[key].shape, device = self.device)
+            noise_dict[key] = self.sigma * white_noise_dict[key]
+        return white_noise_dict, noise_dict
+
+    def _generate_candidate_solutions(self, noise_dict):
+        model_dict = self.model.state_dict()
+        candidate_solutions = []
+        for i in range(self.population_size):
+            candidate_statedict = {}
+            for key in model_dict.keys():
+                candidate_statedict[key] = model_dict[key] + noise_dict[key][i]
+            candidate = deepcopy(self.model)
+            candidate.load_state_dict(candidate_statedict)
+            candidate_solutions.append(candidate)
+        return candidate_solutions
+
+    
+    def calc_gradients(self, *args):
+        ## Generate Noise
+        white_noise_dict, noise_dict = self._generate_noise_dicts()
+        
+        ## Generate candidate solutions
+        candidate_solutions = self._generate_candidate_solutions(noise_dict)
+        
+        ## Calculate fitness then mean shift, scale
+        fitness_values = torch.tensor(list(self.pool.map(fn_copy(self.fitness, args), candidate_solutions)), device = self.device)
+
+        if self.logger is not None:
+            self.logger.append(self.name + "/" + "fitness_value", fitness_values.mean().item())
+        fitness_values = (fitness_values - fitness_values.mean()) / (fitness_values.std() + np.finfo('float').eps)
+
+        ## Insert adjustments into gradients slot
+        self.zero_grad()
+        for name, param in self.model.named_parameters():
+            if param.requires_grad:
+                noise_dim_n = len(white_noise_dict[name].shape)
+                dim = np.repeat(1, noise_dim_n - 1).tolist() if noise_dim_n > 0 else []
+                param.grad = (white_noise_dict[name] * fitness_values.float().reshape(self.population_size, *dim)).mean(0) / self.sigma
+        
+        del white_noise_dict, noise_dict, candidate_solutions
+    
+    # To address error that you can't pickle pool objects...
+    def __getstate__(self):
+        self_dict = self.__dict__.copy()
+        del self_dict['pool']
+        return self_dict
\ No newline at end of file
diff --git a/rltorch/network/__init__.py b/rltorch/network/__init__.py
index 028398f..d178654 100644
--- a/rltorch/network/__init__.py
+++ b/rltorch/network/__init__.py
@@ -1,4 +1,5 @@
 from .ESNetwork import *
+from .ESNetworkMP import *
 from .Network import *
 from .NoisyLinear import *
 from .TargetNetwork import *
\ No newline at end of file