diff --git a/rltorch/agents/QEPAgent.py b/rltorch/agents/QEPAgent.py
index 71e312d..de203b0 100644
--- a/rltorch/agents/QEPAgent.py
+++ b/rltorch/agents/QEPAgent.py
@@ -5,12 +5,13 @@ import torch
 from torch.distributions import Categorical
 import rltorch
 import rltorch.memory as M
+import torch.nn.functional as F
 
 # Q-Evolutionary Policy Agent
 # Maximizes the policy with respect to the Q-Value function.
 # Since function is non-differentiabile, depends on the Evolutionary Strategy algorithm
 class QEPAgent:
-    def __init__(self, policy_net, value_net, memory, config, target_value_net = None, logger = None):
+    def __init__(self, policy_net, value_net, memory, config, target_value_net = None, logger = None, entropy_importance = 0):
         self.policy_net = policy_net
         assert isinstance(self.policy_net, rltorch.network.ESNetwork) or isinstance(self.policy_net, rltorch.network.ESNetworkMP)
         self.policy_net.fitness = self.fitness
@@ -20,6 +21,7 @@ class QEPAgent:
         self.config = deepcopy(config)
         self.logger = logger
         self.policy_skip = 4
+        self.entropy_importance = entropy_importance
     
     def save(self, file_location):
         torch.save({
@@ -36,7 +38,8 @@ class QEPAgent:
 
     def fitness(self, policy_net, value_net, state_batch):
         batch_size = len(state_batch)
-        action_probabilities = policy_net(state_batch)
+        with torch.no_grad():
+            action_probabilities = policy_net(state_batch)
         action_size = action_probabilities.shape[1]
         distributions = list(map(Categorical, action_probabilities))
         actions = torch.stack([d.sample() for d in distributions])
@@ -53,6 +56,7 @@ class QEPAgent:
         obtained_values = state_values.gather(1, actions.view(len(state_batch), 1)).squeeze(1)
         # return -obtained_values.mean().item()
         entropy_importance = 0 # Entropy accounting for 1% of loss seems to work well
+        entropy_importance = next(self.entropy_importance) if isinstance(self.entropy_importance, collections.Iterable) else self.entropy_importance
         value_importance = 1 - entropy_importance
         
         # entropy_loss = (action_probabilities * torch.log2(action_probabilities)).sum(1) # Standard entropy loss from information theory
@@ -131,6 +135,7 @@ class QEPAgent:
           self.policy_skip -= 1
           return
         self.policy_skip = 4
+
         if self.target_value_net is not None:
           self.policy_net.calc_gradients(self.target_value_net, state_batch)
         else:
diff --git a/rltorch/network/ESNetworkMP.py b/rltorch/network/ESNetworkMP.py
index ec954c2..69b0d21 100644
--- a/rltorch/network/ESNetworkMP.py
+++ b/rltorch/network/ESNetworkMP.py
@@ -26,7 +26,8 @@ class ESNetworkMP(Network):
         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
+        mp_ctx = mp.get_context("spawn")
+        self.pool = mp_ctx.Pool(processes=2) #[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