diff --git a/examples/acrobot_single_process.py b/examples/acrobot_single_process.py
new file mode 100644
index 0000000..84855c2
--- /dev/null
+++ b/examples/acrobot_single_process.py
@@ -0,0 +1,135 @@
+import gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import rltorch
+import rltorch.network as rn
+import rltorch.memory as M
+import rltorch.env as E
+from rltorch.action_selector import ArgMaxSelector
+from tensorboardX import SummaryWriter
+import torch.multiprocessing as mp
+
+class Value(nn.Module):
+  def __init__(self, state_size, action_size):
+    super(Value, self).__init__()
+    self.state_size = state_size
+    self.action_size = action_size
+
+    self.fc1 = rn.NoisyLinear(state_size, 255)
+    self.fc_norm = nn.LayerNorm(255)
+    
+    self.value_fc = rn.NoisyLinear(255, 255)
+    self.value_fc_norm = nn.LayerNorm(255)
+    self.value = rn.NoisyLinear(255, 1)
+    
+    self.advantage_fc = rn.NoisyLinear(255, 255)
+    self.advantage_fc_norm = nn.LayerNorm(255)
+    self.advantage = rn.NoisyLinear(255, action_size)
+
+  def forward(self, x):
+    x = F.relu(self.fc_norm(self.fc1(x)))
+    
+    state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
+    state_value = self.value(state_value)
+    
+    advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
+    advantage = self.advantage(advantage)
+    
+    x = state_value + advantage - advantage.mean()
+    
+    return x
+
+
+config = {}
+config['seed'] = 901
+config['environment_name'] = 'Acrobot-v1'
+config['memory_size'] = 2000
+config['total_training_episodes'] = 5
+config['total_evaluation_episodes'] = 2
+config['batch_size'] = 32
+config['learning_rate'] = 1e-3
+config['target_sync_tau'] = 1e-1
+config['discount_rate'] = 0.99
+config['replay_skip'] = 0
+# How many episodes between printing out the episode stats
+config['print_stat_n_eps'] = 1
+config['disable_cuda'] = False
+# Prioritized vs Random Sampling
+# 0 - Random sampling
+# 1 - Only the highest prioirities
+config['prioritized_replay_sampling_priority'] = 0.6
+# How important are the weights for the loss?
+# 0 - Treat all losses equally
+# 1 - Lower the importance of high losses
+# Should ideally start from 0 and move your way to 1 to prevent overfitting
+config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)
+  
+def train(runner, agent, config, logger = None, logwriter = None):
+    finished = False
+    last_episode_num = 1
+    while not finished:
+        runner.run(config['replay_skip'] + 1)
+        agent.learn()
+        if logwriter is not None:
+          if last_episode_num < runner.episode_num:
+            last_episode_num = runner.episode_num
+            agent.net.log_named_parameters()
+          logwriter.write(logger)
+        finished = runner.episode_num > config['total_training_episodes']
+
+if __name__ == "__main__":
+  torch.multiprocessing.set_sharing_strategy('file_system') # To not hit file descriptor memory limit
+
+  # Setting up the environment
+  rltorch.set_seed(config['seed'])
+  print("Setting up environment...", end = " ")
+  env = E.TorchWrap(gym.make(config['environment_name']))
+  env.seed(config['seed'])
+  print("Done.")
+      
+  state_size = env.observation_space.shape[0]
+  action_size = env.action_space.n
+
+  # Logging
+  logger = rltorch.log.Logger()
+  # logwriter = rltorch.log.LogWriter(logger, SummaryWriter())
+  logwriter = rltorch.log.LogWriter(SummaryWriter())
+
+  # Setting up the networks
+  device = torch.device("cuda:0" if torch.cuda.is_available() and not config['disable_cuda'] else "cpu")
+  net = rn.Network(Value(state_size, action_size), 
+                      torch.optim.Adam, config, device = device, name = "DQN", logger = logger)
+  target_net = rn.TargetNetwork(net, device = device)
+  net.model.share_memory()
+  target_net.model.share_memory()
+
+  # Actor takes a net and uses it to produce actions from given states
+  actor = ArgMaxSelector(net, action_size, device = device)
+  # Memory stores experiences for later training
+  memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
+  # memory = M.ReplayMemory(capacity = config['memory_size'])
+
+  # Runner performs a certain number of steps in the environment
+  runner = rltorch.env.EnvironmentRunSync(env, actor, config, name = "Training", memory = memory, logwriter = logwriter)
+
+  # Agent is what performs the training
+  agent = rltorch.agents.DQNAgent(net, memory, config, target_net = target_net, logger = logger)
+    
+  print("Training...")
+
+  train(runner, agent, config, logger = logger, logwriter = logwriter) 
+
+  # For profiling...
+  # import cProfile
+  # cProfile.run('train(runner, agent, config, logger = logger, logwriter = logwriter )')
+  # python -m torch.utils.bottleneck /path/to/source/script.py [args] is also a good solution...
+
+  print("Training Finished.")
+
+  print("Evaluating...")
+  rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
+  print("Evaulations Done.")
+
+  logwriter.close() # We don't need to write anything out to disk anymore
diff --git a/rltorch/env/simulate.py b/rltorch/env/simulate.py
index 42ca46f..d26ef9c 100644
--- a/rltorch/env/simulate.py
+++ b/rltorch/env/simulate.py
@@ -1,3 +1,6 @@
+from copy import deepcopy 
+import rltorch
+
 def simulateEnvEps(env, actor, config, total_episodes = 1, memory = None, logger = None, name = ""):
   for episode in range(total_episodes):
     state = env.reset()
@@ -19,3 +22,44 @@ def simulateEnvEps(env, actor, config, total_episodes = 1, memory = None, logger
     if logger is not None:
       logger.append(name + '/EpisodeReward', episode_reward)
 
+
+class EnvironmentRunSync():
+  def __init__(self, env, actor, config, memory = None, logwriter = None, name = ""):
+    self.env = env
+    self.name = name
+    self.actor = actor
+    self.config = deepcopy(config)
+    self.logwriter = logwriter
+    self.memory = memory
+    self.episode_num = 1
+    self.episode_reward = 0
+    self.last_state = env.reset()
+
+  def run(self, iterations):
+    state = self.last_state
+    logger = rltorch.log.Logger() if self.logwriter is not None else None
+    for _ in range(iterations):
+      action = self.actor.act(state)
+      next_state, reward, done, _ = self.env.step(action)
+       
+      self.episode_reward += reward
+      if self.memory is not None:
+        self.memory.append(state, action, reward, next_state, done)
+       
+      state = next_state
+
+      if done:
+        if self.episode_num % self.config['print_stat_n_eps'] == 0:
+          print("episode: {}/{}, score: {}"
+            .format(self.episode_num, self.config['total_training_episodes'], self.episode_reward))
+          
+        if self.logwriter is not None:
+          logger.append(self.name + '/EpisodeReward', self.episode_reward)
+        self.episode_reward = 0
+        state = self.env.reset()
+        self.episode_num +=  1
+          
+    if self.logwriter is not None:
+      self.logwriter.write(logger)
+    
+    self.last_state = state
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