diff --git a/examples/training_example.py b/examples/training_example.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9359ccc179b41bcf11bff2d6f196d63a69bebe7
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+++ b/examples/training_example.py
@@ -0,0 +1,80 @@
+from flatland.envs.generators import complex_rail_generator
+from flatland.envs.rail_env import RailEnv
+import numpy as np
+from flatland.utils.rendertools import RenderTool
+
+np.random.seed(1)
+
+# Use the complex_rail_generator to generate feasible network configurations with corresponding tasks
+# Training on simple small tasks is the best way to get familiar with the environment
+#
+env = RailEnv(width=15,
+              height=15,
+              rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=10, min_dist=10, max_dist=99999, seed=0),
+              number_of_agents=5)
+
+
+# Import your own Agent or use RLlib to train agents on Flatland
+# As an example we use a random agent here
+
+
+class RandomAgent:
+
+    def __init__(self, state_size, action_size):
+        self.state_size = state_size
+        self.action_size = action_size
+
+    def act(self, state):
+        """
+        :param state: input is the observation of the agent
+        :return: returns an action
+        """
+        return np.random.choice(np.arange(self.action_size))
+
+    def step(self, memories):
+        """
+        Step function to improve agent by adjusting policy given the observations
+
+        :param memories: SARS Tuple to be
+        :return:
+        """
+        return
+
+    def save(self):
+        # Store the current policy
+        return
+
+
+# Load the agent with the parameters corresponding to the environment and observation_builder
+agent = RandomAgent(env.get_observation_size(), env.get_action_size())
+n_trials = 1000
+
+# Empty dictionary for all agent action
+action_dict = dict()
+
+for trials in range(1, n_trials + 1):
+
+    # Reset environment and get initial observations for all agents
+    obs = env.reset()
+    # Here you can also further enhance the provided observation by means of normalization
+    # See training navigation example in the baseline repository
+
+    score = 0
+    # Run episode
+    for step in range(100):
+        # Chose an action for each agent in the environment
+        for a in range(env.get_num_agents()):
+            action = agent.act(obs[a])
+            action_dict.update({a: action})
+
+        # Environment step which returns the observations for all agents, their corresponding
+        # reward and whether their are done
+        next_obs, all_rewards, done, _ = env.step(action_dict)
+
+        # Update replay buffer and train agent
+        agent.step(obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a])
+        score += all_rewards[a]
+
+        obs = next_obs.copy()
+        if done['__all__']:
+            break