diff --git a/tests/test_multi_speed.py b/tests/test_multi_speed.py
index b410754c5dfc4f5b055b9e0268581873812af485..46310a2c069b921c4113480131e37ed82b7a268c 100644
--- a/tests/test_multi_speed.py
+++ b/tests/test_multi_speed.py
@@ -26,7 +26,7 @@ class RandomAgent:
         :param state: input is the observation of the agent
         :return: returns an action
         """
-        return np.random.choice(np.arange(self.action_size))
+        return np.random.choice([1, 2, 3])
 
     def step(self, memories):
         """
@@ -50,34 +50,40 @@ class RandomAgent:
 agent = RandomAgent(218, 4)
 n_trials = 5
 
+
 # Empty dictionary for all agent action
 action_dict = dict()
 
 
+# Set all the different speeds
+
 def test_multi_speed_init():
     # Reset environment and get initial observations for all agents
-    obs = env.reset()
+    env.reset()
     # Here you can also further enhance the provided observation by means of normalization
     # See training navigation example in the baseline repository
+    old_pos = []
     for i_agent in range(env.get_num_agents()):
-        env.agents[i_agent].speed_data['speed'] = 1. / np.random.randint(1, 10)
+        env.agents[i_agent].speed_data['speed'] = 1. / (i_agent + 1)
+        old_pos.append(env.agents[i_agent].position)
     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 = agent.act(0)
             action_dict.update({a: action})
+            # Check that agent did not move inbetween its speed updates
+            assert old_pos[a] == env.agents[a].position
 
         # 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)
+        _, _, _, _ = env.step(action_dict)
 
-        # Update replay buffer and train agent
-        for a in range(env.get_num_agents()):
-            agent.step((obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a]))
-            score += all_rewards[a]
+        # Update old position
+        for i_agent in range(env.get_num_agents()):
+            if (step + 1) % (i_agent + 1) == 0:
+                print(step, i_agent, env.agents[a].position)
 
-        obs = next_obs.copy()
-        if done['__all__']:
-            break
+                old_pos[i_agent] = env.agents[i_agent].position