prior first test

main.py

@@ -144,27 +144,6 @@ class TensorboardCallback(BaseCallback):
         return True
 
 
-def sample_random_action(agent, mask):
-    """Sample random UNMASKED action(s) for agent."""
-    # Return a list of actions: 1 for each action subspace
-    if agent.multi_action_mode:
-        split_masks = np.split(mask, agent.action_spaces.cumsum()[:-1])
-        return [np.random.choice(np.arange(len(m_)), p=m_/m_.sum()) for m_ in split_masks]
-
-    # Return a single action
-    else:
-        return np.random.choice(np.arange(agent.action_spaces), p=mask/mask.sum())
-
-def sample_random_actions(env, obs):
-    """Samples random UNMASKED actions for each agent in obs."""
-        
-    actions = {
-        a_idx: 0
-        for a_idx in range( len(obs))
-    }
-
-    return actions
-
 def printMarket(market):
     for i in range(len(market)):
         step=market[i]