def evaluate_policy_return(T, behavioral_policy, target_policy):
    returns = []
    for trajectory in T:
        importance_weight = 1
        trajectory_return = 0
        for transition in trajectory:
            state, action, reward  = transition[0 : 3]
            action_prob_b = behavioral_policy(state, action)
            action_prob_t = target_policy(state, action)

            importance_weight *= (action_prob_t / action_prob_b)
            trajectory_return += reward

        returns.append(trajectory_return * importance_weight)

    return np.mean(returns)