Baseline DQN
In this problem, the major challenge lies in inconvenient observation and action spaces. Text sequences might be problematic on their own, as we discussed in the previous chapter. The variability of sequence lengths might cause vanishing and exploding gradients in RNNs, slow training, and convergence issues. In addition to that, our TextWorld environment provides us with several such sequences that we need to handle separately. Our scene description string, for example, might have a completely different meaning to the agent than the inventory string, which describes our possessions.
As mentioned, another obstacle is the action space. As you have seen in the previous section, TextWorld might provide us with a list of commands that we can execute in every state. It significantly reduces the action space we need to choose from, but there are other complications. One of them is that the list of admissible commands changes from state to state (as different locations might...
