Zero-Shot Learning of Text Adventure Games with Sentence-Level Semantics

Reinforcement learning algorithms such as Q-learning have shown great promise in training models to learn the optimal action to take for a given system state; a goal in applications with an exploratory or adversarial nature such as task-oriented dialogues or games. However, models that do not have direct access to their state are harder to train; when the only state access is via the medium of language, this can be particularly pronounced. We introduce a new model amenable to deep Q-learning that incorporates a Siamese neural network architecture and a novel refactoring of the Q-value function in order to better represent system state given its approximation over a language channel. We evaluate the model in the context of zero-shot text-based adventure game learning. Extrinsically, our model reaches the baseline's convergence performance point needing only 15% of its iterations, reaches a convergence performance point 15% higher than the baseline's, and is able to play unseen, unrelated games with no fine-tuning. We probe our new model's representation space to determine that intrinsically, this is due to the appropriate clustering of different linguistic mediation into the same state.

No reviews yet.

No comments yet.