Reinforcement Learning Neural Turing Machines - Revised

Abstract: The Neural Turing Machine (NTM) is more expressive than all previously considered models because of its external memory. It can be viewed as a broader effort to use abstract external Interfaces and to learn a parametric model that interacts with them. The capabilities of a model can be extended by providing it with proper Interfaces that interact with the world. These external Interfaces include memory, a database, a search engine, or a piece of software such as a theorem verifier. Some of these Interfaces are provided by the developers of the model. However, many important existing Interfaces, such as databases and search engines, are discrete. We examine feasibility of learning models to interact with discrete Interfaces. We investigate the following discrete Interfaces: a memory Tape, an input Tape, and an output Tape. We use a Reinforcement Learning algorithm to train a neural network that interacts with such Interfaces to solve simple algorithmic tasks. Our Interfaces are expressive enough to make our model Turing complete.

• The paper introduces RL-NTM, a model that integrates reinforcement learning with neural Turing machines to efficiently handle discrete interfaces.
• It demonstrates Turing completeness by managing discrete actions on algorithmic tasks such as sequence copying and reversing.
• The authors propose a novel gradient checking method for the Reinforce algorithm, improving reliability and debugging in complex models.

Analysis of "Reinforcement Learning Neural Turing Machines - Revised"

The paper by Zaremba and Sutskever explores the application of reinforcement learning (RL) to Neural Turing Machines (NTMs), aiming to enhance the model's ability to interact with discrete external interfaces. This is a significant advancement, as typical NTMs primarily leverage continuous, differentiable memory interfaces, limiting their compatibility with the discrete nature of many real-world systems.

Key Contributions

The authors present a model known as the RL-NTM, which integrates Reinforcement Learning to enable interaction with discrete interfaces such as input and output tapes, and a memory tape. They highlight that such discrete interfaces are often computationally efficient due to their access costs being invariant to scale, contrasting sharply with continuous systems, which typically scale linearly.

1. Turing Completeness with RL-NTM: By effectively utilizing reinforcement learning to manage discrete actions for memory and output writing, the RL-NTM achieves Turing completeness. This endows the model with the theoretical ability to solve any computable problem, provided sufficient resources and time.
2. Algorithmic Task Performance: The model's competency is evaluated on various algorithmic tasks like sequence copying and reversing. While the RL-NTM demonstrates success in these tasks, it crucially depends on the architecture of the controller and the task's nature, indicating areas where further optimization and architectural innovation may be necessary.
3. Interface-Controller Interaction: The paper formalizes the interaction between external interfaces and the controller. This is critical for tasks where multiple steps of decision-making and interaction are required, such as controlling the sequential data of input, memory, and output tapes.
4. Gradient Checking with Reinforce: An innovative gradient checking method is described, enabling the verification of RL-NTM's implementation correctness. This is especially important given the model's complexity, involving multiple interacting components and variance reduction techniques in the reinforcement learning gradient estimations.

Implementation and Challenges

The paper explores the implementation intricacies of the RL-NTM, noting the difficulties in ensuring correct behavior due to the numerous interacting components. The authors introduce a procedure to check the gradients of the Reinforce algorithm numerically, enhancing reliability and debugging capabilities.

The RL-NTM utilizes curriculum learning for training, which strategizes presenting problems of increasing complexity. This approach is pivotal as the RL-NTM struggles with difficult tasks unless it progresses through simpler instances first, showcasing the model's dependence on a structured training approach to achieve robustness in performance.

Implications and Future Directions

The RL-NTM's ability to interact with discrete interfaces expands its potential applications across more varied computational problems and real-world tasks. This capability can, in the future, extend to integrating with non-differentiable environments like databases and software systems, which are pivotal across industries demanding enhanced machine learning integration.

While the current empirical evaluation is limited to relatively simple tasks, future research may explore scaling these methods to more complex, real-world scenarios. One may also investigate further reducing the variance in gradient estimation or enhancing controller architecture to improve model efficiency and task solvability.

Conclusion

Zaremba and Sutskever's paper presents a thoughtful approach to redefining NTMs with reinforcement learning to handle discrete interfaces. While the RL-NTM still faces challenges with scalability and task complexity, the foundational work lays significant groundwork for building more universally capable models. These efforts pave the way for future developments that could embrace broader applications, ultimately enhancing model interaction capabilities with the inherently discrete, non-differentiable components of various systems.