Value Iteration Networks (1602.02867v4)

Abstract: We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.

• The paper presents an innovative integration of a differentiable value iteration algorithm into a CNN framework to embed planning within neural networks.
• VINs employ convolutional representations of MDP transition kernels, enabling end-to-end training through gradient descent for improved policy learning.
• Empirical results show that VINs generalize effectively across grid-world navigation, continuous control, and natural language tasks, outperforming standard CNN models.

Value Iteration Networks

The paper by Tamar et al. introduces the concept of Value Iteration Networks (VINs), which embed a planning module within a differentiable neural network framework. This paper is of particular interest for its integration of planning-based reasoning into neural network architectures, specifically for applications in reinforcement learning (RL).

The central contribution of the paper is the embedding of a novel, differentiable approximation of the classical value iteration (VI) algorithm into a neural architecture. By representing the VI algorithm as a convolutional neural network (CNN), the authors harness the power of end-to-end training using standard backpropagation. This allows VIN policies to demonstrate improved generalization to new, unseen tasks in domains requiring planning and reasoning.

Methodology

VINs are designed to function as a policy representation where the traditional planning task, often executed by VI, is naturally incorporated into the neural network's architecture. The strategy involves:

• State and Action Representations: Employing MDPs with discrete state and action spaces. The transition kernels are represented as convolution operations, key for the differentiation of the model.
• Differentiable Planning Module: The VI process calculated through the CNN-based planning module allows gradient descent to optimize planning strategies actively.

These methodologies enable VINs to craft policies that are superior at generalizing across various domains, which include synthetic grid-world navigation, continuous control tasks, and even natural language processing challenges like the WebNav environment.

Empirical Results

The authors demonstrate the efficacy of VINs across several applications with the following highlights:

• Grid-World Domains: The VINs achieve a substantial success rate in grid-world navigation tasks. A marked improvement is noted as problem size increases, outperforming other architectures such as standard CNNs and FCNs.
• Continuous Control: Through leveraging hierarchical planning, VINs demonstrate advanced generalization in continuous control environments. Policies trained with VINs display strong trajectory adherence and goal achievement compared to traditional methods.
• Natural Language-Based Search: In tasks like the WebNav challenge, VINs perform solidly, particularly when encountering more complex initial states that require strategic backtracking not inherently managed by reactive policies.

Theoretical and Practical Implications

VINs showcase an avenue for integrating explicit planning mechanisms into deep learning models. The implications are twofold:

1. Theoretical Advancements: VINs present a unified framework that captures both reactive and deliberative aspects of decision-making, offering a new perspective on policy learning in RL. They pave the way for further research on incorporating other planning algorithms into differentiable modules.
2. Practical Applications: By enhancing model generalization and efficiency, VINs encourage their use in robotics, autonomous navigation, and various control systems. This has far-reaching implications in fields requiring adaptable and data-efficient decision-making processes.

Future Prospects

The clear avenue for future research involves expanding VIN architectures to include different planning strategies or hierarchical VIN implementations. Further exploration into using VINs in more dynamic and less structured environments could yield significant innovations in RL systems.

In conclusion, the introduction of VINs marks a notable progression in the unification of planning algorithms with neural networks. By incorporating explicit planning computation into RL models, this paper sets a foundation for innovative advancements in both model-free and model-based RL methodologies.