General Value Function Networks

TL;DR

This paper introduces General Value Function Networks (GVFNs), a new RNN architecture that uses future predictions as internal states, improving training robustness and incorporating domain knowledge for better learning in partially observable environments.

Contribution

The paper proposes GVFNs, an innovative RNN design where internal states are predictions of the future, enhancing trainability and robustness compared to traditional RNNs.

Findings

GVFNs are more robust to truncation levels in backpropagation.

GVFNs often require only one-step gradient updates for training.

Incorporating future predictions improves RNN stability and performance.

Abstract

State construction is important for learning in partially observable environments. A general purpose strategy for state construction is to learn the state update using a Recurrent Neural Network (RNN), which updates the internal state using the current internal state and the most recent observation. This internal state provides a summary of the observed sequence, to facilitate accurate predictions and decision-making. At the same time, specifying and training RNNs is notoriously tricky, particularly as the common strategy to approximate gradients back in time, called truncated Back-prop Through Time (BPTT), can be sensitive to the truncation window. Further, domain-expertise--which can usually help constrain the function class and so improve trainability--can be difficult to incorporate into complex recurrent units used within RNNs. In this work, we explore how to use multi-step predictions to constrain the RNN and incorporate prior knowledge. In particular, we revisit the idea of using predictions to construct state and ask: does constraining (parts of) the state to consist of predictions about the future improve RNN trainability? We formulate a novel RNN architecture, called a General Value Function Network (GVFN), where each internal state component corresponds to a prediction about the future represented as a value function. We first provide an objective for optimizing GVFNs, and derive several algorithms to optimize this objective. We then show that GVFNs are more robust to the truncation level, in many cases only requiring one-step gradient updates.