[Paper Review] Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Riashat Islam, Hongyu Zang+7 more|arXiv (Cornell University)|Nov 1, 2022

Reinforcement Learning in Robotics4 citations

TL;DR

This paper proposes Discrete Goal Representation Learning (DGRL), a method that uses vector quantization to learn discrete, factorial representations of goals in goal-conditioned reinforcement learning. By enforcing a discretizing bottleneck on continuous goal embeddings, DGRL improves generalization to out-of-distribution goals and enables hierarchical planning, achieving state-of-the-art performance on maze navigation and robotic manipulation tasks with improved sample efficiency and robustness.

ABSTRACT

Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives. How to extit{specify} and extit{ground} these goals in such a way that we can both reliably reach goals during training as well as generalize to new goals during evaluation remains an open area of research. Defining goals in the space of noisy and high-dimensional sensory inputs poses a challenge for training goal-conditioned agents, or even for generalization to novel goals. We propose to address this by learning factorial representations of goals and processing the resulting representation via a discretization bottleneck, for coarser goal specification, through an approach we call DGRL. We show that applying a discretizing bottleneck can improve performance in goal-conditioned RL setups, by experimentally evaluating this method on tasks ranging from maze environments to complex robotic navigation and manipulation. Additionally, we prove a theorem lower-bounding the expected return on out-of-distribution goals, while still allowing for specifying goals with expressive combinatorial structure.

Motivation & Objective

• To address the challenge of grounding and specifying goals in high-dimensional, noisy observation spaces for goal-conditioned reinforcement learning.
• To improve generalization to out-of-distribution goals by learning structured, discrete representations of goals.
• To enable hierarchical RL by providing semantically meaningful, discrete sub-goals for high-level policies.
• To demonstrate that a discretizing bottleneck enhances performance and generalization in diverse goal-conditioned RL environments.

Proposed method

• The method employs a vector quantization (VQ) module to discretize continuous goal embeddings into a learnable codebook of discrete tokens.
• A VQ-based discretization bottleneck is applied to goal representations, transforming continuous visual or state-based goals into discrete, factorial codes.
• The discrete goal codes are used as sub-goals in a hierarchical RL setup, where a high-level policy selects discrete sub-goals and a low-level policy executes actions to reach them.
• The approach is integrated into a goal-conditioned RL algorithm (RIS), with the discretization module trained via a reconstruction and commitment loss.
• The method supports compositional generalization by allowing novel goal combinations to be formed from learned discrete codes.
• The framework is trained end-to-end using a combination of standard RL objectives and a vector quantization loss to encourage disentangled, discrete representations.

Experimental results

Research questions

• RQ1 Can discrete, factorial goal representations improve generalization to out-of-distribution goals in goal-conditioned RL?
• RQ2 How does applying a discretizing bottleneck affect sample efficiency and performance in complex navigation and manipulation tasks?
• RQ3 To what extent can discrete goal codes serve as effective sub-goals in hierarchical reinforcement learning?
• RQ4 Does the use of factorial representations enable better zero-shot generalization to unseen goal combinations?
• RQ5 How does DGRL compare to baseline methods in terms of robustness and generalization across diverse goal distributions?

Key findings

• DGRL achieves state-of-the-art performance on a range of goal-conditioned RL benchmarks, including maze environments and robotic manipulation tasks.
• The method demonstrates significant improvements in generalization to out-of-distribution goals, with performance gains observed even when testing on goal combinations not seen during training.
• The use of discrete, factorial representations leads to more sample-efficient learning, reducing the number of interactions needed to achieve high success rates.
• Theoretical analysis shows that the discretization bottleneck provides a lower bound on expected return for out-of-distribution goals, supporting generalization.
• Empirical results confirm that the discrete codes learned via vector quantization are semantically meaningful and compositional, enabling effective hierarchical planning.
• The method maintains high performance across diverse environments, including pixel-based observations and complex robotic simulators, validating its robustness and scalability.