Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning

Takayuki Osa, Tatsuya Harada

TL;DR

The paper tackles discovering multiple solutions from a single offline RL task by learning unsupervised latent skills and using an EM-like coordinate ascent between a latent-conditioned policy and its posterior. DiveOff jointly optimizes the policy, latent posterior, and a mutual-information term under KL constraints, yielding diverse, high-quality behaviors in offline data. Empirical results on toy tasks and D4RL-based diverse datasets demonstrate that DiveOff can produce qualitatively distinct solutions while maintaining competitive performance, and that these diverse behaviors enable effective few-shot adaptation to unseen environments. The work advances offline RL by integrating latent-skill discovery with principled diversity regularization, offering practical benefits for robustness and transfer in real-world tasks.

Abstract

Recent studies on online reinforcement learning (RL) have demonstrated the advantages of learning multiple behaviors from a single task, as in the case of few-shot adaptation to a new environment. Although this approach is expected to yield similar benefits in offline RL, appropriate methods for learning multiple solutions have not been fully investigated in previous studies. In this study, we therefore addressed the problem of finding multiple solutions from a single task in offline RL. We propose algorithms that can learn multiple solutions in offline RL, and empirically investigate their performance. Our experimental results show that the proposed algorithm learns multiple qualitatively and quantitatively distinctive solutions in offline RL.

Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning

TL;DR

The paper tackles discovering multiple solutions from a single offline RL task by learning unsupervised latent skills and using an EM-like coordinate ascent between a latent-conditioned policy and its posterior. DiveOff jointly optimizes the policy, latent posterior, and a mutual-information term under KL constraints, yielding diverse, high-quality behaviors in offline data. Empirical results on toy tasks and D4RL-based diverse datasets demonstrate that DiveOff can produce qualitatively distinct solutions while maintaining competitive performance, and that these diverse behaviors enable effective few-shot adaptation to unseen environments. The work advances offline RL by integrating latent-skill discovery with principled diversity regularization, offering practical benefits for robustness and transfer in real-world tasks.

Abstract

Recent studies on online reinforcement learning (RL) have demonstrated the advantages of learning multiple behaviors from a single task, as in the case of few-shot adaptation to a new environment. Although this approach is expected to yield similar benefits in offline RL, appropriate methods for learning multiple solutions have not been fully investigated in previous studies. In this study, we therefore addressed the problem of finding multiple solutions from a single task in offline RL. We propose algorithms that can learn multiple solutions in offline RL, and empirically investigate their performance. Our experimental results show that the proposed algorithm learns multiple qualitatively and quantitatively distinctive solutions in offline RL.
Paper Structure (31 sections, 38 equations, 5 figures, 18 tables, 1 algorithm)

This paper contains 31 sections, 38 equations, 5 figures, 18 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Related Work
  4. Reinforcement Learning
  5. Latent-Conditioned Policy
  6. Algorithm to Learn Diverse Behaviors in Offline RL
  7. Problem Formulation
  8. E-Step
  9. M-Step
  10. Learning the Latent Skill Space via Mutual Information Maximization
  11. Practical Algorithm
  12. Experiments
  13. Evaluation on Toy Task
  14. Performance on D4RL Tasks with Diverse Behaviors
  15. Datasets with Diverse Behaviors
  16. ...and 16 more sections

Figures (5)

  • Figure 1: Sequential snapshots of multiple locomotion behaviors obtained by proposed method for the hopper agent.
  • Figure 2: Path planning task on two-dimensional space.
  • Figure 3: Sequential snapshots of locomotion behaviors obtained by DiveOff on the walker2dvel-diverse-expert task.
  • Figure 4: Tasks for few-shot adaptation. Links modified from the original agent are indicated as blue.
  • Figure 5: Examples of locomotion behaviors contained in the expert-diverse datasets. The color of the legs of the ant agent was changed for visibility.