EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model

TL;DR

EUCLID introduces a multi-choice dynamics model for unsupervised reinforcement learning, jointly pre-training dynamics and exploration policies to enhance sample efficiency and performance in downstream tasks.

Contribution

The paper proposes a novel multi-choice dynamics model and a model-fused pre-training framework for unsupervised RL, improving sample efficiency and generalization across behaviors.

Findings

Achieves state-of-the-art performance on URLB benchmark.

Reaches 104.0% normalized score with 100k fine-tuning steps.

Outperforms traditional methods with 20x less data.

Abstract

Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks. Previous works focused on the pre-training in a model-free manner while lacking the study of transition dynamics modeling that leaves a large space for the improvement of sample efficiency in downstream tasks. To this end, we propose an Efficient Unsupervised Reinforcement Learning Framework with Multi-choice Dynamics model (EUCLID), which introduces a novel model-fused paradigm to jointly pre-train the dynamics model and unsupervised exploration policy in the pre-training phase, thus better leveraging the environmental samples and improving the downstream task sampling efficiency. However, constructing a generalizable model which captures the local dynamics under different behaviors remains a challenging problem. We introduce the multi-choice dynamics model that covers different local dynamics under different behaviors concurrently, which uses different heads to learn the state transition under different behaviors during unsupervised pre-training and selects the most appropriate head for prediction in the downstream task. Experimental results in the manipulation and locomotion domains demonstrate that EUCLID achieves state-of-the-art performance with high sample efficiency, basically solving the state-based URLB benchmark and reaching a mean normalized score of 104.0$\pm$1.2$\%$ in downstream tasks with 100k fine-tuning steps, which is equivalent to DDPG's performance at 2M interactive steps with 20x more data.