Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels

TL;DR

This paper introduces a novel unsupervised model-based reinforcement learning approach that pre-trains agents and adapts efficiently to downstream tasks, significantly outperforming previous methods on the Unsupervised RL Benchmark from pixels.

Contribution

It proposes a new method combining unsupervised model-based RL with a hybrid planner and task-aware fine-tuning, advancing the state-of-the-art in visual control benchmarks.

Findings

Achieved 93.59% normalized performance on URLB, surpassing previous baselines.

Validated robustness and generalization through large-scale empirical studies.

Demonstrated effective transfer to real-world RL benchmarks.

Abstract

Controlling artificial agents from visual sensory data is an arduous task. Reinforcement learning (RL) algorithms can succeed but require large amounts of interactions between the agent and the environment. To alleviate the issue, unsupervised RL proposes to employ self-supervised interaction and learning, for adapting faster to future tasks. Yet, as shown in the Unsupervised RL Benchmark (URLB; Laskin et al. 2021), whether current unsupervised strategies can improve generalization capabilities is still unclear, especially in visual control settings. In this work, we study the URLB and propose a new method to solve it, using unsupervised model-based RL, for pre-training the agent, and a task-aware fine-tuning strategy combined with a new proposed hybrid planner, Dyna-MPC, to adapt the agent for downstream tasks. On URLB, our method obtains 93.59% overall normalized performance, surpassing previous baselines by a staggering margin. The approach is empirically evaluated through a large-scale empirical study, which we use to validate our design choices and analyze our models. We also show robust performance on the Real-Word RL benchmark, hinting at resiliency to environment perturbations during adaptation. Project website: https://masteringurlb.github.io/