CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning

TL;DR

CUDC introduces a curiosity-driven, adaptive data collection method for offline reinforcement learning that enhances multi-task learning by diversifying feature space and improving data quality.

Contribution

It proposes a novel adaptive temporal distance mechanism for task-agnostic data collection in offline RL, improving data diversity and learning efficiency.

Findings

CUDC outperforms existing unsupervised methods in efficiency.

CUDC achieves better learning performance on DeepMind control suite tasks.

Adaptive reachability improves data quality for offline RL.

Abstract

Offline reinforcement learning (RL) aims to learn an effective policy from a pre-collected dataset. Most existing works are to develop sophisticated learning algorithms, with less emphasis on improving the data collection process. Moreover, it is even challenging to extend the single-task setting and collect a task-agnostic dataset that allows an agent to perform multiple downstream tasks. In this paper, we propose a Curiosity-driven Unsupervised Data Collection (CUDC) method to expand feature space using adaptive temporal distances for task-agnostic data collection and ultimately improve learning efficiency and capabilities for multi-task offline RL. To achieve this, CUDC estimates the probability of the k-step future states being reachable from the current states, and adapts how many steps into the future that the dynamics model should predict. With this adaptive reachability mechanism in place, the feature representation can be diversified, and the agent can navigate itself to collect higher-quality data with curiosity. Empirically, CUDC surpasses existing unsupervised methods in efficiency and learning performance in various downstream offline RL tasks of the DeepMind control suite.