  Despite recent progress in reinforcement learning (RL) from raw pixel data,
sample inefficiency continues to present a substantial obstacle. Prior works
have attempted to address this challenge by creating self-supervised auxiliary
tasks, aiming to enrich the agent's learned representations with
control-relevant information for future state prediction. However, these
objectives are often insufficient to learn representations that can represent
the optimal policy or value function, and they often consider tasks with small,
abstract discrete action spaces and thus overlook the importance of action
representation learning in continuous control. In this paper, we introduce
TACO: Temporal Action-driven Contrastive Learning, a simple yet powerful
temporal contrastive learning approach that facilitates the concurrent
acquisition of latent state and action representations for agents. TACO
simultaneously learns a state and an action representation by optimizing the
mutual information between representations of current states paired with action
sequences and representations of the corresponding future states.
Theoretically, TACO can be shown to learn state and action representations that
encompass sufficient information for control, thereby improving sample
efficiency. For online RL, TACO achieves 40% performance boost after one
million environment interaction steps on average across nine challenging visual
continuous control tasks from Deepmind Control Suite. In addition, we show that
TACO can also serve as a plug-and-play module adding to existing offline visual
RL methods to establish the new state-of-the-art performance for offline visual
RL across offline datasets with varying quality.
