Low-level sensory and motor signals in the high-dimensional spaces (e.g., image observations or motor torques) in deep reinforcement learning are complicated to understand or harness for downstream tasks directly. While sensory representations have been widely studied, the representations of actions that form motor skills are yet under exploration. In this work, we find that when a multi-task policy network takes as input states and task embeddings, a space based on the task embeddings emerges to contain meaningful action representations with moderate constraints. Within this space, interpolated or composed embeddings can serve as a high-level interface to instruct the agent to perform meaningful action sequences. Empirical results not only show that the proposed action representations have efficacy for intra-action interpolation and inter-action composition with limited or no learning, but also demonstrate their superior ability in task adaptation to strong baselines in Mujoco locomotion tasks. The evidence elucidates that learning action representations is a promising direction toward efficient, adaptable, and composable RL, forming the basis of abstract action planning and the understanding of motor signal space. Anonymous project page: https://sites.google.com/view/emergent-action-representation