The Natural Language of Actions (1902.01119v2)

Abstract: We introduce Act2Vec, a general framework for learning context-based action representation for Reinforcement Learning. Representing actions in a vector space help reinforcement learning algorithms achieve better performance by grouping similar actions and utilizing relations between different actions. We show how prior knowledge of an environment can be extracted from demonstrations and injected into action vector representations that encode natural compatible behavior. We then use these for augmenting state representations as well as improving function approximation of Q-values. We visualize and test action embeddings in three domains including a drawing task, a high dimensional navigation task, and the large action space domain of StarCraft II.

• The paper introduces Act2Vec, a framework that leverages NLP embedding techniques to encode context-based action representations in RL.
• The methodology enhances state representation and Q-value approximations, validated across drawing, navigation, and game domains.
• Experimental results demonstrate semantic organization of actions via PMI, yielding improved exploration in large action spaces.

Insights on Action Representation in Reinforcement Learning

The paper "The Natural Language of Actions" aims to enhance reinforcement learning (RL) through a novel approach centered on action representation. This work introduces Act2Vec, a framework crafted to derive context-based action embeddings within RL domains. The paper presents a macroscopic view contrasting traditional action strategies by focusing on encoding actions into vector spaces, effectively utilizing relationships and similarities among them.

The introduction of Act2Vec is fundamentally inspired by recent advancements in NLP involving word embedding techniques such as Word2Vec and GloVe. The crux of this research lies in leveraging distributed representation theories from NLP, particularly drawing from the skip-gram model with negative sampling (SGNS), to encapsulate the "natural language of actions." This analogy proposes that the context in which an action resides holds critical information capable of optimizing RL tasks, such as transitioning probabilities or capturing latent environment characteristics.

The authors illuminate action embeddings encompassing prior knowledge extracted from demonstrated trajectories. These embeddings facilitate augmenting state representations and improving function approximations of Q-values. Numerical results validate the utility of Act2Vec across three experimental domains: a drawing task, a high-dimensional navigation task, and the complex action space domain of StarCraft II. In each scenario, embeddings reveal semantic relationships between actions, showcasing significant alignment with intuitively understood behaviors and strategies.

A compelling aspect is the effect of representing actions through their context, embodied in the pointwise mutual information (PMI). Using PMI contributes favorable characteristics ensuring that closely related actions in embedding space induce similar environmental outcomes. This property is examined in the drawing and navigation domains where visualizations of action embeddings reflect a semantic organization synonymous with expert understanding. For instance, tasks involving drawing squares illustrate Act2Vec’s impact on optimizing state representation through previous action embeddings, outperforming both one-hot and random embeddings configurations.

The introduction of Act2Vec also extends to function approximation methods, notably with $Q$-Embedding and cluster-based exploration techniques like $k$-Exp. These methods offer substantial improvements for approximating Q-values and efficient exploration in domains with large action spaces. As shown in navigation tasks, sequences of actions provide enriched representations yielding enhanced learning performance.

The theoretical implications of this approach suggest promising avenues for tackling RL challenges related to sparse reward signals, particularly in domains with extensive action spaces and long-time horizons such as StarCraft II. Here, embeddings provide spatially organized representations discerning between different species and strategies, highlighting their significance in action characterization. These embeddings potentially offer practical regularization advantages contributing to smoother agent navigation trajectories.

Future research directions hinted by this paper involve exploring distributional embedding methodologies for other RL components, such as state or reward representations. The potential to refine RL algorithms through contextual embeddings—transforming high-dimensional data into actionable intelligence—presents an appealing prospect.

In summary, "The Natural Language of Actions" provides a substantive contribution to action representation in reinforcement learning, offering significant theoretical and practical implications. Through Act2Vec, the authors have articulated a mechanism where actions become intelligible units in a natural language sense—laying groundwork for future developments harnessing contextual understanding in RL.