Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models (1902.08858v2)

Abstract: Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge. Common practice has been to use handcrafted dialog acts, or the output vocabulary, e.g. in neural encoder decoders, as the action spaces. Both have their own limitations. This paper proposes a novel latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods in order to induce its own action space from the data. Comprehensive experiments are conducted examining both continuous and discrete action types and two different optimization methods based on stochastic variational inference. Results show that the proposed latent actions achieve superior empirical performance improvement over previous word-level policy gradient methods on both DealOrNoDeal and MultiWoz dialogs. Our detailed analysis also provides insights about various latent variable approaches for policy learning and can serve as a foundation for developing better latent actions in future research.

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models

The paper "Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models" by Zhao et al. introduces a significant approach to overcoming existing challenges in conversational agent design, specifically for end-to-end dialog systems. The authors propose a latent action framework that leverages unsupervised methods to define action spaces through latent variables, rather than adhering to pre-defined dialog acts or word-level actions. This representation enables more effective policy learning in reinforcement learning (RL) environments.

The research explores the intricacies of dialog models by evaluating traditional action spaces—either handcrafted or directly learned from vocabularies—highlighting their limitations in handling complex dialog dynamics due to sub-optimal convergence and language degeneration. The proposed Latent Action Reinforcement Learning (LaRL) framework aims to provide a more structured and modular response generation by decoupling dialog strategies from language generation.

Key to this framework are the induced latent action spaces, which are evaluated in both continuous and discrete settings. This approach capitalizes on stochastic variational inference and introduces novel optimization objectives, namely a Lite variant of the Evidence Lower Bound (ELBO) that is shown to mitigate exposure bias. The empirical evaluation across datasets like DealOrNoDeal and MultiWoz demonstrates substantial improvements over existing word-level RL baselines, notably achieving an 18.2% improvement in success rate on the MultiWoz dataset compared to the state-of-the-art.

The implications of this research are twofold. Firstly, it offers a new pathway for creating more adaptive and responsive dialog agents, capable of navigating diverse conversational scenarios independently without requiring prior domain-specific annotations. Secondly, it opens the door for broader theoretical advancements in RL, as latent variable models offer simplified action spaces that can potentially enhance learning efficiency and policy optimization.

Future work might explore the integration of these latent action frameworks in broader dialog applications and test their scalability across richer dialog domains. Moreover, further exploration into the balance of discrete versus continuous latent representations could yield more insights into optimal configurations for specific dialog systems. Overall, this research contributes to the ongoing refinement of dialog agents by revolutionizing the underlying action representation techniques within the reinforcement learning paradigm.