Natural Language Reinforcement Learning (2411.14251v1)

Abstract: Reinforcement Learning (RL) mathematically formulates decision-making with Markov Decision Process (MDP). With MDPs, researchers have achieved remarkable breakthroughs across various domains, including games, robotics, and LLMs. This paper seeks a new possibility, Natural Language Reinforcement Learning (NLRL), by extending traditional MDP to natural language-based representation space. Specifically, NLRL innovatively redefines RL principles, including task objectives, policy, value function, BeLLMan equation, and policy iteration, into their language counterparts. With recent advancements in LLMs, NLRL can be practically implemented to achieve RL-like policy and value improvement by either pure prompting or gradient-based training. Experiments over Maze, Breakthrough, and Tic-Tac-Toe games demonstrate the effectiveness, efficiency, and interpretability of the NLRL framework among diverse use cases. Our code will be released at https://github.com/waterhorse1/Natural-language-RL.

Natural Language Reinforcement Learning: A Synthesis of Natural Language Processing and Reinforcement Learning

The paper "Natural Language Reinforcement Learning" introduces an innovative extension to conventional Reinforcement Learning (RL) by incorporating natural language into its framework, thus contributing to a new paradigm known as Natural Language Reinforcement Learning (NLRL). This approach aligns decision-making processes with the expressive and descriptive capabilities of natural language, offering potential advancements in task interpretability and strategic reasoning.

Conceptual Overview

Natural Language Reinforcement Learning reinterprets traditional RL mechanisms like policies, value functions, and the BeLLMan equation through a linguistic lens. This shift requires redefining RL components as language-based constructs. For instance, policies and value functions are evaluated and represented as linguistic narratives or textual feedback, leveraging LLMs due to their proficiency with natural language.

Framework and Implementation

The NLRL framework is delineated into key components, including text-based MDP, language policy, and language value functions. Natural language serves as a medium to encode the state and action spaces, alongside task instructions guiding the policy toward successful outcomes. The researchers articulate that the integration of LLMs is crucial, allowing the exploitation of rich textual data and facilitating the translation of traditional concepts into language equivalents.

• Language Policy and Value Functions: NLRL utilizes LLMs for the generation of action-driven narratives and policy evaluations. These processes aim to parallel the strategic generation of thoughts akin to a human's chain-of-thought reasoning.
• Language BeLLMan Equation: The conventional BeLLMan equation is translated into a form that uses natural language to describe expectations and decisions. This requires sophisticated language aggregators to process and summarize potential future states and consequences of actions.

Practical Applications and Results

The paper provides empirical evidence of NLRL's effectiveness across diverse environments like Maze, Breakthrough, and Tic-Tac-Toe games. These experiments are integral to demonstrating the framework's practical viability, highlighting improvements in policy evaluation and decision-making. Results indicate that NLRL can enhance interpretability and effectiveness without the need for exhaustive data, owing to its capability of synthesizing user-friendly strategic inputs and outputs.

• Effectiveness and Interpretability: A pivotal advantage presented by NLRL is its improved interpretability of decision-making processes, which can traditionally be opaque in scalar reward environments. The framework's reliance on language-based feedback allows for more explicit articulation of reasoning and strategic thought.

Implications and Future Prospects

NLRL presents significant implications for the future of RL and AI in general. The adaptability of this framework suggests viable extrapolation to real-world scenarios where multi-modal feedback — combining text, visuals, and sensory data — is prevalent. Such capability opens avenues for more robust robotics and advanced AI assistants that require nuanced understanding and communication skills.

• Practical Implications: The NLRL framework could revolutionize existing systems by reducing the brittleness observed in traditional RL setups. This paradigm is particularly promising for deployment in domains demanding high interpretability and strategic complexity, such as autonomous driving and human-computer interaction.
• Theoretical Development: Future work might delve into formalizing the theoretic underpinnings of NLRL, ensuring robustness and generalizability of language-derived RL policies. Continued integration with LLM advancements will be instrumental, optimizing the alignment of contextual understanding with action-oriented strategies.

In conclusion, the proposed NLRL framework marks a significant stride towards synthesizing natural language processing with reinforcement learning — facilitating an AI paradigm that is not only computationally sophisticated but also inherently understandable. By effectively translating RL components into linguistic constructs, NLRL paves the way for enhanced AI capabilities that exhibit human-like reasoning and narrative comprehension, fundamentally advancing the scope of autonomous decision-making systems.