Automated Reward Design for Reinforcement Learning via Coding Large Language Models

Core Concepts

EUREKA, a universal reward design algorithm powered by coding large language models and in-context evolutionary search, can generate human-level reward functions without any task-specific prompting or reward templates.

Abstract

The paper presents EUREKA, a novel reward design algorithm that leverages the capabilities of large language models (LLMs) to automatically generate effective reward functions for reinforcement learning (RL) tasks. Key highlights: EUREKA takes the environment source code as context and uses the coding abilities of LLMs like GPT-4 to zero-shot generate executable reward functions. EUREKA then performs an iterative evolutionary search to progressively improve the generated rewards, guided by a reward reflection mechanism that provides targeted feedback on the reward quality. Without any task-specific prompting or pre-defined reward templates, EUREKA outperforms expert human-engineered rewards on 83% of the 29 RL environments tested, leading to an average normalized improvement of 52%. EUREKA's generality enables a new gradient-free approach to reinforcement learning from human feedback (RLHF), readily incorporating human inputs to improve the quality and safety of the generated rewards. Combining EUREKA with curriculum learning, the paper demonstrates for the first time a simulated Shadow Hand capable of performing rapid pen spinning tricks, a highly dexterous manipulation skill. The core innovation of EUREKA is its ability to leverage the remarkable code generation, zero-shot learning, and in-context improvement capabilities of modern LLMs to tackle the long-standing challenge of reward design in RL. By automating this crucial step, EUREKA paves the way for more scalable and accessible RL systems.

Stats

The paper reports the following key metrics: EUREKA outperforms human-engineered rewards on 83% of the 29 RL environments tested. EUREKA achieves an average normalized improvement of 52% over human rewards across the benchmark. Using EUREKA rewards, a simulated Shadow Hand is able to perform rapid pen spinning tricks for the first time.

Quotes

"Without any task-specific prompting or reward templates, EUREKA autonomously generates rewards that outperform expert human rewards on 83% of the tasks and realizes an average normalized improvement of 52%." "Combining EUREKA with curriculum learning, we demonstrate for the first time rapid pen spinning maneuvers on a simulated anthropomorphic Shadow Hand." "EUREKA can readily benefit from and improve upon existing human reward functions. Likewise, we showcase EUREKA's ability to use human textual feedback to co-pilot reward function designs that capture the nuanced human preferences in agent behavior."

Key Insights Distilled From

Eureka: Human-Level Reward Design via Coding Large Language Models

by Yecheng Jaso... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2310.12931.pdf

Deeper Inquiries

How can EUREKA's reward generation capabilities be further extended to handle more complex and open-ended tasks beyond the current benchmark?

EUREKA's reward generation capabilities can be extended to handle more complex and open-ended tasks by incorporating additional sources of information and feedback. One approach could involve integrating human feedback in a more interactive manner throughout the reward generation process. By allowing human experts to provide real-time feedback on the generated rewards and iteratively refining them based on this feedback, EUREKA can adapt to a wider range of tasks and nuances that may not be captured by the initial prompts alone. Furthermore, EUREKA can benefit from incorporating multi-objective optimization techniques to handle tasks with conflicting objectives or multiple desired outcomes. By optimizing for a combination of different reward criteria, EUREKA can generate more robust and versatile reward functions that cater to a broader spectrum of tasks. Additionally, leveraging transfer learning techniques can enhance EUREKA's ability to generalize across tasks and domains. By pre-training on a diverse set of tasks and environments, EUREKA can learn more generalized reward design principles that can be fine-tuned for specific tasks, enabling it to tackle a wider range of challenges effectively.

What are the potential limitations or failure modes of EUREKA's evolutionary search approach, and how can they be addressed?

One potential limitation of EUREKA's evolutionary search approach is the risk of getting stuck in local optima, especially in high-dimensional or complex search spaces. To address this, EUREKA can benefit from incorporating diversity maintenance strategies, such as introducing mutation operators that encourage exploration of different regions of the search space. By promoting diversity in the generated reward functions, EUREKA can avoid premature convergence to suboptimal solutions. Another potential limitation is the computational complexity of the evolutionary search process, especially when dealing with a large number of reward candidates and iterations. To mitigate this, EUREKA can leverage parallelization techniques and distributed computing resources to speed up the search process and explore a larger space of reward functions efficiently. Additionally, EUREKA's evolutionary search approach may struggle with tasks that have sparse or deceptive reward landscapes, where the true optimal reward function is challenging to discover. To address this, EUREKA can incorporate techniques from novelty search or novelty-based optimization to encourage exploration of novel and unexplored regions of the reward space, potentially leading to the discovery of more effective reward functions.

How can the insights from EUREKA's reward reflection mechanism be generalized to other areas of reinforcement learning, such as reward shaping or inverse reinforcement learning?

The insights from EUREKA's reward reflection mechanism can be generalized to other areas of reinforcement learning by providing a structured and interpretable way to analyze and improve reward functions. In the context of reward shaping, EUREKA's reward reflection can be used to identify and modify specific reward components that may need adjustment to provide more informative and effective learning signals to the agent. By leveraging the feedback from the reward reflection, researchers can iteratively refine the shaped rewards to better guide the learning process. In the case of inverse reinforcement learning (IRL), EUREKA's reward reflection can be instrumental in understanding the underlying dynamics of expert behavior and translating them into interpretable reward functions. By analyzing the policy feedback and identifying patterns in the reward components, EUREKA can assist in inferring the latent reward structure from expert demonstrations more effectively. This can lead to more accurate and human-aligned reward functions in IRL settings, improving the agent's ability to learn from demonstrations and generalize to new tasks.