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Text2Reward: Reward Shaping with Language Models for Reinforcement Learning (2309.11489v3)

Published 20 Sep 2023 in cs.LG, cs.AI, cs.CL, and cs.RO

Abstract: Designing reward functions is a longstanding challenge in reinforcement learning (RL); it requires specialized knowledge or domain data, leading to high costs for development. To address this, we introduce Text2Reward, a data-free framework that automates the generation and shaping of dense reward functions based on LLMs. Given a goal described in natural language, Text2Reward generates shaped dense reward functions as an executable program grounded in a compact representation of the environment. Unlike inverse RL and recent work that uses LLMs to write sparse reward codes or unshaped dense rewards with a constant function across timesteps, Text2Reward produces interpretable, free-form dense reward codes that cover a wide range of tasks, utilize existing packages, and allow iterative refinement with human feedback. We evaluate Text2Reward on two robotic manipulation benchmarks (ManiSkill2, MetaWorld) and two locomotion environments of MuJoCo. On 13 of the 17 manipulation tasks, policies trained with generated reward codes achieve similar or better task success rates and convergence speed than expert-written reward codes. For locomotion tasks, our method learns six novel locomotion behaviors with a success rate exceeding 94%. Furthermore, we show that the policies trained in the simulator with our method can be deployed in the real world. Finally, Text2Reward further improves the policies by refining their reward functions with human feedback. Video results are available at https://text-to-reward.github.io/ .

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Essay on "Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning"

The paper "Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning" introduces a novel approach to reward shaping in reinforcement learning (RL) by leveraging LLMs for automated dense reward function generation. This method addresses the challenge of crafting reward functions, traditionally requiring domain expertise and being a labor-intensive process, by utilizing the knowledge embedded in LLMs. The proposed framework, Text2Reward, not only provides interpretable and scalable reward functions but also enables iterative refinement through human feedback, offering significant advantages over existing methods such as inverse reinforcement learning (IRL) and preference learning.

Framework and Methodology

The Text2Reward framework enables the automatic generation of dense reward functions from natural language goal descriptions, grounded on a compact, Pythonic representation of the environment. The dense reward codes generated are used with RL algorithms like PPO and SAC to train agent policies. The process involves:

  1. Instruction and Environment Abstraction: Text2Reward generates reward codes by analyzing a natural language description of the task alongside a Pythonic representation of the environment. This representation employs classes and attributes to encapsulate the state of the environment succinctly.
  2. Background Knowledge and Few-shot Learning: Incorporating additional knowledge and few-shot examples aids in the generation of reward functions. The inclusion of handy functions and retrieved instruction-reward pairs refines the LLM's output and facilitates adaptation to various tasks and environments.
  3. Iterative Refinement with Human Feedback: Post initial policy execution, human feedback is solicited to further refine the reward functions. The framework enables continuous improvement of the generated code based on user preferences and observation of execution videos.

Empirical Evaluation

The authors conducted systematic evaluations across multiple robotic manipulation benchmarks, namely ManiSkill2, MetaWorld, and Gym MuJoCo, demonstrating several salient achievements of Text2Reward:

  • Performance and Flexibility: On manipulation tasks, Text2Reward either matched or surpassed the performance of expertly crafted reward functions in most cases. Successfully training policies indicate that the method can accommodate a diverse set of manipulation and locomotion tasks with minimal to zero tuning.
  • Generative and Generalizable: Notably, the framework achieved a greater than 94% success rate on novel locomotion tasks under the Gym MuJoCo environments, revealing its robust generalization capabilities in generating dense reward functions for diverse applications.
  • Real-world Applicability: The feasibility of policies trained in a simulator being deployed directly to a real-world robot, especially highlighted in the tasks executed on a Franka Panda robot arm, demonstrates the practical applicability of Text2Reward.

Key Insights and Future Prospects

The proposed methodology underscores the potential of leveraging LLMs to facilitate and streamline complex tasks in RL, especially reward crafting which traditionally necessitates expert intuition. The framework reflects a significant shift towards automated methodologies, reducing the overhead in RL environment setup and enabling seamless integration with existing RL paradigms.

Text2Reward's capacity for human-in-the-loop iterations presents opportunities for bridging gaps between abstract task intentions and practical robot behaviors. The integration of human feedback not only helps improve policy success rates but also resolves task ambiguities through interactive learning.

Future work could explore more extensive applications across varied domains, beyond robotic manipulation and locomotion, by utilizing the flexible nature of text-based reward generation. Moreover, advancing the integration of perceptual tasks, alongside refining the combination of symbolic and neural reward models, could escalate the applicability of this approach across more complex and real-world tasks.

Overall, Text2Reward positions itself as a versatile framework demonstrating that LLMs can effectively contribute to creating dense, interpretative reward functions, offering an innovative avenue for future reinforcement learning research and applications.

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Authors (8)
  1. Tianbao Xie (22 papers)
  2. Siheng Zhao (9 papers)
  3. Chen Henry Wu (17 papers)
  4. Yitao Liu (10 papers)
  5. Qian Luo (13 papers)
  6. Victor Zhong (25 papers)
  7. Yanchao Yang (41 papers)
  8. Tao Yu (282 papers)
Citations (20)
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