Constraints as Rewards: Reinforcement Learning for Robots without Reward Functions (2501.04228v2)

Abstract: Reinforcement learning has become an essential algorithm for generating complex robotic behaviors. However, to learn such behaviors, it is necessary to design a reward function that describes the task, which often consists of multiple objectives that needs to be balanced. This tuning process is known as reward engineering and typically involves extensive trial-and-error. In this paper, to avoid this trial-and-error process, we propose the concept of Constraints as Rewards (CaR). CaR formulates the task objective using multiple constraint functions instead of a reward function and solves a reinforcement learning problem with constraints using the Lagrangian-method. By adopting this approach, different objectives are automatically balanced, because Lagrange multipliers serves as the weights among the objectives. In addition, we will demonstrate that constraints, expressed as inequalities, provide an intuitive interpretation of the optimization target designed for the task. We apply the proposed method to the standing-up motion generation task of a six-wheeled-telescopic-legged robot and demonstrate that the proposed method successfully acquires the target behavior, even though it is challenging to learn with manually designed reward functions.

• The paper introduces a novel framework that replaces traditional reward functions with dynamic constraint functions to balance objectives.
• It leverages Lagrange multipliers to automatically tune multiple objectives, reducing manual tuning in reinforcement learning.
• Experimental results on a six-wheeled telescopic-legged robot demonstrate robust performance in achieving a standing-up task.

Constraints as Rewards in Reinforcement Learning: An Evaluation

In the field of robotics, reinforcement learning (RL) has established itself as a critical method for developing sophisticated behaviors. The success of RL hinges on the design of a reward function that effectively encapsulates the objectives of a desired task. Crafting such a function is non-trivial, frequently requiring multiple iterations in a process known as reward engineering. The paper in discussion introduces an innovative approach aimed at mitigating the complexity associated with reward engineering by proposing the framework of Constraints as Rewards (CaR).

The central thesis of this work is the recognition of the inherent challenges in the iterative tuning of reward functions when various objectives must be balanced. Traditional RL methods can be cumbersome due to the manual effort needed to define and refine these functions. Instead of a singular reward function, the proposed CaR framework utilizes constraint functions to express task objectives, thereby transforming the RL problem into an optimization challenge with constraints. This novel method leverages the Lagrangian approach, wherein Lagrange multipliers are employed to dynamically balance multiple objectives.

The distinct contributions of CaR are twofold. First, it offers a systematic mechanism to balance diverse objectives inherently, bypassing the typical trial-and-error method of reward tuning. Second, constraints formulated as inequalities grant a clear, intuitive understanding of the optimization goals, which enhances the design of task objectives. These advantages are evidenced through the application to a robotic motion task—specifically, achieving the standing-up motion with a six-wheeled telescopic-legged robot. The results suggest that CaR adeptly navigates complex behavioral tasks, which can often be resistive to manual reward function design.

Quantitative assessments from the experimentation illustrate that the CaR method effectively achieved the desired behavior, showcasing its robustness in scenarios where traditional reward functions fall short. The reliance on Lagrange multipliers as implicit weights marks a significant advancement in automatic objective balancing within the RL domain.

The implications of this research span both practical and theoretical domains. Practically, it presents a promising approach for the broader field of robotics, particularly in automating the development of task behaviors that are otherwise difficult to specify. Theoretically, it proposes a shift in how RL problems might be formulated, potentially influencing future methodologies that emphasize constraints over rewards.

Future exploration might delve into the scalability of the CaR framework with increasingly complex robotics tasks, as well as its adaptation to domains beyond robotics. The broader adoption of constraint-based formulations could stimulate a new wave of RL strategies that capitalize on the innate human understanding of objectives framed as constraints. This research opens avenues for the simplified integration of multiple competing objectives within automated learning systems, positioning CaR as a compelling construct in the development of intelligent agents.