DrS: Learning Reusable Dense Rewards for Multi-Stage Tasks (2404.16779v1)

Abstract: The success of many RL techniques heavily relies on human-engineered dense rewards, which typically demand substantial domain expertise and extensive trial and error. In our work, we propose DrS (Dense reward learning from Stages), a novel approach for learning reusable dense rewards for multi-stage tasks in a data-driven manner. By leveraging the stage structures of the task, DrS learns a high-quality dense reward from sparse rewards and demonstrations if given. The learned rewards can be \textit{reused} in unseen tasks, thus reducing the human effort for reward engineering. Extensive experiments on three physical robot manipulation task families with 1000+ task variants demonstrate that our learned rewards can be reused in unseen tasks, resulting in improved performance and sample efficiency of RL algorithms. The learned rewards even achieve comparable performance to human-engineered rewards on some tasks. See our project page (https://sites.google.com/view/iclr24drs) for more details.

Dense Reward Learning from Stages (DrS): A Novel Approach for Reusable Dense Reward Functions in Multi-Stage Tasks

Overview

In the domain of Reinforcement Learning (RL), specifically regarding reward acquisition strategies, the novel approach dubbed "Dense reward learning from Stages" (DrS) proposes a method for learning and utilizing dense rewards from multi-stage task structures. The DrS model leverages sparse rewards, potentially supplemented by demonstrations, to generate a refined dense reward signal capable of being repurposed in unseen tasks, which addresses significant challenges in reward engineering.

Methodology

Dense Reward Learning

DrS innovates by categorizing RL tasks into stages and utilizing associated stage-wise discriminators to discern between successful and failed stage completions. Rewards are then assigned in a manner where transitions closer to success are rewarded higher compared to those closer to failures. This learning is optimized through:

• Formulating the learning phase to differentiate between successful and unsuccessful trajectory segments relative to task stages.
• Leveraging a discriminator for each stage in multi-stage tasks, which classifies trajectory segments as successful or failed based on sparse rewards.
• Applying off-policy methods alongside modern algorithms like Soft Actor-Critic to improve sample efficiency during training.

Reward Reusability

A standout feature of DrS is its focus on reward reusability across task variations within the same family, significantly lowering the need for new reward formulations when faced with new task instances. The approach's modular nature allows the learned dense reward function to be efficiently applied to various tasks sharing similar structures, thereby enhancing the generalizability and applicability of the method.

Experimentation

Task Families and Benchmarks

Experiments conducted on the ManiSkill benchmark encompass three challenging physical manipulation task families:

1. Pick-and-Place: Involves relocating an object to a designated position.
2. Turn Faucet: Requires turning a faucet handle to a specific angle.
3. Open Cabinet Door: Entails opening a cabinet door to a prescribed degree.

Across these task families, over 1000 task variants were tested to substantiate the reusable nature and efficacy of the learned dense rewards.

Baselines and Evaluation

DrS's performance was rigorously evaluated against several baselines including human-engineered rewards, semi-sparse rewards, and other reward learning methods like VICE-RAQ and ORIL. The results indicated that DrS not only surpasses other automated reward methods in terms of performance and sample efficiency but also closely matches or even exceeds the efficiency of expert-designed rewards in certain tasks.

Implications and Future Directions

Practical Advantages

The practical implications of DrS are profound, primarily its ability to drastically cut down the labor-intensive and expertise-driven process of reward engineering. This makes it an attractive proposition for deploying RL in real-world settings where reward engineering can be a bottleneck.

Theoretical Contributions

From a theoretical standpoint, DrS contributes to the understanding of reward shaping in multi-stage tasks and extends the functionality of discriminators in RL beyond traditional usages, proposing a novel way to maintain the utility of discriminators across different stages of task execution.

Speculations on Future Developments

Looking forward, the modular and extensible nature of DrS suggests its potential applicability in more complex scenarios, such as dynamically changing environments or tasks with more granular multi-stage structures. Further, integration with other machine learning paradigms, like unsupervised learning for automatic stage discovery, could enhance the autonomy and efficiency of the system.

Conclusion

DrS stands out as a significant contribution to the field of reinforcement learning, especially in the field of automated, efficient, and reusable reward system design. Its capability to reduce reliance on human input for reward system setup and its adaptability to a range of tasks portend well for broader applications in AI-driven systems.