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The Value of Dense Reward Learning from Sparse Rewards
Challenges in Reward Design
The success of reinforcement learning (RL) techniques often depends on dense reward functions, but designing them can be challenging and require expertise. Sparse rewards, on the other hand, are easier to obtain but can pose challenges for RL algorithms.
Proposed Solution: DrS Model
Researchers from UC San Diego present Dense reward learning from Stages (DrS), a unique approach to learning reusable rewards by incorporating sparse rewards as a supervision signal. This model offers a practical solution for transferring learned rewards across tasks with varying object geometries, simplifying the reward design process for RL applications.
Key Phases of DrS Model
The DrS model consists of two phases: Reward Learning and Reward Reuse. In the Reward Learning phase, a classifier is trained to differentiate between successful and unsuccessful trajectories using sparse rewards, serving as a dense reward generator. The Reward Reuse phase applies the learned dense reward to train new RL agents in test tasks, ensuring effective guidance through task progression.
Evaluation Results
The proposed model was evaluated on challenging physical manipulation tasks, demonstrating the reusability of learned rewards and outperforming baseline rewards across all task families. Results showcased the effectiveness of DrS in transferring across tasks with varying object geometries, holding promise for scaling up RL applications in diverse scenarios.
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