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Source note3 min read

Learning Process Rewards via Success Visitation Matching for Efficient RL

Source

arXiv

arxiv.org/abs/2606.23640v1

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Source

Plain-Language Brief

Researchers propose a method to make reinforcement learning (RL) more effective when dealing with sparse rewards, which are typically given only when a task is completed. The method incentivizes the RL-learned policy to match the state-action visitations of successful episodes, while avoiding those of unsuccessful ones, by training a discriminator to distinguish between these two types of episodes.

What the Document Says

  • The paper focuses on solving the credit assignment problem in reinforcement learning (RL) when dealing with sparse rewards.
  • The proposed approach is based on transforming a sparse outcome reward into a dense process reward.
  • This transformation relies on training a discriminator to distinguish between successful and unsuccessful episodes.
  • The policy is incentivized to match the state-action visitations of successful episodes, while avoiding those of unsuccessful ones.
  • By incentivizing the policy to match the visitations over all states, this reward provides dense feedback on whether progress is being made towards task completion.
  • The paper demonstrates that this method leads to faster RL finetuning performance on simulated and real-world manipulation tasks compared to maximizing the sparse outcome reward.

Key Concepts Mentioned

  • reinforcement learning
  • credit assignment problem
  • sparse rewards
  • process reward
  • discriminator
  • state-action visitations

Claims Made by the Document

  • The proposed method achieves faster RL performance when dealing with sparse rewards.
  • By incentivizing the policy to match state-action visitations of successful episodes, dense feedback is provided on progress towards task completion.

Evidence Used

  • Improved RL finetuning performance demonstrated on simulated and real-world manipulation tasks

Methods / Approach

  • Transforming sparse outcome rewards into dense process rewards by training a discriminator to distinguish between successful and unsuccessful episodes.
  • Incentivizing the policy to match the state-action visitations of successful episodes, while avoiding those of unsuccessful ones.

Limitations Stated by the Authors

  • The paper focuses specifically on finetuning of robotic control policies.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The method might not be as effective for other types of tasks or policies.
  • The long-term impact and generalizability of the method is not discussed in the paper.

Open Questions

  • How does this method perform compared to other methods for dealing with sparse rewards?
  • What are the potential limitations or drawbacks of this method?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper does not provide a direct comparison with other methods for dealing with sparse rewards.
  • It's unclear if the method has any specific disadvantages.

Raw Abstract / Excerpt

In many modern applications of reinforcement learning (RL), the natural reward for a task of interest is inherently sparse: a reward of 0 is given everywhere except when the task is completed, when a reward of +1 is given. Training a policy to maximize such a sparse reward requires solving a challenging credit assignment problem, leading to slow or ineffective RL improvement. We propose a simple approach to transform a sparse outcome reward into a dense process reward. Our approach relies on training a discriminator to distinguish between previous successful and unsuccessful episodes, and using this discriminator to incentivize the RL-learned policy to match the state-action visitations of successful episodes, while avoiding those of unsuccessful episodes. By incentivizing the policy to match the visitations over all states, not just those that correspond to task success, this reward provides dense feedback on whether progress is being made towards task completion, and, we show, provably achieves this without changing the optimal policy. Focusing on finetuning of robotic control policies, we demonstrate that our approach leads to significantly faster RL finetuning performance on both simulated and real-world manipulation tasks, as compared to simply maximizing the sparse outcome reward.