Summary of Trajectory-oriented Policy Optimization with Sparse Rewards, by Guojian Wang et al.
Trajectory-Oriented Policy Optimization with Sparse Rewards
by Guojian Wang, Faguo Wu, Xiao Zhang
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach leverages offline demonstration trajectories to accelerate online reinforcement learning in environments with scarce rewards. By treating offline demonstrations as guidance, rather than mere imitation, the method learns a policy whose distribution of state-action visitation matches that of offline demonstrations. The key innovation is a novel trajectory distance metric based on maximum mean discrepancy (MMD), which enables policy optimization as a distance-constrained problem. This problem is then streamlined into a policy-gradient algorithm, incorporating rewards shaped by insights from offline demonstrations. Experimental results demonstrate the superiority of this approach over baseline methods in various exploration scenarios and optimal policy acquisition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us figure out how to teach machines to learn and make decisions when there’s not much reward or feedback. Right now, most machine learning algorithms struggle to find a good way to explore and learn in these situations. The idea is to use “demonstration” trajectories from experts to guide the machine learning process. This approach can help the machine learn faster and more efficiently. The researchers created a new distance metric that helps them optimize the policy, or decision-making process, based on this expert guidance. They tested their method with many examples and found it outperforms other approaches in finding good policies. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Reinforcement learning
