Summary of Policy Optimization with Smooth Guidance Learned From State-only Demonstrations, by Guojian Wang et al.
Policy Optimization with Smooth Guidance Learned from State-Only Demonstrations
by Guojian Wang, Faguo Wu, Xiao Zhang, Tianyuan Chen
First submitted to arxiv on: 30 Dec 2023
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 This paper tackles a crucial problem in online deep reinforcement learning (DRL) – the scarcity of reward feedback. Prior approaches have achieved impressive results using offline demonstrations, but these methods require high-quality demonstrations, which can be costly and unrealistic to obtain. The proposed Policy Optimization with Smooth Guidance (POSG) algorithm leverages a small set of state-only demonstrations to indirectly make approximate credit assignments and facilitate exploration. POSG uses a trajectory-importance evaluation mechanism to determine the quality of current trajectories against demonstrations, then computes guidance rewards based on trajectory importance, fusing state distributions with reward information. Theoretical analysis shows performance improvement due to smooth guidance rewards, and a new worst-case lower bound is derived. Experimental results demonstrate POSG’s advantages in control performance and convergence speed across four sparse-reward environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in artificial intelligence – getting machines to learn without enough feedback. Right now, we use demonstrations (like videos) of experts doing tasks, but this can be hard and expensive. The new algorithm, Policy Optimization with Smooth Guidance (POSG), uses these demonstrations to help machines make good choices and explore new things. POSG looks at how well the machine is doing compared to the expert, then uses that information to guide it towards better actions. This makes the machine learn faster and do a better job. The paper shows that this works really well in four different scenarios. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
