Summary of Potential-based Intrinsic Motivation: Preserving Optimality with Complex, Non-markovian Shaping Rewards, by Grant C. Forbes et al.
Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards
by Grant C. Forbes, Leonardo Villalobos-Arias, Jianxun Wang, Arnav Jhala, David L. Roberts
First submitted to arxiv on: 16 Oct 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 This abstract presents a solution to mitigate the risks associated with intrinsic motivation (IM) reward-shaping methods in complex environments. Recent advancements in IM have led to the development of various reward-shaping methods, which can inadvertently alter optimal policies, resulting in suboptimal behavior. The authors propose an extension to potential-based reward shaping (PBRS), a method previously shown to mitigate risks, but only applicable to simple reward functions. They introduce Potential-Based Intrinsic Motivation (PBIM) and Generalized Reward Matching (GRM), methods converting IM rewards into potential-based forms that preserve optimal policies. Experimental results on MiniGrid environments demonstrate the effectiveness of PBIM and GRM in preventing suboptimal policies and speeding up training. The authors also provide proofs, experimental results, and discussion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with learning in complex environments. Some methods for making agents learn faster can actually make them do worse. The authors found a way to fix this by creating new methods that keep the agent from getting stuck in bad behaviors. They tested these methods on some games and showed that they work well. This is important because it helps us understand how to make agents learn better without making them do worse. |
