Summary of Social Interpretable Reinforcement Learning, by Leonardo Lucio Custode et al.
Social Interpretable Reinforcement Learning
by Leonardo Lucio Custode, Giovanni Iacca
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 proposes Social Interpretable Reinforcement Learning (SIRL), a novel approach that can significantly reduce the training cost of interpretable reinforcement learning models. SIRL mimics a social learning process where agents learn from individual experience and peer-to-peer interactions. The method consists of two phases: collaborative and individual. In the collaborative phase, agents interact with a shared environment and vote on actions; in the individual phase, each agent refines its performance by interacting with its own environment. SIRL reduces computational cost by up to 76% while improving convergence speed and solution quality on six widely-known RL benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SIRL is a new way for computers to learn from experience and work together. It’s like how humans learn from each other! The method has two parts: first, all the agents work together in one place to make decisions. Then, each agent practices its own skills by itself. This helps the agents learn faster and better without using too much computer power. |
Keywords
* Artificial intelligence * Reinforcement learning
