Summary of Causal Coordinated Concurrent Reinforcement Learning, by Tim Tse et al.
Causal Coordinated Concurrent Reinforcement Learning
by Tim Tse, Isaac Chan, Zhitang Chen
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 an algorithmic framework for data sharing and coordinated exploration under a concurrent reinforcement learning (CRL) setting, allowing agents to learn more data-efficient and better performing policies. The approach relaxes the assumption of identical environments, instead considering individual variations within a shared global structure. The authors leverage a causal inference algorithm, Additive Noise Model – Mixture Model (ANM-MM), to extract model parameters governing differentials via independence enforcement. A new data sharing scheme is proposed based on similarity measures of extracted model parameters, demonstrating superior learning speeds on autoregressive, pendulum, and cart-pole swing-up tasks. The framework integrates causal inference with reinforcement learning (RL) in a non-identical environment setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists created a new way for machines to share data and work together better. They wanted to see how different agents could learn and make decisions when they were all trying to accomplish the same thing, but each had its own unique situation. To figure this out, they used a special kind of math called causal inference. This helped them understand what made each agent’s environment different. Then, they came up with a new way for the agents to share their knowledge and work together more effectively. They tested this approach on some simple tasks and found that it worked really well. |
Keywords
* Artificial intelligence * Autoregressive * Inference * Mixture model * Reinforcement learning
