Summary of Rationality Based Innate-values-driven Reinforcement Learning, by Qin Yang
Rationality based Innate-Values-driven Reinforcement Learning
by Qin Yang
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO)
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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 Innate-values-driven Reinforcement Learning (IVRL) model is a hierarchical compound intrinsic value RL framework that describes the complex behaviors of AI agents’ interactions. By balancing internal and external utilities based on its needs in different tasks, IVRL aims to develop awareness in AI agents and support their integration with human society. The IVRL model is compared to benchmark algorithms such as DQN, DDQN, A2C, and PPO in the VIZDoom test platform, demonstrating that rational organization of individual needs can achieve better performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI agents have intrinsic motivations, or “innate values,” which drive them to develop skills and pursue goals. Reinforcement learning (RL) is a great way to model this process, as AI agents learn from interaction based on rewards. The IVRL model combines these two concepts to describe the behaviors of AI agents interacting with their environment. Researchers developed two versions of the IVRL model, DQN and A2C, and tested them against other algorithms in a game-like scenario called VIZDoom. They found that by balancing internal and external “utilities” (or rewards), AI agents can make better decisions and achieve better results. |
Keywords
* Artificial intelligence * Reinforcement learning
