Summary of Zero-sum Positional Differential Games As a Framework For Robust Reinforcement Learning: Deep Q-learning Approach, by Anton Plaksin and Vitaly Kalev
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach
by Anton Plaksin, Vitaly Kalev
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Systems and Control (eess.SY); Optimization and Control (math.OC)
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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 Robust Reinforcement Learning (RRL), a paradigm that trains models robust to uncertainty or disturbances for real-world applications. By viewing uncertainty as an adversarial agent, the problem is reduced to finding policies robust to any opponent’s actions. The authors use positional differential game theory to develop a centralized Q-learning approach and prove that the same Q-function can be used to solve both minimax and maximin Bellman equations under Isaacs’ condition. This leads to the development of Isaacs Deep Q-Network algorithms, which outperform baseline RRL and Multi-Agent RL algorithms in various environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Robust Reinforcement Learning is a new way to make machines learn better for real-world situations. Imagine you’re playing a game with someone who is trying to trick you – you need to be good at reacting to what they do, so you can stay ahead. This paper shows how we can use this idea to train machines that are super good at handling unexpected things or surprises. They used special math to make it work and created new algorithms that did better than other approaches in different situations. |
Keywords
» Artificial intelligence » Reinforcement learning
