Summary of Adaptive Primal-dual Method For Safe Reinforcement Learning, by Weiqin Chen et al.
Adaptive Primal-Dual Method for Safe Reinforcement Learning
by Weiqin Chen, James Onyejizu, Long Vu, Lan Hoang, Dharmashankar Subramanian, Koushik Kar, Sandipan Mishra, Santiago Paternain
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed adaptive primal-dual (APD) method for Safe Reinforcement Learning (SRL) optimizes policy in each iteration by adjusting two adaptive learning rates (LRS) to Lagrangian multipliers. The APD algorithm theoretically converges, optimizes, and is feasible, outperforming constant LR cases in four well-known environments using PPO-Lagrangian and DDPG-Lagrangian algorithms. The robustness of selecting the two adaptive LRs is empirically supported. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Safely learning how to make good choices can be tricky. A new way to solve this problem, called Adaptive Primal-Dual (APD), adjusts two special numbers (learning rates) based on other important numbers (Lagrangian multipliers). This helps the algorithm learn and improve faster. The researchers tested APD with four different scenarios and found it worked better than a simpler approach. This new method can help create safer AI systems. |
Keywords
* Artificial intelligence * Reinforcement learning
