GitBook: [master] 62 pages modified

incremental-learning-1.md

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 # Reinforcement Learning
 
-1. vidhya on [Getting ready for AI based gaming agents – Overview of Open Source Reinforcement Learning Platforms](https://www.analyticsvidhya.com/blog/2016/12/getting-ready-for-ai-based-gaming-agents-overview-of-open-source-reinforcement-learning-platforms/)
-2. vidhya on [Simple Beginner’s guide to Reinforcement Learning & its implementation](https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/)
+1. Vidhya on [Getting ready for AI based gaming agents – Overview of Open Source Reinforcement Learning Platforms](https://www.analyticsvidhya.com/blog/2016/12/getting-ready-for-ai-based-gaming-agents-overview-of-open-source-reinforcement-learning-platforms/)
+2. Vidhya on [Simple Beginner’s guide to Reinforcement Learning & its implementation](https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/)
 3. ZipRecruiter on [Classifying Job Titles With Noisy Labels Using REINFORCE ](https://medium.com/@ziprecruiter.engineering/classifying-job-titles-with-noisy-labels-using-reinforce-ce1a4bde05e2)- Fine-grained job title classification with noisy labels using the REINFORCE algorithm and multi-task learning
    1. this article has a very nice trick in adding a reward component to the loss function in order to mitigate for unbalanced class label problem, instead of the usual balancing.
 
@@ -12,8 +12,8 @@
 * **Returns optimal policy.**
 * **Refer to youtube** [**here**](https://www.youtube.com/watch?v=9m_6q_KECTk)
 
-### **RL IN DL**
-
-[**A review paper about RL in DL**](https://arxiv.org/pdf/1701.07274.pdf)  
+### **Deep Learning**
 
+1. [**A review paper about RL in DL**](https://arxiv.org/pdf/1701.07274.pdf)
+2. [deep Q-learning](https://www.analyticsvidhya.com/blog/2019/04/introduction-deep-q-learning-python/)