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Coinrun.md

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+## Quantifying Generalization in Reinforcement Learning
+> Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman
+
+### Abstract
+In this paper, we investigate the problem of overfitting in deep reinforcement learning. 
+
+Among the most common benchmarks in RL, it is customary to use the same environments for both training and testing. 
+
+This practice offers relatively little insight into an agent’s ability to generalize. 
+
+We address this issue by using procedurally generated environments to construct distinct training and test sets. 
+
+Most notably, we introduce a new environment called CoinRun, designed as a benchmark for generalization in RL. 
+
+Using CoinRun, we find that agents overfit to surprisingly large training sets. 
+
+We then show that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization.