diff --git a/README.md b/README.md
index eb23435..d9817c2 100644
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@ Any suggestions and pull requests are welcome.
 
 ## All Papers
   * [Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning](http://arxiv.org/abs/1509.08731), S. Mohamed and D. J. Rezende, *arXiv*, 2015.
-  * [Deep reinforcement learning with double q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning with Double Q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
   * [Continuous control with deep reinforcement learning](http://arxiv.org/abs/1509.02971), T. P. Lillicrap et al., *arXiv*, 2015.
   * [Language Understanding for Text-based Games Using Deep Reinforcement Learning](http://people.csail.mit.edu/karthikn/pdfs/mud-play15.pdf), K. Narasimhan et al., *EMNLP*, 2015.
   * [Giraffe: Using Deep Reinforcement Learning to Play Chess](http://arxiv.org/abs/1509.01549), M. Lai, *arXiv*, 2015.
@@ -33,11 +33,11 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Massively Parallel Methods for Deep Reinforcement Learning](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Publications_files/gorila.pdf), A. Nair et al., *ICML Workshop*, 2015.
   * [Trust Region Policy Optimization](http://jmlr.org/proceedings/papers/v37/schulman15.pdf), J. Schulman et al., *ICML*, 2015.
   * [Human-level control through deep reinforcement learning](http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf), V. Mnih et al., *Nature*, 2015.
-  * [Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
+  * [Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
   * [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), V. Mnih et al., *NIPS Workshop*, 2013.
 
 ## Q-learning
-  * [Deep reinforcement learning with double q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning with Double Q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
   * [Continuous control with deep reinforcement learning](http://arxiv.org/abs/1509.02971), T. P. Lillicrap et al., *arXiv*, 2015.
   * [Language Understanding for Text-based Games Using Deep Reinforcement Learning](http://people.csail.mit.edu/karthikn/pdfs/mud-play15.pdf), K. Narasimhan et al., *EMNLP*, 2015.
   * [Action-Conditional Video Prediction using Deep Networks in Atari Games](http://arxiv.org/abs/1507.08750), J. Oh et al., *NIPS*, 2015.
@@ -52,7 +52,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Trust Region Policy Optimization](http://jmlr.org/proceedings/papers/v37/schulman15.pdf), J. Schulman et al., *ICML*, 2015.
 
 ## Monte-Carlo Tree Search
-  * [Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
+  * [Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
 
 ## Improving Exploration
   * [Action-Conditional Video Prediction using Deep Networks in Atari Games](http://arxiv.org/abs/1507.08750), J. Oh et al., *NIPS*, 2015.
@@ -60,7 +60,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
 
 ## Discrete Control
   * [Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning](http://arxiv.org/abs/1509.08731), S. Mohamed and D. J. Rezende, *arXiv*, 2015.
-  * [Deep reinforcement learning with double q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning with Double Q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
   * [Language Understanding for Text-based Games Using Deep Reinforcement Learning](http://people.csail.mit.edu/karthikn/pdfs/mud-play15.pdf), K. Narasimhan et al., *EMNLP*, 2015.
   * [Giraffe: Using Deep Reinforcement Learning to Play Chess](http://arxiv.org/abs/1509.01549), M. Lai, *arXiv*, 2015.
   * [Action-Conditional Video Prediction using Deep Networks in Atari Games](http://arxiv.org/abs/1507.08750), J. Oh et al., *NIPS*, 2015.
@@ -69,7 +69,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Universal Value Function Approximators](http://schaul.site44.com/publications/uvfa.pdf), T. Schaul et al., *ICML*, 2015.
   * [Massively Parallel Methods for Deep Reinforcement Learning](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Publications_files/gorila.pdf), A. Nair et al., *ICML Workshop*, 2015.
   * [Human-level control through deep reinforcement learning](http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf), V. Mnih et al., *Nature*, 2015.
-  * [Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
+  * [Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
   * [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), V. Mnih et al., *NIPS Workshop*, 2013.
  
 ## Continuous Control
@@ -85,7 +85,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
 
 ## Visual Domain
   * [Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning](http://arxiv.org/abs/1509.08731), S. Mohamed and D. J. Rezende, *arXiv*, 2015.
-  * [Deep reinforcement learning with double q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning with Double Q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
   * [Continuous control with deep reinforcement learning](http://arxiv.org/abs/1509.02971), T. P. Lillicrap et al., *arXiv*, 2015.
   * [Giraffe: Using Deep Reinforcement Learning to Play Chess](http://arxiv.org/abs/1509.01549), M. Lai, *arXiv*, 2015.
   * [Action-Conditional Video Prediction using Deep Networks in Atari Games](http://arxiv.org/abs/1507.08750), J. Oh et al., *NIPS*, 2015.
@@ -96,7 +96,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Massively Parallel Methods for Deep Reinforcement Learning](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Publications_files/gorila.pdf), A. Nair et al., *ICML Workshop*, 2015.
   * [Trust Region Policy Optimization](http://jmlr.org/proceedings/papers/v37/schulman15.pdf), J. Schulman et al., *ICML*, 2015.
   * [Human-level control through deep reinforcement learning](http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf), V. Mnih et al., *Nature*, 2015.
-  * [Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
+  * [Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
   * [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), V. Mnih et al., *NIPS Workshop*, 2013.
 
 ## Robotics
@@ -107,7 +107,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
 
 ## Games
   * [Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning](http://arxiv.org/abs/1509.08731), S. Mohamed and D. J. Rezende, *arXiv*, 2015.
-  * [Deep reinforcement learning with double q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning with Double Q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
   * [Continuous control with deep reinforcement learning](http://arxiv.org/abs/1509.02971), T. P. Lillicrap et al., *arXiv*, 2015.
   * [Language Understanding for Text-based Games Using Deep Reinforcement Learning](http://people.csail.mit.edu/karthikn/pdfs/mud-play15.pdf), K. Narasimhan et al., *EMNLP*, 2015.
   * [Giraffe: Using Deep Reinforcement Learning to Play Chess](http://arxiv.org/abs/1509.01549), M. Lai, *arXiv*, 2015.
@@ -118,5 +118,5 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Massively Parallel Methods for Deep Reinforcement Learning](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Publications_files/gorila.pdf), A. Nair et al., *ICML Workshop*, 2015.
   * [Trust Region Policy Optimization](http://jmlr.org/proceedings/papers/v37/schulman15.pdf), J. Schulman et al., *ICML*, 2015.
   * [Human-level control through deep reinforcement learning](http://www.nature.com/nature/journal/v518/n7540/pdf/nature14236.pdf), V. Mnih et al., *Nature*, 2015.
-  * [Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
+  * [Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning](http://papers.nips.cc/paper/5421-deep-learning-for-real-time-atari-game-play-using-offline-monte-carlo-tree-search-planning.pdf), X. Guo et al., *NIPS*, 2014.
   * [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), V. Mnih et al., *NIPS Workshop*, 2013.