Add two more papers

Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
Recurrent Reinforcement Learning: A Hybrid Approach

README.md

@@ -19,9 +19,11 @@ Any suggestions and pull requests are welcome.
   * [Games](#games)
 
 ## All Papers
+  * [Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control](http://arxiv.org/abs/1511.03791), F. Zhang et al., *arXiv*, 2015.
   * [ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources](http://arxiv.org/abs/1510.02879), J. Rajendran et al., *arXiv*, 2015. 
   * [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.
+  * [Recurrent Reinforcement Learning: A Hybrid Approach](http://arxiv.org/abs/1509.03044), X. Li 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.
@@ -41,7 +43,9 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), V. Mnih et al., *NIPS Workshop*, 2013.
 
 ## Q-learning
+  * [Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control](http://arxiv.org/abs/1511.03791), F. Zhang et al., *arXiv*, 2015.
   * [Deep Reinforcement Learning with Double Q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
+  * [Recurrent Reinforcement Learning: A Hybrid Approach](http://arxiv.org/abs/1509.03044), X. Li 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.
@@ -67,9 +71,11 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models](http://arxiv.org/abs/1507.00814), B. C. Stadie et al., *arXiv*, 2015.
 
 ## Discrete Control
+  * [Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control](http://arxiv.org/abs/1511.03791), F. Zhang et al., *arXiv*, 2015.
   * [ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources](http://arxiv.org/abs/1510.02879), J. Rajendran et al., *arXiv*, 2015.
   * [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.
+  * [Recurrent Reinforcement Learning: A Hybrid Approach](http://arxiv.org/abs/1509.03044), X. Li 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.
@@ -96,6 +102,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences](http://arxiv.org/abs/1506.04089), H. Mei et al., *arXiv*, 2015.
 
 ## Visual Domain
+  * [Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control](http://arxiv.org/abs/1511.03791), F. Zhang et al., *arXiv*, 2015.
   * [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.
   * [Continuous control with deep reinforcement learning](http://arxiv.org/abs/1509.02971), T. P. Lillicrap et al., *arXiv*, 2015.
@@ -112,6 +119,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf), V. Mnih et al., *NIPS Workshop*, 2013.
 
 ## Robotics
+  * [Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control](http://arxiv.org/abs/1511.03791), F. Zhang et al., *arXiv*, 2015.
   * [Learning Deep Neural Network Policies with Continuous Memory States](http://arxiv.org/abs/1507.01273), M. Zhang et al., *arXiv*, 2015.
   * [End-to-End Training of Deep Visuomotor Policies](http://arxiv.org/abs/1504.00702), S. Levine et al., *arXiv*, 2015.
   * [DeepMPC: Learning Deep Latent Features for