Add two papers

README.md

@@ -21,6 +21,8 @@ Any suggestions and pull requests are welcome.
   * [Multi-Agent](#multi-agent)
 
 ## All Papers
+  * [Deep Exploration via Bootstrapped DQN](http://arxiv.org/abs/1602.04621), I. Osband et al., *arXiv*, 2016.
+  * [Value Iteration Networks](http://arxiv.org/abs/1602.02867), A. Tamar et al., *arXiv*, 2016.
   * [Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks](http://arxiv.org/abs/1602.02672), J. N. Foerster et al., *arXiv*, 2016.
   * [Asynchronous Methods for Deep Reinforcement Learning](http://arxiv.org/abs/1602.01783), V. Mnih et al., *arXiv*, 2016.
   * [Mastering the game of Go with deep neural networks and tree search](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html), D. Silver et al., *Nature*, 2016.
@@ -66,6 +68,8 @@ 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.
 
 ## Value Function Approximation
+  * [Deep Exploration via Bootstrapped DQN](http://arxiv.org/abs/1602.04621), I. Osband et al., *arXiv*, 2016.
+  * [Value Iteration Networks](http://arxiv.org/abs/1602.02867), A. Tamar et al., *arXiv*, 2016.
   * [Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks](http://arxiv.org/abs/1602.02672), J. N. Foerster et al., *arXiv*, 2016.
   * [Asynchronous Methods for Deep Reinforcement Learning](http://arxiv.org/abs/1602.01783), V. Mnih et al., *arXiv*, 2016.
   * [Mastering the game of Go with deep neural networks and tree search](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html), D. Silver et al., *Nature*, 2016.
@@ -104,6 +108,8 @@ 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.
 
 ## Discrete Control
+  * [Deep Exploration via Bootstrapped DQN](http://arxiv.org/abs/1602.04621), I. Osband et al., *arXiv*, 2016.
+  * [Value Iteration Networks](http://arxiv.org/abs/1602.02867), A. Tamar et al., *arXiv*, 2016.
   * [Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks](http://arxiv.org/abs/1602.02672), J. N. Foerster et al., *arXiv*, 2016.
   * [Asynchronous Methods for Deep Reinforcement Learning](http://arxiv.org/abs/1602.01783), V. Mnih et al., *arXiv*, 2016.
   * [Mastering the game of Go with deep neural networks and tree search](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html), D. Silver et al., *Nature*, 2016.
@@ -159,6 +165,8 @@ 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
+  * [Deep Exploration via Bootstrapped DQN](http://arxiv.org/abs/1602.04621), I. Osband et al., *arXiv*, 2016.
+  * [Value Iteration Networks](http://arxiv.org/abs/1602.02867), A. Tamar et al., *arXiv*, 2016.
   * [Asynchronous Methods for Deep Reinforcement Learning](http://arxiv.org/abs/1602.01783), V. Mnih et al., *arXiv*, 2016.
   * [Mastering the game of Go with deep neural networks and tree search](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html), D. Silver et al., *Nature*, 2016.
   * [Memory-based control with recurrent neural networks](http://arxiv.org/abs/1512.04455), N. Heess et al., *NIPS Workshop*, 2015.
@@ -201,6 +209,7 @@ 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.
 
 ## Games
+  * [Deep Exploration via Bootstrapped DQN](http://arxiv.org/abs/1602.04621), I. Osband et al., *arXiv*, 2016.
   * [Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks](http://arxiv.org/abs/1602.02672), J. N. Foerster et al., *arXiv*, 2016.
   * [Asynchronous Methods for Deep Reinforcement Learning](http://arxiv.org/abs/1602.01783), V. Mnih et al., *arXiv*, 2016.
   * [Mastering the game of Go with deep neural networks and tree search](http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html), D. Silver et al., *Nature*, 2016.
@@ -244,9 +253,10 @@ 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.
 
 ## Improving Exploration
+  * [Deep Exploration via Bootstrapped DQN](http://arxiv.org/abs/1602.04621), I. Osband et al., *arXiv*, 2016.
   * [Action-Conditional Video Prediction using Deep Networks in Atari Games](http://arxiv.org/abs/1507.08750), J. Oh et al., *NIPS*, 2015.
   * [Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models](http://arxiv.org/abs/1507.00814), B. C. Stadie et al., *arXiv*, 2015.
 
-## Multi Agent
+## Multi-Agent
   * [Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks](http://arxiv.org/abs/1602.02672), J. N. Foerster et al., *arXiv*, 2016.
   * [Multiagent Cooperation and Competition with Deep Reinforcement Learning](http://arxiv.org/abs/1511.08779), A. Tampuu et al., *arXiv*, 2015.