Update README.md

vcashk · Feb 9, 2016 · b63461e · b63461e
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Showing 1 changed file with 5 additions and 5 deletions.
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@@ -34,7 +34,7 @@ Any suggestions and pull requests are welcome.
   * [Policy Distillation](http://arxiv.org/abs/1511.06295), A. A. Rusu et at., *ICLR*, 2016.
   * [Prioritized Experience Replay](arxiv.org/abs/1511.05952), T. Schaul et al., *ICLR*, 2016.
   * [Deep Reinforcement Learning with an Action Space Defined by Natural Language](http://arxiv.org/abs/1511.04636), J. He et al., *arXiv*, 2015.
-  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *ICLR*, 2016.
   * [Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control](http://arxiv.org/abs/1511.03791), F. Zhang et al., *arXiv*, 2015.
   * [Generating Text with Deep Reinforcement Learning](http://arxiv.org/abs/1510.09202), H. Guo, *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. 
@@ -72,7 +72,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Dueling Network Architectures for Deep Reinforcement Learning](arxiv.org/abs/1511.06581), Z. Wang et al., *arXiv*, 2015.
   * [Prioritized Experience Replay](arxiv.org/abs/1511.05952), T. Schaul et al., *ICLR*, 2016.
   * [Deep Reinforcement Learning with an Action Space Defined by Natural Language](http://arxiv.org/abs/1511.04636), J. He et al., *arXiv*, 2015.
-  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *ICLR*, 2016.
   * [Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control](http://arxiv.org/abs/1511.03791), F. Zhang et al., *arXiv*, 2015.
   * [Generating Text with Deep Reinforcement Learning](http://arxiv.org/abs/1510.09202), H. Guo, *arXiv*, 2015.
   * [Deep Reinforcement Learning with Double Q-learning](http://arxiv.org/abs/1509.06461), H. van Hasselt et al., *arXiv*, 2015.
@@ -111,7 +111,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Policy Distillation](http://arxiv.org/abs/1511.06295), A. A. Rusu et at., *ICLR*, 2016.
   * [Prioritized Experience Replay](arxiv.org/abs/1511.05952), T. Schaul et al., *ICLR*, 2016.
   * [Deep Reinforcement Learning with an Action Space Defined by Natural Language](http://arxiv.org/abs/1511.04636), J. He et al., *arXiv*, 2015.
-  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *ICLR*, 2016.
   * [Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control](http://arxiv.org/abs/1511.03791), F. Zhang et al., *arXiv*, 2015.
   * [Generating Text with Deep Reinforcement Learning](http://arxiv.org/abs/1510.09202), H. Guo, *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.
@@ -162,7 +162,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Better Computer Go Player with Neural Network and Long-term Prediction](arxiv.org/abs/1511.06410), Y. Tian et al., *ICLR*, 2016.
   * [Policy Distillation](http://arxiv.org/abs/1511.06295), A. A. Rusu et at., *ICLR*, 2016.
   * [Prioritized Experience Replay](arxiv.org/abs/1511.05952), T. Schaul et al., *ICLR*, 2016.
-  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *ICLR*, 2016.
   * [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.
@@ -204,7 +204,7 @@ Model Predictive Control](http://deepmpc.cs.cornell.edu/DeepMPC.pdf), I. Lenz, e
   * [Policy Distillation](http://arxiv.org/abs/1511.06295), A. A. Rusu et at., *ICLR*, 2016.
   * [Prioritized Experience Replay](arxiv.org/abs/1511.05952), T. Schaul et al., *ICLR*, 2016.
   * [Deep Reinforcement Learning with an Action Space Defined by Natural Language](http://arxiv.org/abs/1511.04636), J. He et al., *arXiv*, 2015.
-  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *arXiv*, 2015.
+  * [Deep Reinforcement Learning in Parameterized Action Space](http://arxiv.org/abs/1511.04143), M. Hausknecht et al., *ICLR*, 2016.
   * [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., *ICLR*, 2016.