Update README.md

README.md

@@ -28,13 +28,24 @@ Please use this bibtex if you want to cite this repository in your publications:
 }
 ```
 
-List of publications & submissions using MiniGrid (please open a pull request to add missing entries):
+List of publications & submissions using MiniGrid or BabyAI (please open a pull request to add missing entries):
+- [Learning to Request Guidance in Emergent Communication](https://arxiv.org/pdf/1912.05525.pdf) (University of Amsterdam, Dec 2019)
 - [Working Memory Graphs](https://arxiv.org/abs/1911.07141) (MSR, Nov 2019)
+- [Fast Task-Adaptation for Tasks Labeled Using
+Natural Language in Reinforcement Learning](https://arxiv.org/pdf/1910.04040.pdf) (Oct 2019, University of Antwerp)
 - [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
 ](https://arxiv.org/abs/1910.12911) (MSR, NeurIPS, Oct 2019)
+- [Recurrent Independent Mechanisms](https://arxiv.org/pdf/1909.10893.pdf) (Mila, Sept 2019) 
 - [Learning Effective Subgoals with Multi-Task Hierarchical Reinforcement Learning](http://surl.tirl.info/proceedings/SURL-2019_paper_10.pdf) (Tsinghua University, August 2019)
+- [Mastering emergent language: learning to guide in simulated navigation](https://arxiv.org/abs/1908.05135) (University of Amsterdam, Aug 2019)
+- [Transfer Learning by Modeling a Distribution over Policies](https://arxiv.org/abs/1906.03574) (Mila, June 2019)
+- [Reinforcement Learning with Competitive Ensembles
+of Information-Constrained Primitives](https://arxiv.org/abs/1906.10667) (Mila, June 2019)
 - [Learning distant cause and effect using only local and immediate credit assignment](https://arxiv.org/abs/1905.11589) (Incubator 491, May 2019)
+- [Practical Open-Loop Optimistic Planning](https://arxiv.org/abs/1904.04700) (INRIA, April 2019)
 - [Learning World Graphs to Accelerate Hierarchical Reinforcement Learning](https://arxiv.org/abs/1907.00664) (Salesforce Research, 2019)
+- [Variational State Encoding as Intrinsic Motivation in Reinforcement Learning](https://mila.quebec/wp-content/uploads/2019/05/WebPage.pdf) (Mila, TARL 2019)
+- [Unsupervised Discovery of Decision States Through Intrinsic Control](https://tarl2019.github.io/assets/papers/modhe2019unsupervised.pdf) (Georgia Tech, TARL 2019)
 - [Modeling the Long Term Future in Model-Based Reinforcement Learning](https://openreview.net/forum?id=SkgQBn0cF7) (Mila, ICLR 2019)
 - [Practical Open-Loop Optimistic Planning](https://arxiv.org/pdf/1904.04700.pdf) (INRIA, Apr 2019)
 - [Unifying Ensemble Methods for Q-learning via Social Choice Theory](https://arxiv.org/pdf/1902.10646.pdf) (Max Planck Institute, Feb 2019)
@@ -42,7 +53,7 @@ List of publications & submissions using MiniGrid (please open a pull request to
 - [Guiding Policies with Language via Meta-Learning](https://arxiv.org/abs/1811.07882) (UC Berkeley, Nov 2018)
 - [On the Complexity of Exploration in Goal-Driven Navigation](https://arxiv.org/abs/1811.06889) (CMU, NeurIPS, Nov 2018)
 - [Transfer and Exploration via the Information Bottleneck](https://openreview.net/forum?id=rJg8yhAqKm) (Mila, Nov 2018)
-- [Modeling the Long Term Future in Model-Based Reinforcement Learning](https://openreview.net/forum?id=SkgQBn0cF7) (Nov 2018)
+- [Creating safer reward functions for reinforcement learning agents in the gridworld](https://gupea.ub.gu.se/bitstream/2077/62445/1/gupea_2077_62445_1.pdf) (University of Gothenburg, 2018)
 - [BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop](https://arxiv.org/abs/1810.08272) (Mila, ICLR, Oct 2018)
 
 This environment has been built as part of work done at the [MILA](https://mila.quebec/en/). The Dynamic obstacles environment has been added as part of work done at [IAS in TU Darmstadt](https://www.ias.informatik.tu-darmstadt.de/) and the University of Genoa for mobile robot navigation with dynamic obstacles.