Update README.md

README.md

@@ -14,6 +14,7 @@ Relevant Papers:
 9. Distributional Reinforcement Learning with Quantile Regression [[Publication]](https://arxiv.org/abs/1710.10044)[[code]](https://github.com/qfettes/DeepRL-Tutorials/blob/master/09.QuantileRegression-DQN.ipynb)
 10. Rainbow with Quantile Regression [[code]](https://github.com/qfettes/DeepRL-Tutorials/blob/master/10.Quantile-Rainbow.ipynb)
 11. Deep Recurrent Q-Learning for Partially Observable MDPs [[Publication]](https://arxiv.org/abs/1507.06527)[[code]](https://github.com/qfettes/DeepRL-Tutorials/blob/master/11.DRQN.ipynb)
+12. Advantage Actor Critic (A2C) [[Publication1]](https://arxiv.org/abs/1602.01783)[[Publication2]](https://blog.openai.com/baselines-acktr-a2c/)[[code]](https://github.com/qfettes/DeepRL-Tutorials/blob/master/12.A2C.ipynb)
 
 
 Requirements: 
@@ -24,8 +25,10 @@ Requirements:
 * Pytorch 0.4.0 
 * Matplotlib 
 * OpenCV 
+* Baslines
 
 Acknowledgements: 
 * Credit to [@baselines](https://github.com/openai/baselines) for the environment wrappers and inspiration for the prioritized replay code used only in the development code
 * Credit to [@higgsfield](https://github.com/higgsfield) for the plotting code, epsilon annealing code, and inspiration for the prioritized replay implementation in the IPython notebook
 * Credit to [@Kaixhin](https://github.com/Kaixhin) for factorized Noisy Linear Layer implementation and the projection_distribution function found in Categorical-DQN.ipynb
+* Credit to [@ikostrikov](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr) for A2C, GAE, and visdom plotting code implementation reference